Deep Dive Library: Ukraine, Modern War, and Allied Defense Innovation
A curated library of authoritative sources on Ukraine’s history and sovereignty, the human cost of the war, modern war technology, responsible AI, and the trusted pathways that turn frontline innovation into allied capability. Each card explains why a source matters, what it teaches, and the Helicon takeaway.
External materials are provided for educational context. Inclusion does not imply endorsement of any organization, outlet, or viewpoint, nor any affiliation with Helicon. We summarize and link to original sources; we do not republish or iframe their content. Independently verify any figure before operational use.
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Ukraine History & Sovereignty
Where the resolve comes from: the long history of Ukrainian statehood, identity, and the cost of its denial.
Human StoryOfficial Source
Holodomor Museum (Kyiv)
National Museum of the Holodomor-Genocide
Why this matters
It documents the 1932-33 famine-genocide that Soviet policy inflicted on Ukraine — essential context for why Ukrainians treat sovereignty as a question of survival.
What it teaches
That the deliberate starvation of millions of Ukrainians is recognized by Ukraine and many states as a genocide, and that erasure of Ukrainian identity has a long, documented history.
Helicon takeaway
Understanding this history explains the resolve behind Ukraine’s defense innovation — and why credible, sustainable allied capability matters.
The National Museum of the Holodomor-Genocide in Kyiv documents the man-made famine of 1932-33 in which millions of Ukrainians died as a direct result of Soviet policy under Stalin. The mechanics of the Holodomor were deliberate: Soviet authorities confiscated grain from Ukrainian villages, imposed impossibly high procurement quotas, blockaded communities to prevent the movement of food, and placed families on blacklists that denied them access to any provisions at all. The famine was not a consequence of drought or natural crop failure — it was engineered through administrative action applied with particular severity against the Ukrainian Soviet Socialist Republic and against ethnic Ukrainians in the Kuban and other regions.
The museum assembles the full documentary record: archival evidence of Soviet orders and quota decisions, victim testimony gathered from survivors and their descendants, demographic research on mortality, and the scholarly apparatus behind the case for genocide recognition. Mortality estimates from serious historical scholarship range widely due to the deliberate destruction and falsification of Soviet-era records, but figures in the range of several million Ukrainian deaths are broadly accepted by researchers working from demographic reconstruction and archival sources. Ukraine recognizes the Holodomor as genocide, as do a growing number of states and parliaments — the museum tracks this recognition internationally and participates in bodies such as the Platform of European Memory and Conscience. Recent museum programming has included scholarly presentations, lectures, and a new publication titled Darkness Over Kyiv documenting the city's experience during the famine year of 1933.
For a reader trying to understand the present war, the museum supplies essential historical context. Attempts to suppress Ukrainian identity, language, culture, and statehood are not new; they have a long and documented history that the museum preserves precisely because memory is itself a form of resistance. The Holodomor sits within a longer pattern — the deliberate weakening of Ukrainian national consciousness through famine, forced collectivization, the suppression of the Ukrainian language, and the physical elimination of Ukrainian intellectual and cultural leaders in the same period. That history is a large part of why Ukrainians treat national sovereignty not as an abstract preference but as a matter of collective survival. The resolve behind Ukraine's defense, including its defense innovation, runs deep in part because of what this history documents about the cost of vulnerability.
Understanding the Holodomor helps outside partners grasp the stakes Ukrainians themselves attach to this fight — and why the question of who controls Ukraine's future is experienced not as a geopolitical abstraction but as an existential one. This is a Helicon-written summary; the museum's full documentation and educational resources are available at the National Museum of the Holodomor-Genocide in Kyiv.
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It shows the country and culture behind the war — the people, regions, and everyday life that sovereignty is meant to protect.
What it teaches
That Ukraine is a diverse, modern European nation with deep regional cultures, not a borderland or an abstraction in a conflict map.
Helicon takeaway
The human and cultural reality is the reason the work matters. Capability that protects civilians and infrastructure protects this.
Ukrainer International is a multilingual, non-profit media organization built around the work of more than 800 volunteers, dedicated to documenting Ukraine — its regions, communities, languages, crafts, landscape, and everyday culture — through long-form reporting, photography, and film. The project was founded to show the country beyond the headlines: not the abstraction that a conflict map produces, but the diverse, textured reality of a modern European nation with deep and distinct regional identities, from the Carpathian highlands to the Black Sea littoral to the industrial cities of the east. Its expeditions and multimedia storytelling build a portrait that a reader cannot get from wire dispatches or casualty counts.
That portrait matters for outside readers who are trying to make sense of the war. Sovereignty, in the Ukrainian understanding, is not reducible to a line on a map; it is the continued existence of specific communities, languages, craft traditions, and ways of life that have their own histories and their own stakes in the outcome. When Helicon describes capabilities that protect civilians and critical infrastructure, this is the human and cultural reality those capabilities exist to protect. A drone defense system or a resilient power grid is not abstract technology — it is the difference between a village in Poltava or a workshop in Lviv surviving the winter or not.
Ukrainer is also, practically, a sober and accessible starting point for any partner or analyst who wants to understand the country before engaging with the conflict. Its framing is non-polemical and its subject matter is the people and places of Ukraine rather than the politics surrounding them. A partner who has read even a few Ukrainer profiles comes away with a more grounded understanding of what is at stake in this war — and a more credible instinct about why Ukrainian partners in technology, manufacturing, and defense approach their work with the intensity and ownership that they do. The project's multilingual reach also speaks to the international constituency it serves: it is a resource for building understanding of Ukraine across the allied world, not only within it.
Helicon links to Ukrainer as part of the context library because the human and cultural picture is the foundation on which everything else rests. The technologies and transition pathways Helicon works on are ultimately measured against a simple question: do they help protect people like the ones whose lives Ukrainer documents. This is a Helicon-written summary; explore Ukrainer's full archive at ukrainer.net.
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Ukraine, Nuclear Weapons, and Security Assurances at a Glance
Why this matters
It is the authoritative factsheet on the Budapest Memorandum — the agreement under which Ukraine surrendered the world’s third-largest nuclear arsenal.
What it teaches
That Ukraine returned all warheads by 1996 in exchange for 1994 security assurances — not binding guarantees — with no enforcement mechanism.
Helicon takeaway
Credible deterrence rests on demonstrable, sustainable capability, not paper promises. That is why trusted production matters.
The Arms Control Association factsheet provides the precise legal and historical record of Ukraine's nuclear status — a record that has become one of the most-cited cautionary reference points in contemporary deterrence debates. When the Soviet Union dissolved, newly independent Ukraine found itself holding the world's third-largest nuclear arsenal: an estimated 1,900 strategic warheads, 176 intercontinental ballistic missiles, and 44 strategic bombers physically on its territory. Ukraine never held operational launch control over those weapons — that authority remained with Moscow — but it possessed the physical arsenal and the leverage that came with it.
The path to denuclearization was neither immediate nor uncomplicated. Ukraine initially sought binding security guarantees and compensation as preconditions for surrendering the weapons. A January 1994 Trilateral Statement signed by Ukraine, Russia, and the United States committed Ukraine to transferring its nuclear warheads to Russia in exchange for compensation for the commercial value of the highly enriched uranium and U.S. assistance in dismantling delivery vehicles. On 5 December 1994, the Budapest Memorandum on Security Assurances was signed by the United States, Russia, and the United Kingdom. Under it, Ukraine acceded to the Nuclear Non-Proliferation Treaty as a non-nuclear-weapon state. The last warhead was transferred to Russia by June 1996, and the last strategic nuclear delivery vehicle was eliminated in October 2001 under the Strategic Arms Reduction Treaty framework.
The Arms Control Association's factsheet is precise about the legal weight of what was exchanged. The 1994 instrument provided security assurances — a political commitment in accordance with Helsinki Accords principles — not legally binding security guarantees with an enforcement mechanism. The signatories committed to respect Ukraine's sovereignty and existing borders, and to refrain from the threat or use of force against its territorial integrity or political independence. Russia, the United States, and the United Kingdom each made those commitments. Russia's annexation of Crimea in March 2014 was immediately characterized by the United States, the United Kingdom, and Ukraine as a blatant violation of the Budapest Memorandum's assurances. Russia's position was that the assurances were given to the legitimate government, not to those who came to power following what Moscow characterized as a coup. The full-scale invasion of February 2022 constituted a further and more comprehensive violation.
The episode now anchors contemporary debates about disarmament incentives and deterrence credibility. A state surrendered an enormous deterrent in exchange for political assurances that were not honored by the most powerful signatory. The factsheet is updated as the legal and diplomatic record develops, and the ACA provides the authoritative reference for the specific language and timeline. Helicon's reading is that credible deterrence rests on demonstrable, sustainable capability rather than promises alone — a lesson that shapes how allied commitments to Ukraine must now be structured. This is a Helicon-written summary; read the original factsheet at the Arms Control Association for the full text and updated timeline.
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The civilian reality of the war, recorded soberly. We link to these archives and reports; we do not republish trauma.
Human StoryOfficial SourceReport
UNHCR — The UN Refugee Agency
Ukraine Emergency
Why this matters
It quantifies the displacement and humanitarian need created by Russia’s invasion — the civilian scale of the war.
What it teaches
That millions of Ukrainians have been forced from their homes and millions more inside the country need humanitarian assistance.
Helicon takeaway
Resilient energy, air defense, demining, and protected logistics are not abstractions — they connect directly to civilian survival.
UNHCR, the UN Refugee Agency, maintains its Ukraine Emergency page as the authoritative running account of the displacement and humanitarian need created by Russia's full-scale invasion. UNHCR has been present in Ukraine since 1994; the agency's current operation is described as one of the largest humanitarian responses it has ever mounted, spanning Ukraine itself and the twelve surrounding and neighboring countries to which Ukrainians have fled.
The numbers are sobering. As of January 2025, some 6.8 million refugees from Ukraine were recorded globally — the largest displacement of people in Europe since the Second World War. More than 3.6 million Ukrainians are internally displaced within the country. Over 12.7 million people inside Ukraine require urgent humanitarian assistance — people who have not fled but whose access to shelter, heat, water, and basic services has been severely disrupted by the war. The destruction of energy infrastructure has been a compounding factor: Ukraine has lost a significant portion of its overall energy generation capacity due to escalating attacks on power systems, and damage to energy facilities substantially increases the need for winter assistance when temperatures fall. Between May and October 2024 alone, intensified hostilities resulted in approximately 160,000 additional people being displaced from frontline areas.
The assistance UNHCR and its more than 300 partner organizations are providing in Ukraine includes cash assistance, in-kind support, emergency shelter repair kits, housing repairs, legal aid, and psychological counseling for those suffering the trauma of war. In 2024, UNHCR and partners delivered services to over 1.6 million people in need inside Ukraine. The Regional Refugee Response Plan coordinates the international response across neighboring countries, where millions of Ukrainians are sheltering and where host-country capacity is under sustained pressure. Because UNHCR is an operational agency rather than a political actor, its figures and assessments are grounded in field operations and updated as conditions evolve; the page is best consulted for current numbers rather than as a fixed snapshot.
For Helicon, the scale of humanitarian need documented here translates directly into capability priorities. Resilient energy infrastructure means that hospitals, heating systems, and water treatment plants continue functioning when grid attacks occur. Air and missile defense means that cities far from the front receive warning and protection before strikes arrive. Counter-drone systems reduce the casualty toll from the short-range drones that HRMMU identifies as a growing cause of civilian harm. Demining protects the agricultural land and rural communities where returns are otherwise impossible. These are not abstractions; they are the engineering and procurement response to the displacement and suffering that UNHCR documents. This is a Helicon-written summary; current figures and the full scope of the response are available at the UNHCR Ukraine Emergency page.
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It preserves first-person testimony from Ukrainian civilians living through the war — the human record, in their own words.
What it teaches
That the war is experienced one family at a time. The archive collects civilian stories to ensure they are documented and remembered.
Helicon takeaway
We link to this archive; we do not republish trauma. The civilian experience is the moral center of why responsible capability matters.
The Museum of Civilian Voices, an initiative of the Rinat Akhmetov Foundation, describes itself as the world's largest collection of stories from civilians who have experienced Russia's war against Ukraine. It is an oral-history and primary documentation project: an archive built around first-person testimony gathered from ordinary people — not from officials, military analysts, or journalists — so that the civilian experience of this war is recorded, preserved, and accessible rather than absorbed into statistics or lost to the passage of time.
The premise behind the archive is that war is experienced one family, one household, one person at a time. The evidence of what it means to shelter during shelling, to leave an occupied town with little notice, to wait for news of someone taken, or to return to a home that no longer exists is a form of historical record that statistics cannot capture and policy language cannot substitute for. The museum's collection is built around personal accounts rather than graphic imagery, making it a sober and humane record rather than a sensational one — testimony preserved with the dignity of the people whose lives it documents. The project describes its archive as the primary source of truth about civilian experience in this war, and the scale of the collection — continuously updated as the conflict continues — reflects a sustained institutional commitment to that documentation mission.
Helicon links to the Museum of Civilian Voices for context, and does not republish testimony from it. The archive belongs to the people whose lives it records, and Helicon has no claim on their words. The reason the museum belongs in this source library is the same reason the humanitarian figures from UNHCR and HRMMU belong: the civilian experience is the moral center of why responsible capability development matters. Every system Helicon helps move into trusted allied evaluation and acquisition pathways is ultimately measured against the question of whether it helps protect people like the ones whose accounts are preserved here.
Reading even a few of these testimonies is a useful corrective to the tendency — common in defense and technology work — to think about war only in terms of systems, doctrine, and procurement. The people behind the numbers are real, their losses are specific, and the record of what they have endured is part of what gives this work its weight. Helicon's role in moving wartime-developed technologies into U.S., European, and NATO acquisition pathways is ultimately in service of that civilian protection mission. This is a Helicon-written summary; the full archive is available at the Museum of Civilian Voices.
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ICC Issues Arrest Warrant for Putin Over Alleged War Crimes in Ukraine
Why this matters
It marks the first ICC arrest warrant ever issued against the sitting leader of a UN Security Council permanent member.
What it teaches
That the warrant holds Vladimir Putin personally responsible for the unlawful deportation and transfer of Ukrainian children from occupied territory.
Helicon takeaway
The human cost is the moral center of the war. Capabilities that protect civilians — air defense, counter-drone, demining — are part of why this work matters.
On 17 March 2023, the International Criminal Court issued an arrest warrant for Russian President Vladimir Putin, finding reasonable grounds to believe he bears individual criminal responsibility for the war crime of the unlawful deportation and unlawful transfer of children from occupied areas of Ukraine to the Russian Federation. As Associated Press reported, the Court found that Putin was responsible both for committing the acts directly and for failing to exercise proper control over civilian and military subordinates who carried them out. It was the first time in the Court's history that it had issued an arrest warrant against the sitting head of state of a permanent member of the UN Security Council — a legal threshold of significant weight, even if enforcement remains remote.
A second warrant was issued the same day for Maria Lvova-Belova, Russia's Commissioner for Children's Rights in the Office of the President, on the same allegations. ICC Prosecutor Karim Khan had visited Ukraine four times since opening the investigation a year earlier. Ukrainian officials reported, based on data from Ukraine's National Information Bureau, that 16,226 children had been deported, with 308 returned at the time of reporting. AP's earlier investigation had traced the child removal process into Russian territory through dozens of interviews and documents. The charges center on forcible removal of Ukrainian children — conduct that, when carried out with intent to destroy a group in whole or in part, can also bear on the legal definition of genocide.
Russia immediately rejected the warrants. Kremlin spokesman Dmitry Peskov called the Court's decisions legally void and its move outrageous, consistent with Russia's long-standing position that it does not recognize the ICC's jurisdiction and will not extradite its nationals. The ICC has no police force of its own; enforcement depends on member states. With 123 member states, the Court's warrants carry practical consequence for where Putin can travel — legal scholars noted he would likely avoid member states that would be obligated to arrest him — though a trial remains, in the AP's framing, extremely remote.
The legal and moral significance is nonetheless substantial. Human rights organizations described the warrants as making Putin a wanted man and taking a first step toward ending impunity for crimes in Russia's war. The broader UN-backed inquiry cited in the same reporting documented systematic torture and killing in occupied regions, a filtration system aimed at singling out Ukrainians for detention, and inhumane conditions for detainees — potential war crimes and crimes against humanity. Helicon does not adjudicate the merits of these proceedings; it links to the public record so readers can understand the legal context of the conflict. The human cost — measured in lost children, displaced families, and civilian casualties — remains the moral center of this work. We do not republish Associated Press text; this is a Helicon-written summary linking to the original AP reporting.
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Civilian Casualties in Ukraine Up Sharply in 2025, UN Monitor Says
Why this matters
It quantifies, from UN monitoring data, how the war’s burden continues to fall on civilians.
What it teaches
That the UN Human Rights Monitoring Mission recorded 2,514 civilians killed and 12,142 injured in 2025 — a 31% rise over 2024.
Helicon takeaway
Resilient energy, air and missile defense, counter-drone, logistics, demining, and battlefield awareness connect directly to civilian survival.
Citing the UN Human Rights Monitoring Mission in Ukraine (HRMMU), Reuters reported in January 2026 that 2,514 civilians were killed and 12,142 injured in Ukraine during 2025 — a total of 14,656 verified civilian casualties, representing a roughly 31 percent increase compared with 2024 and making 2025 the deadliest year for civilians since 2022. The monitor attributes the rise to multiple converging factors: escalated confrontations along the front lines, the increased deployment of long-range weaponry beginning in June 2025, and a significant rise in short-range drone attacks that rendered many areas near the front virtually uninhabitable.
The geographic pattern reflects the nature of the weapons being used. Nearly two-thirds of all civilian casualties in 2025 occurred in frontline regions, where the density of artillery, short-range drone activity, and ground combat is highest. But the increase in long-range weapons use simultaneously extended the danger deep into urban areas far from the contact line — areas where populations have less warning, less physical protection, and less reason to have evacuated. Long-range strikes on energy infrastructure were a particular driver of civilian harm, compounding displacement and the destruction of essential services. The most vulnerable populations, the report notes, include elderly individuals who have remained in or near frontline communities.
As with all HRMMU tallies, the 2025 figures are verified minimums. Each casualty is individually corroborated before it is counted, which makes the process conservative by design — the true numbers are very likely higher. The value of the HRMMU methodology is precisely this discipline: its figures draw on an independent UN body applying a consistent standard rather than either government's claims, which gives them credibility in a contested information environment where casualty numbers are routinely disputed. The UN monitoring mission's reporting is described by Reuters as distinct from the broader context in which Ukraine itself also conducts strikes affecting civilian infrastructure, but the vast preponderance of civilian casualties documented are attributed to Russian military operations against Ukrainian-government-controlled territory.
For Helicon, the figures translate directly into capability priorities rather than remaining abstract statistics. When 2,514 people are killed and 12,142 injured in a single year — and the trend is worsening, not easing — the relevant question is what capabilities reduce that toll. Air and missile defense, counter-drone systems that detect and neutralize short-range threats near population centers, battlefield early-warning systems, resilient energy infrastructure, protected logistics corridors, and demining all connect to civilian survival. The humanitarian cost is the reason this work matters, and the steady year-on-year escalation documented here is a concrete case for why trusted allied capability development cannot wait. We do not republish Reuters' text; this is a Helicon-written summary linking to the original reporting.
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How the war is actually being fought, and how frontline learning is moving to allied forces.
Royal United Services Institute (RUSI)
Preliminary Lessons in Conventional Warfighting from Russia’s Invasion of Ukraine
Why this matters
Foundational origin study (February–July 2022). It is the cornerstone early field study of the war’s opening months — useful for how the conflict began, but read the 2025 RUSI operational study above for the current battlefield.
What it teaches
That there is no sanctuary on the modern battlefield; unmanned systems and counter-UAS are everywhere; electronic warfare is central; precision is contested; and stockpiles and industrial capacity decide endurance.
Helicon takeaway
Helicon screens for capabilities that hold up against these realities — attritable, sustainable, and resilient under EW — not demonstrations that only work in clean conditions.
This RUSI special report — drawn from operational data accumulated by the Ukrainian General Staff covering February through July 2022 — is one of the most-cited primary field studies of how modern conventional war is actually being fought. The authors are explicit that it should be understood as testimony rather than a finished academic study, because the underlying source material cannot yet be made public for operational security reasons.
The report's central analytical finding is that Russia planned to seize Kyiv in roughly ten days, relying on speed and strategic deception to keep Ukrainian forces away from the capital. The deception largely worked — Russia achieved a reported twelve-to-one force ratio advantage north of Kyiv. But operational security that enabled deception left Russian forces tactically unprepared, and when speed failed, there were no fallback courses of action. Russia subsequently refocused on Donbas; Ukraine, having largely expended its ammunition supply, lost fire-volume parity. By June 2022, Russia held an estimated ten-to-one advantage in volume of fire — a shift driven not by better technology, but by industrial depth. The report notes that Ukraine began the conflict with 1,176 barrel artillery systems against Russia's 2,433, and 1,680 multiple-launch rocket systems against Russia's 3,547, holding rough parity for about six weeks before munitions ran thin.
Five structural findings stand out for NATO readers. First, there is no sanctuary: persistent unmanned aerial surveillance combined with networked precision fires means that detected forces are struck quickly, so dispersal, concealment, and mobility become survival imperatives. The Russians struck roughly 75 percent of Ukraine's static defense sites within the first 48 hours. Second, electronic warfare is central, not supporting — it determines whether drones, communications, and precision weapons function at all. Russian EW systems and capabilities rarely deconflicted, creating fratricide risk; the lesson for the West is that EW for attack, protection, and direction-finding must be deliberately integrated. Third, precision is contested, not guaranteed: kill chains must be sequenced around EW disruption to create windows of opportunity. Fourth, unmanned systems are essential at every echelon and for every service, but 90 percent of UAS employed are lost — they must be cheap and attritable, with counter-UAS primarily addressed through EW. Fifth, and perhaps most consequential for allied planning: no NATO country other than the United States has sufficient initial weapons stocks or the industrial capacity to sustain large-scale operations.
For Helicon, the RUSI study is a calibration document. It establishes the environment against which candidate capabilities are measured: high-tempo, EW-saturated, logistically demanding, and decided as much by industrial endurance as by any single platform. Helicon screens for technologies that hold up in these conditions — attritable, manufacturable at scale, EW-resilient, and sustainably supportable — rather than capabilities that perform well only in clean demonstration environments. This is a Helicon-written summary; the full report is available at the Royal United Services Institute.
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Center for a New American Security (CNAS) — Stacie Pettyjohn
Evolution Not Revolution: Drone Warfare and the Lessons of the Ukraine War
Why this matters
It is the most disciplined corrective to drone hype — separating what has genuinely changed from what has not.
What it teaches
That drones are evolutionary, not revolutionary; commercial, cheap, and attritable matter more than exquisite platforms; effects come from stacks, not swarms; electronic warfare is the key counter; and drones supplement rather than replace artillery and airpower.
Helicon takeaway
Helicon values affordability, manufacturability at scale, and EW resilience over novelty — and treats finding skilled operators as part of the capability.
Stacie Pettyjohn's February 2024 CNAS report offers a disciplined, evidence-based corrective to inflated claims about drone warfare. Its thesis, embedded in the title, is that drones have meaningfully transformed the battlefield in Ukraine but have done so in an evolutionary rather than revolutionary fashion — falling short of the disruptive discontinuity that constitutes a true revolution in military affairs. The report draws on secondary sources and confidential interviews with U.S. government, NATO, and subject-matter experts, situating Ukraine as a case study for thinking about drone dynamics in potential future great-power conflict.
The most important finding about drone roles is that the systems dominating the front lines are cheap, commercial, and attritable — small quadcopters used for ISR and artillery spotting at every echelon, FPV racing drones adapted as inexpensive kamikaze munitions, and long-range one-way attack drones for deep strategic strikes. These are not the autonomous networked swarms of popular imagination. In Ukraine, drones have operated in stacks coordinated by human operators rather than true swarms that autonomously coordinate behavior. Both sides have so far been unable to realize the autonomous swarming concept under real EW conditions. Crucially, mass artillery fires still dominate battlefield outcomes; drones enhance artillery accuracy and extend ground-force reach by roughly six times compared to conventional anti-armor weapons, but they do not substitute for indirect fire mass. FPV drones are very cheap anti-armor weapons with a small payload — tactical beyond-line-of-sight tools, not strategic game-changers on their own.
The cost logic at the heart of the report is important and counterintuitive. Drones are not more survivable than crewed aircraft — they are vulnerable to electronic warfare, guns, and surface-to-air missiles. But cheapness substitutes for survivability: if a system is inexpensive enough, resiliency comes from reconstitution rather than hardening. Both sides have opted to buy more cheap drones rather than harden them against electronic attack. Electronic warfare is in fact the most effective counter, not kinetic interceptors; the primary drone-versus-drone competition has largely been about finding and attacking operators, whose proximity to operating areas makes them vulnerable. The innovation cycle is fast and two-sided: because drone technologies are largely commercial or dual-use, Ukrainian adaptations diffuse to Russia quickly.
Two human-factor findings deserve particular weight. Volunteer networks have played an unprecedented role in acquiring, modifying, building, and professionalizing drone use on both sides — identifying best practices and establishing training pipelines. Skilled FPV operators are a genuine limiting factor; training the human pipeline is as much a part of fielding the capability as procuring the hardware. Helicon's posture follows directly from Pettyjohn's analysis: weight affordability, manufacturability at scale, and EW resilience over novelty; treat integrated sensor-shooter-communications stacks rather than stand-alone platforms as the unit of value; and regard operator training as an integral part of any capability, not an afterthought. This is a Helicon-written summary; the full report is available at the Center for a New American Security.
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Ukraine to Give Polish Forces Drone-Defence Training After Incursion
Why this matters
It shows Ukraine exporting hard-won counter-drone knowledge into NATO — the flow of frontline learning into allied forces.
What it teaches
That after drone incursions into Polish airspace, Ukraine agreed to train Polish forces on drone defence — a direct transfer of operational experience to a NATO ally.
Helicon takeaway
Frontline learning is an allied asset. Helicon’s purpose is to move that learning into trusted U.S. and allied capability responsibly.
Reuters reported on 18 September 2025 that, following the breach of Polish airspace by Russian drones on the night of 9-10 September, Ukraine agreed to provide Polish armed forces with training on drone defense. Ukrainian Defense Minister Denys Shmyhal announced that Ukrainian soldiers and engineers would work with Polish counterparts in a collaborative training initiative to be conducted at a facility in Lipa, in southern Poland. Ukraine also agreed to grant Poland access to systems used to track Russian aerial threats, enabling Poland to monitor potential incursions into its own airspace.
The incursion itself was significant. More than 20 Russian drones entered Polish airspace during the September incident. NATO fighter jets engaged and downed several of them using missiles — at a cost, the reporting notes, significantly higher than the inexpensive, mass-produced drones being intercepted. The cost asymmetry is important: defending against cheap attritable drones with expensive interceptor missiles is not a sustainable equation, and the incident sharpened allied urgency about developing lower-cost counter-drone approaches. Russia stated its forces were conducting operations against Ukraine at the time and denied intending to target Poland.
The training arrangement that followed inverted the usual direction of military assistance. Rather than a more capable NATO member equipping a non-member partner, Ukraine — a state defending itself outside NATO's formal command — transferred operational counter-drone knowledge into a member state's armed forces, because Ukraine holds the most current and hard-won experience in this domain. Shmyhal described the training as covering the full ecosystem of intercepting hostile unmanned aerial vehicles: locating them, employing electronic jamming, and using interceptor drones to bring them down. Ukraine's approach integrates interceptor drones, heavy machine guns, and electronic warfare measures, and the training addressed engineers and soldiers alike — recognizing that the human pipeline is part of the capability.
The episode illustrates a pattern the war keeps demonstrating: the lessons Ukraine has paid for at the front — in detection, electronic warfare, tactics, and operator training — are becoming shared allied assets rather than local knowledge. That knowledge transfer, however, is only as valuable as the quality of the transition pathway through which it moves. The relevant capabilities are detection, location, and protection: understanding where drone threats originate, tracking them, and neutralizing them in ways that protect civilian and military assets. For Helicon, the directional lesson is clear. Frontline learning is valuable to the entire alliance, and the responsible task is moving that learning into trusted U.S. and allied capability deliberately — with attention to provenance, security, and appropriate transition pathways — rather than allowing it to diffuse informally or be lost. We do not republish Reuters' text; this is a Helicon-written summary linking to the original reporting.
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Responsible AI for defense: human judgment required, provenance and uncertainty by design.
Official SourceTechnical
U.S. Department of Defense
DoD Directive 3000.09 — Autonomy in Weapon Systems
Why this matters
It is the U.S. policy that requires appropriate human judgment over the use of force in autonomous and semi-autonomous weapons.
What it teaches
That the directive mandates rigorous verification and validation, test and evaluation, explainability, auditability, and well-designed human-machine interfaces — human judgment is required, not optional.
Helicon takeaway
Helicon Labs builds toward decision support with provenance and explicit uncertainty — human-in-the-loop, never autonomous lethal decision-making.
DoD Directive 3000.09 is the U.S. Department of Defense policy governing autonomy in weapon systems, and it is the formal anchor for keeping a human meaningfully in the loop. Its central requirement is that autonomous and semi-autonomous weapon systems be designed so that commanders and operators can exercise appropriate levels of human judgment over the use of force. To make that requirement real rather than rhetorical, the directive sets engineering and process conditions around it: rigorous verification and validation, realistic test and evaluation, explainability and auditability so that a system’s behavior can be understood and reviewed, and human-machine interfaces that are readily understandable to trained operators. In other words, human judgment is treated as a design obligation, not an optional add-on, and systems that cannot meet these conditions are not supposed to be fielded. For Helicon, the directive is the policy reference behind a clear engineering posture: Helicon Labs builds toward decision support with memory, provenance, and explicit uncertainty, surfacing confidence and alternatives to a human rather than substituting for human judgment, and never toward autonomous lethal decision-making. The full public-domain text of the directive is hosted on this site for reading; the official DoD PDF remains the authoritative source. Read the original for the authoritative wording.
Optional quick digest prepared by Helicon. The complete public-domain original is hosted here — use “Read full text” to read it in full on this site.
U.S. National Institute of Standards and Technology (NIST)
AI Risk Management Framework (AI RMF)
Why this matters
It is the neutral, widely-adopted U.S. government framework for building trustworthy AI.
What it teaches
That trustworthy AI is organized around four functions — Govern, Map, Measure, and Manage — and characteristics including validity, reliability, safety, security, accountability, transparency, explainability, and fairness.
Helicon takeaway
Helicon treats provenance, uncertainty, and human oversight as design requirements — consistent with the AI RMF — not as features added later.
NIST’s AI Risk Management Framework is the neutral, widely adopted U.S. government framework for building and managing trustworthy AI. It is voluntary and sector-agnostic by design, which is part of why it has become a common reference point across government, industry, and academia rather than a niche compliance document. The framework organizes practice around four core functions that work as a continuous loop: Govern (establish the culture, policies, and accountability for managing AI risk), Map (understand the context and identify where risks arise), Measure (analyze, assess, and track those risks with appropriate methods and metrics), and Manage (prioritize and act on the risks, allocating resources to the most significant). Around these functions it defines the characteristics of trustworthy AI: validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness with harmful bias managed. The framework’s value is that it gives builders a shared vocabulary and structure for reasoning about AI risk before, during, and after deployment, rather than treating safety as an afterthought. For Helicon, it aligns directly with how Helicon Labs works: provenance, explicit uncertainty, and human oversight are treated as design requirements consistent with the AI RMF, engineered in from the start rather than bolted on once a system already exists.
Optional quick digest prepared by Helicon. The complete public-domain original is hosted here — use “Read full text” to read it in full on this site.
Chief Digital and Artificial Intelligence Office (CDAO)
Why this matters
It is the official home of how the U.S. military actually frames and fields AI.
What it teaches
That the DoD frames its flagship work around decision support — the Maven Smart System for sensor fusion, agentic battle management, and data integration — with the human kept in the loop.
Helicon takeaway
Helicon Labs builds toward decision support with memory, provenance, and explicit uncertainty — never autonomous lethal decision-making.
The Chief Digital and Artificial Intelligence Office (CDAO) is the U.S. Department of Defense organization responsible for accelerating the adoption of data, analytics, and artificial intelligence across the military. Its public material is notable for the language it chooses: it frames flagship efforts around decision support rather than the more aggressive marketing language of decision dominance or advantage. The clearest example is the Maven Smart System, which fuses sensor feeds to help analysts and commanders make sense of large volumes of data and nominate targets for human consideration. CDAO also describes work on agentic battle management and on integrating data across previously siloed systems so that information is usable at speed. Across these efforts the consistent emphasis is that humans remain in the loop on consequential decisions; the goal is to speed and improve human judgment, not to remove it. For Helicon, CDAO is the authoritative reference for how the U.S. military actually frames and fields AI, and it validates the approach Helicon Labs takes: decision support built with memory, provenance, and explicit uncertainty — never autonomous lethal decision-making. Read CDAO’s own framing as the baseline, and keep the claims appropriately hedged.
Optional quick digest prepared by Helicon. The complete public-domain original is hosted here — use “Read full text” to read it in full on this site.
Ukraine Collects a Vast War-Data Trove to Train AI Models
Why this matters
It shows how battlefield data — not just hardware — has become a strategic asset in modern war.
What it teaches
That Ukraine has gathered an enormous archive of drone footage and engagement data, drawn from 15,000+ drone crews, to train automatic-target-recognition and other AI models.
Helicon takeaway
Data provenance, confidence, and uncertainty must be engineered in. AI here is decision support — it should expose its reasoning, not hide it.
Reuters reporting, republished via The Straits Times, describes how Ukraine has assembled an extraordinary archive of battlefield video through the Ochi system — a non-profit Ukrainian digital platform originally designed to give military commanders a unified view of their operating areas by displaying drone footage from all nearby crews side by side on a single screen. The archive has grown to over two million hours of battlefield video since 2022, contributed by more than 15,000 drone crews working on the front lines, with new material arriving at a rate of five to six terabytes per day. The raw scale — described as the equivalent of 228 years of continuous video — is itself a strategic fact.
The significance is conceptual as much as technical. Ukraine is now using this footage to train artificial-intelligence models, including systems for target recognition, combat-tactics analysis, and weapons-effectiveness assessment. According to Oleksandr Dmitriev, the footage gives AI systems battlefield experience that can be translated into mathematical models — allowing algorithms to learn trajectories, effective engagement angles, target shapes and colors under real combat conditions. Analysts cited in the reporting note that the size and quality of the dataset are critical because AI recognition models learn from patterns in real imagery, and synthetic or exercise data does not capture the visual complexity, adversary behavior, and friction of actual operations the way live combat footage does. The Avengers system maintained by Ukraine's Defense Ministry separately centralizes drone and CCTV feeds, and has been described as identifying roughly 12,000 Russian pieces of equipment per week using AI identification tools. Thousands of drones are already using AI-assisted navigation, and Ukrainian companies are developing drone swarms in which computer systems would execute coordinated commands across linked clouds of dozens of units.
The article reframes data itself as a strategic asset of modern war. The side that systematically captures, labels, and learns from real engagement footage builds an advantage that compounds over time, because each model improvement feeds back into better collection and faster iteration. This is a different kind of arms race — measured in datasets, feedback loops, and model generations rather than only in platforms and munitions counts.
That framing also raises hard governance questions that Helicon takes seriously. Combat data carries provenance, quality, and bias problems that are not always visible in model outputs; algorithms trained on one theater's conditions can fail in unfamiliar environments; and the line between human-supervised decision support and automated targeting must be drawn deliberately rather than left implicit. The Helicon position is that AI in this context should function as decision support — exposing confidence levels, provenance, uncertainty, and alternatives to a human decision-maker rather than concealing them — with meaningful human judgment retained over the use of force. This is a Helicon-written summary; the full Reuters reporting is available via The Straits Times.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
The trusted pathways that move technology from innovation to fielded, allied capability.
Defense TransitionOfficial Source
NATO
NATO DIANA — Defence Innovation Accelerator for the North Atlantic
Why this matters
It is NATO’s flagship pathway for moving dual-use deep technology toward allied defense and security use.
What it teaches
That DIANA offers selected companies funding, mentoring, and market access through a network of accelerators and test centres across the Alliance, with pathways into the NATO enterprise and Allied markets.
Helicon takeaway
DIANA is one of the allied innovation pathways Helicon tracks for Ukrainian and allied innovators seeking trusted routes to market.
DIANA, NATO’s Defence Innovation Accelerator for the North Atlantic, is the Alliance’s flagship pathway for moving dual-use deep technology toward allied defence and security use. It runs structured challenge programmes inviting companies to solve defined defence and security problems, and selected firms receive grant funding, a structured acceleration programme, and expert mentoring rather than just a check. What sets DIANA apart is the infrastructure behind it: a network of accelerator sites and test centres spread across the Alliance — on the order of two dozen accelerators and well over a hundred test centres — that give companies access to specialized facilities and end users they could not easily reach on their own. The grants are commonly structured in phases, with an initial phase grant typically around EUR100,000 and further phase funding up to roughly EUR300,000 for projects that advance. Beyond money, the real value is access: a credible route to market within the NATO enterprise and allied markets, plus the validation that comes from being selected by an Alliance programme. For Helicon, DIANA is one of the concrete allied innovation pathways worth tracking on behalf of Ukrainian and allied innovators who need trusted, legitimate routes from a working prototype to fielded allied capability, rather than ad hoc deals. It is a real example of how innovation becomes capability inside the Alliance.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
It is Ukraine’s government platform for coordinating defense-technology innovation — the front door to Ukraine’s defense-tech ecosystem.
What it teaches
That Brave1 connects developers, the military, government, and investors across verticals including logistics, UAV, robotics, demining, cyber, intelligence, navigation, and medical technology.
Helicon takeaway
Brave1 maps where Ukrainian innovation is concentrated — useful context for trusted, responsible transition into allied ecosystems.
Brave1 is the Government of Ukraine’s coordination platform for defense-technology innovation — effectively the front door to Ukraine’s defense-tech ecosystem. It was created to connect the parties that otherwise struggle to find each other quickly in wartime: developers and startups building new systems, the military units that actually need them, the state bodies that fund and procure, and investors looking for credible projects. Brave1 organizes this effort across a wide set of verticals, including logistics, unmanned aerial vehicles, ground robotics, demining, cyber, intelligence, navigation, and medical technology, and it offers practical support along the way — grants, testing and evaluation, and a path toward procurement — so that a promising idea can move from prototype to fielded use far faster than a peacetime process would allow. The compression of that cycle, driven by urgent frontline demand, is part of why Ukrainian defense innovation has matured so rapidly. For partners abroad, Brave1 is valuable as a map: it shows where Ukrainian frontline innovation is actually concentrated and how it is organized, which is exactly the context needed to think about trusted, responsible transition into allied ecosystems. Helicon tracks platforms like this not to extract technology, but to understand where capability is emerging and how it might responsibly reach U.S., EU, NATO, and allied production.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
Unmanned Systems Forces of Ukraine — Official Site
Why this matters
It shows that Ukraine has institutionalized unmanned systems as a distinct branch of its armed forces — a structural milestone in how modern militaries are organized.
What it teaches
That the Unmanned Systems Forces describes itself as the world’s first armed-forces branch built around unmanned and robotic systems across the air, ground, and surface and subsurface maritime domains.
Helicon takeaway
When a nation stands up a dedicated branch for unmanned systems, it signals that this capability is now permanent doctrine, not a wartime improvisation — the strategic backdrop for the technologies Helicon helps transition responsibly.
In 2024 Ukraine created the Unmanned Systems Forces, and its official site describes it as the first branch of any armed forces in the world built specifically around unmanned and robotic systems used in combat operations across the air, ground, and surface and subsurface maritime domains. The significance is organizational rather than tactical. For most of modern history, unmanned systems were treated as tools attached to existing branches — a drone flown by an artillery or reconnaissance unit, for example. Standing up a dedicated branch means dedicated doctrine, training pipelines, procurement priorities, career paths, and command structures built around unmanned and robotic capability as a permanent feature of the force, not a temporary wartime adaptation. The branch spans reconnaissance and strike fixed-wing aircraft, multirotor platforms, interceptor and deep-strike systems, and a growing family of ground robots for tasks such as logistics, mine-laying and clearance, and casualty evacuation that reduce the risk to human soldiers. For allied audiences, the lesson is that the organizational center of gravity in modern war is shifting: nations are now formalizing unmanned systems the way they once formalized air forces. That structural shift is the strategic backdrop for the kinds of technologies Helicon helps move into trusted allied evaluation, manufacturing, and sustainment pathways. This is a Helicon-written summary that links to the official source.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
Work With Us — How DIU Contracts Commercial Technology
Why this matters
It is the clearest public explanation of the fastest commercial-to-DoD pathway.
What it teaches
That DIU uses a Commercial Solutions Opening to award Other Transaction prototype agreements in roughly 60-90 days, with a path to follow-on production — far faster than the traditional 12-24 month cycle.
Helicon takeaway
Helicon structures transitions around real pathways like this one, choosing the route that fits the capability and the customer.
The Defense Innovation Unit’s Work With Us page is the clearest public explanation of one of the fastest commercial-to-Department-of-Defense pathways. DIU exists to bring proven commercial technology into military use quickly, and the mechanism it describes is distinctive. Rather than a traditional procurement, DIU issues a Commercial Solutions Opening — a problem statement inviting commercial solutions — and then awards Other Transaction (OT) prototype agreements to selected companies, typically in roughly 60 to 90 days. The OT authority sidesteps much of the Federal Acquisition Regulation overhead that slows conventional contracting, and a successful prototype carries a built-in path to follow-on production without a fresh full-and-open competition. The contrast that makes this matter is timeline: the traditional defense contracting cycle often runs 12 to 24 months before a company even begins work, long enough to exhaust a startup’s runway. By compressing that to weeks, DIU lowers the barrier for commercial and dual-use firms — including allied innovators — to engage the U.S. military. Helicon structures transitions around real pathways like this one, choosing the route that fits the maturity of the capability and the needs of the customer rather than forcing every technology through the same door.
Optional quick digest prepared by Helicon. The complete public-domain original is hosted here — use “Read full text” to read it in full on this site.
It addresses the question every innovator asks: who owns the intellectual property.
What it teaches
That under DIU Other Transaction agreements, IP is generally retained by the company while the government receives a license or government-purpose rights — and a successful prototype can transition to follow-on production without re-competition.
Helicon takeaway
Structuring IP, licensing, and export pathways early is part of Helicon’s transition discipline — handled with qualified professionals.
Intellectual property is the question every innovator asks before engaging the U.S. government, and DIU’s Other Transaction (OT) framework gives a reassuring answer. Under these agreements, IP is generally retained by the company; the government typically receives a license or government-purpose rights to use what it paid to develop, rather than taking full ownership of the underlying technology. That arrangement lets a firm keep commercializing its product in other markets while still supplying the Department of Defense. Equally important is the transition mechanism: a successful OT prototype can move directly into follow-on production without re-competing the work through a new full-and-open solicitation, which removes a notorious valley of death where promising prototypes die for lack of a contracting path. The practical lesson for innovators — and the discipline Helicon applies — is that IP ownership, licensing terms, and export posture (ITAR and EAR considerations, especially for allied technologies) should be structured deliberately at the very start of an engagement, with qualified legal and export counsel, not negotiated under time pressure once a deal is on the table. Getting the IP and export structure right early is what makes a clean, durable transition possible later.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
It is the reference point for trusted, NDAA-compliant drones and components.
What it teaches
That the Blue UAS Cleared List — vetted commercial drones cleared for DoD use — moved from DIU to the Defense Contract Management Agency on December 3, 2025.
Helicon takeaway
Trusted UAS and component pathways are a manufacturing discipline: knowing the bill of materials and qualifying compliant sources.
The Blue UAS Cleared List is the U.S. government’s roster of commercial small drones that have been vetted as compliant with National Defense Authorization Act (NDAA) restrictions — chiefly the bans on certain foreign-made components and manufacturers — and cleared for federal use. For buyers, it is a trusted shortlist: choosing a Blue-listed platform means the supply-chain and security vetting has already been done. DIU announced that responsibility for maintaining the list transitioned to the Defense Contract Management Agency (DCMA) on December 3, 2025. The handoff is more than administrative. It ties trusted-drone vetting to the agency that already manages contract compliance across the defense-industrial base, signaling that the government intends to treat drone trust as an ongoing compliance discipline rather than a one-time certification. The deeper point, and the reason it appears here, is that supply-chain trust runs all the way down to the bill of materials — the radios, flight controllers, cameras, and chips inside a system, and where they are made. For Helicon, this is a manufacturing discipline: knowing a system’s component lineage and qualifying compliant, trusted sources is part of what makes a capability fieldable in allied ecosystems.
Optional quick digest prepared by Helicon. The complete public-domain original is hosted here — use “Read full text” to read it in full on this site.
Endurance is industrial. Trusted production capacity is the answer to adversary scale.
Defense TransitionOfficial Source
European Commission
European Defence Industrial Strategy (EDIS)
Why this matters
It is the EU’s strategy for strengthening the European defense-industrial base — the policy frame for allied manufacturing and readiness in Europe.
What it teaches
That EDIS aims to make the European defense industry more ready, responsive, and collaborative — boosting joint procurement, production capacity, and supply-chain resilience.
Helicon takeaway
Trusted manufacturing is increasingly transatlantic. EDIS is part of the European context Helicon works within for allied production.
The European Defence Industrial Strategy (EDIS), presented by the European Commission, is the EU’s strategy for strengthening the European defense-industrial base, and it is the policy frame for how allied manufacturing and readiness are organized on the European side. Its core aim is to make the European defense industry more ready, more responsive, and more collaborative. In practice that means several connected shifts: boosting joint and collaborative procurement so member states buy together rather than fragmenting demand across dozens of incompatible national programs; expanding production capacity so industry can surge output when needed; and building more resilient supply chains so Europe is less exposed to single points of failure for critical inputs. The deeper change EDIS signals is one of posture — moving from reactive, emergency support driven by the war toward sustained, structural investment in defense industry as a long-term strategic priority. The war exposed how thin European stockpiles and production rates had become, and EDIS is the institutional answer to that gap. For Helicon, EDIS matters because trusted manufacturing is increasingly transatlantic rather than confined to any one country. It is the European counterpart to the U.S. industrial-base questions raised by the war, and it is part of the policy context Helicon works within when helping move selected technologies toward trusted allied production across both the European and U.S. ecosystems.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
U.S. Army Awards Contract for Domestic TNT Production
Why this matters
It marks a deliberate move to rebuild U.S. domestic capacity to produce a basic munitions explosive after decades of reliance on foreign sources.
What it teaches
That the U.S. Army awarded a contract to establish domestic TNT production — addressing a long-standing gap in the U.S. defense-industrial base.
Helicon takeaway
Trusted supply chains start at the raw-material level. Rebuilding domestic and allied industrial capacity is central to credible deterrence.
The U.S. Army announced a contract to establish domestic production of TNT, a foundational military explosive that the United States had not manufactured on its own soil for decades, relying instead on foreign suppliers for this basic munitions input. The award is significant precisely because of how unglamorous it is. TNT is not a cutting-edge system; it is a building block, and the fact that a country with the world’s largest defense budget had let its ability to produce it domestically lapse illustrates how deep the erosion of industrial-base capacity had become. Re-establishing that production is a deliberate move to close a long-standing gap, reduce dependence on overseas sources for essential inputs, and make the supply chain for munitions more resilient and trusted. It is a concrete example of allied industrial-readiness investment on the U.S. side, and it pairs naturally with reporting on adversary explosives capacity: both point to the same lesson the war keeps teaching, that endurance is decided by the ability to produce the basics at scale. For Helicon, the takeaway is that trusted supply chains start at the raw-material level, not just at the finished platform. Rebuilding domestic and allied industrial capacity for foundational inputs is central to credible deterrence, because a force can only sustain what its industry can actually make.
Optional quick digest prepared by Helicon. The complete public-domain original is hosted here — use “Read full text” to read it in full on this site.
Russia Building Major New Explosives Facility as Ukraine War Drags On
Why this matters
It documents how adversary industrial capacity — not just front-line tactics — shapes the war’s endurance.
What it teaches
That a Reuters investigation, using procurement records and satellite imagery, found Russia constructing a new explosives production line in Siberia intended to produce a potent high explosive used in many munitions.
Helicon takeaway
Endurance is industrial. Trusted allied production capacity — the work Helicon supports — is the answer to adversary scale, framed soberly and factually.
A May 2025 Reuters investigation, drawing on publicly accessible state procurement documents, satellite imagery, and a construction contractor familiar with the project, found that Russia is building a major new explosives production line at the Biysk Oleum Plant (BOZ) — part of the Sverdlov Plant complex — located in Biysk, Siberia, approximately 3,000 kilometers east of Moscow. The site's remote location is operationally significant: it places the facility beyond the reach of most Ukrainian long-range drones that have been used to strike Russian arms production facilities closer to the front.
Reuters' forensic analysis of procurement codes, documented chemical precursors including urotropine and nitric acid, and satellite imagery tracking construction progress from late 2023 through April 2025 pointed strongly to a new production line for RDX — a high explosive not currently manufactured at BOZ — with analysts noting the structural and chemical similarities with HMX production as well. The total project budget is reported at 1.5 billion rubles (approximately $189 million), with construction scheduled to have begun in 2023 and to conclude by end of 2025. Projected annual output is on the order of 3,000 tons — enough, by Reuters' own calculation, to fill the warheads of approximately 1.5 million standard 152mm OF-29 artillery shells. An explosives specialist at Ludwig Maximilian University described the quantity as substantial and said it would significantly enhance Russian defense capabilities; RUSI's Joseph Watling noted that increasing high-explosive availability is vital to Russia's ongoing military effort.
The industrial context deepens the story. Russia's existing explosives production is concentrated at the Sverdlov Plant's Dzerzhinsk facility, which Reuters reports is now within range of Ukrainian long-range drones — a vulnerability the Biysk expansion appears designed to address by distributing production capacity beyond drone reach. Russia has also relied on large imports of artillery shells from North Korea, with Ukraine's military intelligence reporting approximately 2.7 million shells imported in 2024, though largely of subpar quality. Russia's own reported domestic production of 122mm and 152mm rounds ran to approximately 2 million shells in 2024. The new RDX capacity would materially reduce reliance on both the vulnerable Dzerzhinsk plant and on imported North Korean munitions.
The significance of the investigation is what it reveals about the nature of a sustained high-intensity war. Front-line tactics and novel systems capture attention, but endurance is decided upstream — in the industrial capacity to keep producing the basic inputs of combat power. An adversary investing $189 million in new deep-interior explosives infrastructure is signaling both intent to fight a long war and the industrial seriousness to sustain it. The answer to adversary industrial scale is trusted allied production capacity built at comparable seriousness — the kind of defense-industrial base development that Helicon supports in moving selected wartime-developed technologies into U.S., European, and NATO acquisition pathways. We do not republish Reuters' text; this is a Helicon-written summary linking to the original investigation.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
It is happening now: the 2026 conference is taking place in Gdansk on June 24-26, 2026, co-hosted by Poland and Ukraine.
What it teaches
That the defense dimension covers defense-industrial capacity, air defense, unmanned technology, military mobility, dual-use and AI, and defense-industrial partnerships.
Helicon takeaway
This is the venue where allied defense-industrial resilience is being organized in real time — the policy frame around Helicon’s transition work.
The Ukraine Recovery Conference (URC) 2026 is taking place in Gdansk on June 24-26, 2026, co-hosted by Poland and Ukraine — a venue that is itself significant, pairing a frontline-adjacent NATO and EU member with Ukraine to anchor recovery firmly within the allied community. The URC is the principal international forum where governments, industry, and financial institutions coordinate support for Ukraine’s reconstruction, and the 2026 edition gives explicit weight to a security and defense dimension. That dimension spans defense-industrial capacity (the ability to produce at scale), air defense, unmanned technology, military mobility, dual-use and artificial-intelligence applications, and defense-industrial partnerships between Ukraine and allied states. Treating these as recovery topics — not separate military matters — reflects the recognition that a country cannot rebuild while it cannot defend itself, and that defense production is part of national resilience. Because the conference is happening now rather than in prospect, it is best understood as the live policy frame within which allied defense-industrial cooperation is being organized in real time. For Helicon, the URC defense dimension is the institutional context around its own work: helping move selected Ukrainian defense technologies into trusted U.S., EU, NATO, and allied production. Official session recordings and a livestream are expected to be published by the organizers.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
Where to follow the situation responsibly. Helicon hosts no live map.
ReportOfficial SourceCurrent SituationHuman Story
UN Human Rights Monitoring Mission in Ukraine (HRMMU)
Protection of Civilians in Armed Conflict — May 2026
Why this matters
It is the primary UN monitoring record for the most recent verified civilian-harm figures.
What it teaches
That HRMMU verified at least 274 civilians killed and 1,763 injured in May 2026 — the highest monthly total since April 2022.
Helicon takeaway
Long-range strikes and deeper drone reach drive these figures — reinforcing why air and missile defense, counter-drone, and resilient infrastructure matter.
This is the primary UN monitoring record for the most recent verified civilian-harm figures from Ukraine. According to the UN Human Rights Monitoring Mission in Ukraine (HRMMU), at least 274 civilians were killed and 1,763 injured across Ukraine in May 2026 — a total of 2,037 verified civilian casualties for the month. HRMMU's own comparative data shows this represents the highest monthly total of civilians killed and injured since April 2022, a 93 percent increase compared with May 2025 (191 killed, 865 injured), and a 23 percent increase over April 2026 (240 killed, 1,422 injured). The acceleration is steep.
The weapon-type breakdown is as instructive as the headline totals. Long-range weapons — missiles and drones reaching cities far from the contact line — caused 115 deaths and 803 injuries, accounting for 45 percent of all May casualties. These are strikes on urban centers such as Kyiv, Dnipro, and Zaporizhzhia, where populations have little early warning and no tactical shelter comparable to what frontline communities develop over time. Short-range drones caused 64 deaths and 539 injuries — more civilians killed and injured by this weapon type in May 2026 than in any month since the full-scale invasion began. Aerial bombardments accounted for 57 deaths and 179 injuries; artillery and multiple-launch rocket systems for a further 28 deaths and 208 injuries; and explosive remnants of war and mines for 10 deaths and 34 injuries. Civilian casualties were recorded across 20 regions of Ukraine and the city of Kyiv, with 1,973 of 2,037 total casualties occurring in Ukrainian-government-controlled territory.
As with all HRMMU reporting, these are verified minimums. Each casualty is individually corroborated before it is counted, which means the methodology is deliberately conservative and the true totals are very likely higher. The value of the HRMMU source is precisely this discipline: an independent UN body applying a consistent standard, insulated from either government's information operations, providing figures that are credible in a contested environment where casualty numbers are routinely disputed by parties to the conflict.
The pattern documented in this report is the operational basis for Helicon's capability priorities. When long-range missiles and drones are killing civilians in Kyiv and Dnipro — far from any front line — the relevant capabilities are air and missile defense systems that intercept threats before they reach populated areas. When short-range drones are causing their highest-ever monthly civilian toll near the front, the relevant capabilities are detection, electronic warfare, and counter-drone protection that reduce exposure for civilians who remain in contested communities. When explosive remnants of war and mines add to the toll after the fighting moves on, the relevant capability is demining. The May 2026 HRMMU figures translate civilian protection from a policy aspiration into a concrete engineering and procurement problem. This is a Helicon-written summary; the full verified record is available at the OHCHR Ukraine monitoring mission page.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
It is the clearest one-page reference for the war’s actual timeline — useful for anyone who still believes the war began in 2022.
What it teaches
That Russia’s campaign began in 2014 with the annexation of Crimea and the war in Donbas, escalating to the full-scale invasion in February 2022.
Helicon takeaway
Understanding the long arc of the war is the starting point for understanding why Ukraine’s defense innovation matured the way it did.
The Council on Foreign Relations Global Conflict Tracker situates the war in Ukraine within its continuously updated catalogue of global conflicts, and its most important framing contribution is the timeline: the tracker dates the conflict's origin to 2014, not 2022. Russia's annexation of Crimea in March 2014 and the subsequent armed conflict in Donetsk and Luhansk — where Russian-backed separatists declared independence following disputed referendums and Russian troops and equipment were reported near Donetsk with cross-border shelling — constitute the conflict's first phase. The full-scale invasion of 24 February 2022 was an escalation of an existing campaign, not a new war. That distinction matters for understanding why Ukraine's armed forces and defense-technology sector did not begin from zero in 2022: they had been adapting under fire for eight years before the full-scale invasion.
The tracker maps the arc of the war through its major phases: the failed rapid seizure of Kyiv in early 2022 and Russia's subsequent withdrawal from Ukraine's capital region by April 6; the pivot east and the fall of Mariupol in May 2022; Ukraine's counteroffensives in the northeast and south in autumn 2022, recovering Kherson and significant territory in Kharkiv region; the grueling siege of Bakhmut through spring 2023; the June 2023 Ukrainian counteroffensive that achieved limited gains against heavily fortified Russian positions; and the subsequent consolidation of a largely attritional conflict along contested lines in the east and south. As of the tracker's June 2026 update, Russia occupies roughly 20 percent of Ukraine and gained approximately five thousand square kilometers in 2025. Nearly 56,000 civilian casualties have been recorded since the invasion, 3.7 million Ukrainians are internally displaced, 5.9 million are registered as refugees, and 10.8 million people require humanitarian assistance.
The diplomatic dimension has grown substantially more complex since early 2026. U.S. aid to Ukraine since January 2022 stands at approximately $188 billion; EU aid at approximately $197 billion. The tracker documents the Trump administration's engagement in seeking a settlement, including a twenty-point draft peace proposal and an Alaska summit between Presidents Trump and Putin, while noting that territorial concessions and security guarantees remain unresolved. Ukraine has been deepening security relationships with Gulf states, signing ten-year security agreements with Saudi Arabia, Qatar, and the UAE. Meanwhile, Russia's aerial campaign has intensified: a May 2026 strike involved over 1,560 drones and 56 missiles, and a May 24 attack on Kyiv included a nuclear-capable Oreshnik ballistic missile alongside hundreds of drones.
Because the tracker page is updated as events develop, it is best used as a live orientation tool rather than a fixed historical snapshot. Consulting it provides the current picture — territorial situation, diplomatic status, humanitarian scale — in a format that contextualizes Ukraine's conflict within CFR's broader global-conflict catalogue. This is a Helicon-written summary; for current figures and the latest developments, consult the Council on Foreign Relations directly.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
These resources are included for technical education and high-level understanding. Helicon does not publish operational instructions, sensitive tactics, weapons design data, source code, frequencies, or controlled technical information through the public site.
TechnicalDeep Dive
arXiv (academic survey)
A Survey on Counter-Unmanned Aircraft Systems (C-UAS) Technologies
Why this matters
It is a high-level, openly available academic overview of how counter-drone systems detect and mitigate hostile UAS.
What it teaches
That detection approaches include acoustic, vision, passive radio-frequency, and radar sensing — often fused — while mitigation spans capture and jamming, each with trade-offs.
Helicon takeaway
Helicon’s Counter-UxS focus is detection, location, and protection. We do not publish operational instructions; this survey is for high-level understanding only.
This open-access academic survey is a high-level overview of how counter-unmanned-aircraft-system (C-UAS) technologies detect and mitigate hostile drones. On the detection side it organizes the main sensing approaches and their trade-offs: acoustic sensing (listening for rotor and motor signatures), electro-optical and infrared vision (seeing the drone or its heat), passive radio-frequency detection (picking up control and video links without emitting anything), and radar (active detection at range). A recurring theme is that no single sensor is sufficient — each has blind spots — so practical systems fuse multiple modalities to improve reliability and reduce false alarms, especially against small, low, slow targets that are hard to distinguish from birds or clutter. On the mitigation side it surveys the spectrum from non-destructive options, such as capture or radio-frequency jamming that severs the link, to destructive ones, each carrying trade-offs in safety, collateral risk, legality, and effectiveness. Read at this level, the survey is useful for understanding the problem space and the engineering tensions involved, not as an operational how-to. That framing matches Helicon’s Counter-UxS focus, which is detection, location, and protection rather than offensive technique. Helicon does not publish operational instructions; this literature overview is included strictly for high-level understanding of why counter-drone is hard and where the design choices lie.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
A Survey on UAV Electronic-Warfare and Cyber Threats and Countermeasures
Why this matters
It frames the electronic-warfare and cyber dimension of unmanned systems in structured, defensive terms.
What it teaches
That UAV threats can be analyzed with structured models (such as STRIDE) across prevention, detection, and mitigation — emphasizing resilience and defense.
Helicon takeaway
Resilience under EW and cyber pressure is a design requirement. Helicon screens for capability that holds up, not demonstrations under clean conditions.
This open-access academic survey examines the electronic-warfare and cyber dimension of unmanned aerial vehicles — the threats they face and the countermeasures available — and it does so in structured, defensive terms. Rather than cataloging attacks as a how-to, it applies established threat-modeling frameworks, including the STRIDE model (spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege), to reason systematically about where an unmanned system is vulnerable. It then organizes defenses across three stages: prevention (designing systems so weaknesses do not exist or are hard to exploit), detection (recognizing when an attack or interference is underway), and mitigation (limiting damage and recovering). The throughline is resilience: how to harden links, navigation, and control so that a drone keeps functioning, or fails safely, under jamming, spoofing, and cyber pressure. That analytical, defense-oriented framing is exactly why it belongs on a technical shelf rather than in operational guidance. For Helicon, the survey reinforces a core screening principle: resilience under electronic-warfare and cyber pressure is a design requirement, not a nice-to-have. Helicon screens for capability that holds up under contested, real-world conditions — jammed, spoofed, and degraded — rather than demonstrations that only work in clean laboratory or range conditions. Understanding the threat structure is the first step toward building systems that survive it.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
U.S. National Institute of Standards and Technology (NIST)
AI Risk Management Framework (AI RMF)
Why this matters
It is the neutral, widely-adopted U.S. government framework for building trustworthy AI.
What it teaches
That trustworthy AI is organized around four functions — Govern, Map, Measure, and Manage — and characteristics including validity, reliability, safety, security, accountability, transparency, explainability, and fairness.
Helicon takeaway
Helicon treats provenance, uncertainty, and human oversight as design requirements — consistent with the AI RMF — not as features added later.
NIST’s AI Risk Management Framework is the neutral, widely adopted U.S. government framework for building and managing trustworthy AI. It is voluntary and sector-agnostic by design, which is part of why it has become a common reference point across government, industry, and academia rather than a niche compliance document. The framework organizes practice around four core functions that work as a continuous loop: Govern (establish the culture, policies, and accountability for managing AI risk), Map (understand the context and identify where risks arise), Measure (analyze, assess, and track those risks with appropriate methods and metrics), and Manage (prioritize and act on the risks, allocating resources to the most significant). Around these functions it defines the characteristics of trustworthy AI: validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness with harmful bias managed. The framework’s value is that it gives builders a shared vocabulary and structure for reasoning about AI risk before, during, and after deployment, rather than treating safety as an afterthought. For Helicon, it aligns directly with how Helicon Labs works: provenance, explicit uncertainty, and human oversight are treated as design requirements consistent with the AI RMF, engineered in from the start rather than bolted on once a system already exists.
Optional quick digest prepared by Helicon. The complete public-domain original is hosted here — use “Read full text” to read it in full on this site.
DoD Directive 3000.09 — Autonomy in Weapon Systems
Why this matters
It is the U.S. policy that requires appropriate human judgment over the use of force in autonomous and semi-autonomous weapons.
What it teaches
That the directive mandates rigorous verification and validation, test and evaluation, explainability, auditability, and well-designed human-machine interfaces — human judgment is required, not optional.
Helicon takeaway
Helicon Labs builds toward decision support with provenance and explicit uncertainty — human-in-the-loop, never autonomous lethal decision-making.
DoD Directive 3000.09 is the U.S. Department of Defense policy governing autonomy in weapon systems, and it is the formal anchor for keeping a human meaningfully in the loop. Its central requirement is that autonomous and semi-autonomous weapon systems be designed so that commanders and operators can exercise appropriate levels of human judgment over the use of force. To make that requirement real rather than rhetorical, the directive sets engineering and process conditions around it: rigorous verification and validation, realistic test and evaluation, explainability and auditability so that a system’s behavior can be understood and reviewed, and human-machine interfaces that are readily understandable to trained operators. In other words, human judgment is treated as a design obligation, not an optional add-on, and systems that cannot meet these conditions are not supposed to be fielded. For Helicon, the directive is the policy reference behind a clear engineering posture: Helicon Labs builds toward decision support with memory, provenance, and explicit uncertainty, surfacing confidence and alternatives to a human rather than substituting for human judgment, and never toward autonomous lethal decision-making. The full public-domain text of the directive is hosted on this site for reading; the official DoD PDF remains the authoritative source. Read the original for the authoritative wording.
Optional quick digest prepared by Helicon. The complete public-domain original is hosted here — use “Read full text” to read it in full on this site.
Ukraine Collects a Vast War-Data Trove to Train AI Models
Why this matters
It shows how battlefield data — not just hardware — has become a strategic asset in modern war.
What it teaches
That Ukraine has gathered an enormous archive of drone footage and engagement data, drawn from 15,000+ drone crews, to train automatic-target-recognition and other AI models.
Helicon takeaway
Data provenance, confidence, and uncertainty must be engineered in. AI here is decision support — it should expose its reasoning, not hide it.
Reuters reporting, republished via The Straits Times, describes how Ukraine has assembled an extraordinary archive of battlefield video through the Ochi system — a non-profit Ukrainian digital platform originally designed to give military commanders a unified view of their operating areas by displaying drone footage from all nearby crews side by side on a single screen. The archive has grown to over two million hours of battlefield video since 2022, contributed by more than 15,000 drone crews working on the front lines, with new material arriving at a rate of five to six terabytes per day. The raw scale — described as the equivalent of 228 years of continuous video — is itself a strategic fact.
The significance is conceptual as much as technical. Ukraine is now using this footage to train artificial-intelligence models, including systems for target recognition, combat-tactics analysis, and weapons-effectiveness assessment. According to Oleksandr Dmitriev, the footage gives AI systems battlefield experience that can be translated into mathematical models — allowing algorithms to learn trajectories, effective engagement angles, target shapes and colors under real combat conditions. Analysts cited in the reporting note that the size and quality of the dataset are critical because AI recognition models learn from patterns in real imagery, and synthetic or exercise data does not capture the visual complexity, adversary behavior, and friction of actual operations the way live combat footage does. The Avengers system maintained by Ukraine's Defense Ministry separately centralizes drone and CCTV feeds, and has been described as identifying roughly 12,000 Russian pieces of equipment per week using AI identification tools. Thousands of drones are already using AI-assisted navigation, and Ukrainian companies are developing drone swarms in which computer systems would execute coordinated commands across linked clouds of dozens of units.
The article reframes data itself as a strategic asset of modern war. The side that systematically captures, labels, and learns from real engagement footage builds an advantage that compounds over time, because each model improvement feeds back into better collection and faster iteration. This is a different kind of arms race — measured in datasets, feedback loops, and model generations rather than only in platforms and munitions counts.
That framing also raises hard governance questions that Helicon takes seriously. Combat data carries provenance, quality, and bias problems that are not always visible in model outputs; algorithms trained on one theater's conditions can fail in unfamiliar environments; and the line between human-supervised decision support and automated targeting must be drawn deliberately rather than left implicit. The Helicon position is that AI in this context should function as decision support — exposing confidence levels, provenance, uncertainty, and alternatives to a human decision-maker rather than concealing them — with meaningful human judgment retained over the use of force. This is a Helicon-written summary; the full Reuters reporting is available via The Straits Times.
Optional quick digest prepared by Helicon from the cited source. Open the original for the full text.
Originating Component: Office of the Under Secretary of Defense for Policy
Effective: January 25, 2023
Releasability: Cleared for public release. Available on the Directives Division Website at https://www.esd.whs.mil/DD/.
Reissues and Cancels: DoD Directive 3000.09, "Autonomy in Weapon Systems," November 21, 2012
Approved by: Kathleen H. Hicks, Deputy Secretary of Defense
Purpose
This directive: - Establishes policy and assigns responsibilities for developing and using autonomous and semi-autonomous functions in weapon systems, including armed platforms that are remotely operated or operated by onboard personnel. - Establishes guidelines designed to minimize the probability and consequences of failures in autonomous and semi-autonomous weapon systems that could lead to unintended engagements. - Establishes the Autonomous Weapon Systems Working Group.
Section 1: General Issuance Information
#### 1.1. Applicability
a. This directive applies to: (1) OSD, the Military Departments, the Office of the Chairman of the Joint Chiefs of Staff (CJCS) and the Joint Staff, the Combatant Commands, the Office of Inspector General of the Department of Defense, the Defense Agencies, the DoD Field Activities, and all other organizational entities within the DoD. (2) The design, development, acquisition, testing, fielding, and employment of autonomous and semi-autonomous weapon systems, including guided munitions that are capable of automated target selection. (3) The application of lethal or non-lethal, kinetic or non-kinetic, force by autonomous or semi-autonomous weapon systems.
b. This directive does not apply to: (1) Autonomous or semi-autonomous cyberspace capabilities. (2) Unarmed platforms, whether remotely operated or operated by onboard personnel, and whether autonomous or semi-autonomous. (3) Unguided munitions. (4) Munitions manually guided by the operator (e.g., laser- or wire-guided munitions). (5) Mines. (6) Unexploded explosive ordnance. (7) Autonomous or semi-autonomous systems that are not weapon systems.
#### 1.2. Policy
a. Autonomous and semi-autonomous weapon systems will be designed to allow commanders and operators to exercise appropriate levels of human judgment over the use of force.
(1) Systems will go through rigorous hardware and software verification and validation (V&V) and realistic system developmental and operational test and evaluation (T&E) in accordance with Section 3. Training, doctrine, and tactics, techniques, and procedures (TTPs) applicable to the system in question will be established. These measures will provide sufficient confidence that autonomous and semi-autonomous weapon systems:
(a) Function as anticipated in realistic operational environments against adaptive adversaries taking realistic and practicable countermeasures. (b) Complete engagements within a timeframe and geographic area, as well as other relevant environmental and operational constraints, consistent with commander and operator intentions. If unable to do so, the systems will terminate the engagement or obtain additional operator input before continuing the engagement. (c) Are sufficiently robust to minimize the probability and consequences of failures.
(2) Consistent with the potential consequences of an unintended engagement or unauthorized parties interfering with the operation of the system, physical hardware and software will be designed with appropriate: (a) System safety, anti-tamper mechanisms, and cybersecurity in accordance with DoD Instruction (DoDI) 8500.01 and Military Standard 882E. (b) Human-machine interfaces and controls. (c) Technologies and data sources that are transparent to, auditable by, and explainable by relevant personnel.
(3) For operators to make informed and appropriate decisions regarding the engagement of targets, the human-machine interface for autonomous and semi-autonomous weapon systems will: (a) Be readily understandable to trained operators, such as by clearly indicating what actions operators need to perform and which actions the system will perform. (b) Provide transparent feedback on system status. (c) Provide clear procedures for trained operators to activate and deactivate system functions.
b. Persons who authorize the use of, direct the use of, or operate autonomous and semi-autonomous weapon systems will do so with appropriate care and in accordance with the law of war, applicable treaties, weapon system safety rules, and applicable rules of engagement (ROE). The use of AI capabilities in autonomous or semi-autonomous weapons systems will be consistent with the DoD AI Ethical Principles, as provided in Paragraph 1.2.f.
c. With the exception of systems intended to be used in a manner that falls within the policies in Paragraphs 1.2.d.(1) through 1.2.d.(4), autonomous weapon systems, including weapon systems with both autonomous and semi-autonomous modes of operation, must be approved by the Under Secretary of Defense for Policy (USD(P)), the Under Secretary of Defense for Research and Engineering (USD(R&E)), and the Vice Chairman of the Joint Chiefs of Staff (VCJCS) before formal development. They must be approved again by the USD(P), the Under Secretary of Defense for Acquisition and Sustainment (USD(A&S)), and the VCJCS before fielding. These requirements for approval are supplementary to the requirements in other applicable policies and issuances. Autonomous weapon systems requiring these senior approvals in accordance with Section 4 of this directive before formal development and again before fielding include:
(1) Autonomous weapon systems that have not previously been reviewed and approved in accordance with this directive, including autonomous weapon systems that are modifications of an existing non-autonomous weapon system. (2) Modified versions of previously approved autonomous weapon systems whose system algorithms, intended mission sets, intended operational environments, intended target sets, or expected adversarial countermeasures substantially differ from those applicable to the previously approved weapon systems so as to fall outside the scope of what was previously approved in the senior review. Such modified systems require a new senior review and approval before formal development and again before fielding.
d. The senior review described in Paragraph 1.2.c is not required for weapon systems intended to be used in the manner described in Paragraphs 1.2.d.(1) through 1.2.d.(4). These will be considered for approval in accordance with applicable policies and issuances, such as applicable issuances related to the Defense Acquisition System. Weapon systems that do not require the senior review provided in Paragraph 1.2.c are:
(1) Semi-autonomous weapon systems used to apply lethal or non-lethal, kinetic or non-kinetic, force without any modes of operation in which they are intended to function as an autonomous weapon system. (2) Operator-supervised autonomous weapon systems used to select and engage materiel targets for local defense to intercept attempted time-critical or saturation attacks for: (a) Static defense of installations with personnel, including networked defense where the autonomous weapon system is not co-located with the installation. (b) Onboard and/or networked defense of platforms with onboard personnel. (3) Operator-supervised autonomous weapon systems used to select and engage materiel targets for defending operationally deployed remotely piloted or autonomous vehicles and/or vessels. (4) Autonomous weapon systems used to apply non-lethal, non-kinetic force against materiel targets in accordance with DoDD 3000.03E.
e. International sales or transfers of autonomous and semi-autonomous weapon systems will be approved in accordance with existing technology security and foreign disclosure requirements and processes in accordance with DoDD 5111.21.
f. The design, development, deployment, and use of AI capabilities in autonomous and semi-autonomous weapon systems will be consistent with the DoD AI Ethical Principles and the DoD Responsible Artificial Intelligence Strategy and Implementation Pathway. The DoD AI Ethical Principles, as adopted in the February 21, 2020 Secretary of Defense Memorandum, are:
(1) Responsible. DoD personnel will exercise appropriate levels of judgment and care, while remaining responsible for the development, deployment, and use of AI capabilities. (2) Equitable. The DoD will take deliberate steps to minimize unintended bias in AI capabilities. (3) Traceable. The DoD's AI capabilities will be developed and deployed such that relevant personnel possess an appropriate understanding of the technology, development processes, and operational methods applicable to AI capabilities, including with transparent and auditable methodologies, data sources, and design procedures and documentation. (4) Reliable. The DoD's AI capabilities will have explicit, well-defined uses, and the safety, security, and effectiveness of such capabilities will be subject to testing and assurance within those defined uses across their entire life cycles. (5) Governable. The DoD will design and engineer AI capabilities to fulfill their intended functions while possessing the ability to detect and avoid unintended consequences, and the ability to disengage or deactivate deployed systems that demonstrate unintended behavior.
Section 2: Responsibilities
#### 2.1. USD(P)
The USD(P): a. Provides policy oversight for developing and employing autonomous and semi-autonomous weapon systems. b. Receives requests for approval of systems submitted in accordance with Paragraph 1.2.c, and in coordination with the USD(A&S) or USD(R&E) and the VCJCS, reviews and considers for approval such systems. c. Issues guidance to help implement this directive, and reviews, as necessary, the appropriateness of such guidance given the continual advancement of new technologies and changing warfighter needs. d. Approves the DoD position on international sales or transfers of autonomous and semi-autonomous weapon systems in accordance with existing technology security and foreign disclosure requirements and processes. e. Supervises and assigns a chair for the Autonomous Weapon Systems Working Group, provides necessary logistical and administrative support for the working group, approves the charter for the working group, and provides guidance and terms of reference as needed.
#### 2.2. USD(A&S)
The USD(A&S): a. In coordination with the USD(P) and the VCJCS, reviews and considers for approval weapon systems submitted before fielding in accordance with Paragraph 1.2.c. b. Ensures that DoD guidance relating to the Defense Acquisition System includes a requirement to document the determination that an autonomous or semi-autonomous weapon system is intended to be used in a manner that falls within the policies in Paragraphs 1.2.d.(1) through 1.2.d.(4), and therefore does not require senior approval in accordance with this directive. This documentation should occur before formal development and again before fielding, regardless of the acquisition pathway that is applicable to that weapon system.
#### 2.3. USD(R&E)
The USD(R&E): a. Oversees establishment of standards and evaluation metrics for developmental testing, safety certification, and reliability assessment of autonomous and semi-autonomous weapon systems, with particular attention to the risk of unintended engagements or operational interference by unauthorized parties. b. Oversees establishment of science and technology and research and development priorities for autonomy in weapon systems, including the development of new methods of V&V and T&E and the establishment of minimum thresholds of risk and reliability for the performance of autonomy in weapon systems. c. Oversees formulation of concrete, testable requirements for all non-AI elements of autonomous and semi-autonomous weapon systems. d. Collaborates with the Chief Digital and Artificial Intelligence Officer (CDAO) to formulate concrete, testable requirements for implementing the DoD AI Ethical Principles and the DoD Responsible AI Strategy and Implementation Pathway. e. Oversees and evaluates the developmental testing of autonomous and semi-autonomous weapon systems to assess the risk of failures. f. Develops and maintains workforce certification processes, talent management, and curricula to support T&E and V&V of autonomous and semi-autonomous weapon systems by DoD personnel. g. In coordination with the USD(P) and the VCJCS, reviews and considers for approval weapon systems submitted before entering formal development in accordance with Paragraph 1.2.c. h. Coordinates with the Director, Operational Test and Evaluation (DOT&E) and the appropriate Secretary of a Military Department or Commander, United States Special Operations Command (USSOCOM) to provide for monitoring to identify and address when changes to the system design or operational environment require additional T&E to provide sufficient confidence that the system will continue to avoid unintended engagements and resist interference by unauthorized parties.
#### 2.4. Under Secretary of Defense for Personnel and Readiness
In accordance with DoDD 1322.18, the Under Secretary of Defense for Personnel and Readiness oversees and establishes policy for: a. Individual military training programs for the Total Force relating to autonomous and semi-autonomous weapon systems. b. Individual and functional training programs for military personnel and the collective training programs of military units and staffs relating to autonomous and semi-autonomous weapon systems.
#### 2.5. DOT&E
The DOT&E: a. Oversees development of realistic operational T&E standards for autonomous and semi-autonomous weapon systems, including requirements for data collection and standards for T&E of any changes to the system following initial operational T&E (IOT&E), in accordance with Paragraph 1.2.a.(1) and Section 3. b. Evaluates whether autonomous and semi-autonomous weapon systems under DOT&E oversight have met standards for rigorous V&V and T&E in realistic operational conditions, including potential adversary action, to provide sufficient confidence that the probability and consequences of failures have been minimized. c. Establishes standards for data collection post-fielding and monitoring and assessment by programs. d. Coordinates with the USD(R&E) and the appropriate Secretary of a Military Department or Commander, USSOCOM to provide for monitoring to identify and address when changes to the system design or operational environment require additional T&E to provide sufficient confidence that the system will continue to avoid unintended engagements and resist interference by unauthorized parties. e. Reviews and approves operational and live fire test plans for autonomous and semi-autonomous weapon systems for Major Defense Acquisition Programs and programs designated for DOT&E oversight.
#### 2.6. General Counsel of the Department of Defense (GC DOD)
In accordance with DoDD 5000.01, DoDD 2311.01, DoDD 5145.01, and, where applicable, DoDD 3000.03E, the GC DoD provides for guidance on, and coordination of, significant legal issues in autonomy in weapon systems. The GC DoD also coordinates on the review of the legality of weapon systems submitted in accordance with Paragraph 1.2.c.
#### 2.7. Assistant to the Secretary of Defense for Public Affairs
The Assistant to the Secretary of Defense for Public Affairs coordinates on the development of guidance on public affairs matters concerning autonomous and semi-autonomous weapon systems and the use of such guidance and approves final guidance release.
#### 2.8. CDAO
The CDAO: a. Monitors and evaluates AI capabilities in and cybersecurity for autonomous and semi-autonomous weapon systems, in accordance with Paragraph 1.2.a.(2)(a) of this directive and DoDI 8500.01, and advises the Secretary of Defense on such matters. b. Collaborates with the USD(R&E) to formulate concrete, testable requirements for implementing the DoD AI Ethical Principles and the DoD Responsible AI Strategy and Implementation Pathway. c. Establishes policy and issues guidance on definitions of requirements and testability for AI-enabled systems to implement and demonstrate adherence to the DoD AI Ethical Principles and the DoD Responsible AI Strategy and Implementation Pathway. d. Issues guidance on T&E practices for AI capabilities in autonomous or semi-autonomous weapon systems. e. Coordinates with the USD(R&E) and DOT&E on developing and using common tools and infrastructure for T&E and V&V of AI capabilities in autonomous or semi-autonomous weapon systems.
#### 2.9. Secretaries of the Military Departments; Commander, USSOCOM; and Directors of the Defense Agencies and DoD Field Activities
The Secretaries of the Military Departments; the Commander, USSOCOM; and, under the authority, direction, and control of their respective OSD Component head, the Directors of Defense Agencies and DoD Field Activities:
a. Design and develop autonomous and semi-autonomous weapon systems that allow commanders and operators to exercise appropriate levels of human judgment over the use of force. This will include developing and implementing: (1) Employment concepts, doctrine, experimentation strategies, TTPs, training, and logistics support. (2) V&V, anti-tamper mechanisms, physical hardware, and software system safety in accordance with Military Standard 882E. (3) Cyber survivability, operational resilience, and cybersecurity in accordance with DoDI 8500.01. (4) Appropriate developmental and operational T&E, regardless of acquisition pathway, the joint/non-joint nature of those system's missions, or the lack of a survivability Key Performance Parameter for those systems.
b. For the systems in Paragraph 2.9.a: (1) Design autonomous and semi-autonomous weapon systems to minimize the probability and consequences of failures. (2) Perform rigorous and realistic developmental and operational T&E and V&V, including T&E of any changes to the system following IOT&E, in accordance with Paragraph 1.2.a.(1) and Section 3. (3) In coordination with the USD(R&E) and DOT&E, provide for monitoring to identify and address when changes to the system design or operational environment require additional T&E to provide sufficient confidence that the system will continue to avoid unintended engagements and resist interference by unauthorized parties. (4) For systems incorporating AI capabilities, design the system to utilize robust AI, in accordance with the DoD Responsible AI Strategy and Implementation Pathway, so that the system is resilient in real-world settings and against adversarial attacks and spoofing. (5) Design system safety, anti-tamper mechanisms, cyber survivability, operational resilience, and cybersecurity capabilities in accordance with Paragraph 1.2.a.(2) of this directive, DoDI 5000.83, the Joint Capabilities Integration and Development System Manual, and DoDI 8500.01. (6) Design human-machine interfaces to be readily understandable to trained operators, with clear procedures to activate and deactivate system functions, and to provide transparent feedback on system status in accordance with Paragraph 1.2.a.(3). (7) Certify that operators have been trained in system capabilities, doctrine, and TTPs to exercise appropriate levels of human judgment over the use of force and employ systems with appropriate care in accordance with the law of war, applicable treaties, weapon system safety rules, and ROE that are applicable or reasonably expected to be applicable. (8) Establish and periodically review training, TTPs, and doctrine to ensure operators and commanders understand the functioning, capabilities, and limitations of a system's autonomy under realistic operational conditions, including as a result of possible adversary actions.
c. Ensure that legal reviews of the intended acquisition, procurement, or modification of autonomous and semi-autonomous weapon systems are conducted in accordance with DoDD 5000.01, DoDD 2311.01, and, where applicable, DoDD 3000.03E. Legal reviews must address consistency with all applicable domestic and international law and, in particular, the law of war.
d. Consider for support only those autonomous and semi-autonomous weapon systems that are technically feasible, consistent with applicable law, and consistent with the standards in this directive.
e. In accordance with Paragraphs 1.2.c and 1.2.d, submit any autonomous weapon system for which approval is required to the USD(P), USD(A&S) or USD(R&E), and the VCJCS before a decision to enter formal development, and again before fielding of any such system.
#### 2.10. CJCS
The CJCS: a. Develops and implements joint employment concepts, doctrine, experimentation strategies, TTPs, training, and logistics support for autonomous and semi-autonomous weapon systems. b. Assesses military requirements for autonomous and semi-autonomous weapon systems, including applicable Key Performance Parameters and key system attributes. c. Develops and publishes joint doctrine, policy, and other guidance as appropriate to incorporate emerging capabilities of autonomous and semi-autonomous weapon systems into joint and combined operations, in accordance with this directive.
#### 2.11. VCJCS
In coordination with the USD(P) and USD(A&S) or USD(R&E), the VCJCS reviews and considers for approval autonomous weapon systems submitted in accordance with Paragraph 1.2.c.
#### 2.12. Combatant Commanders
The Combatant Commanders: a. Use autonomous and semi-autonomous weapon systems in accordance with this directive and in a manner consistent with their design, testing, certification, operator training, doctrine, TTPs, and approval as autonomous or semi-autonomous weapon systems. b. Employ autonomous and semi-autonomous weapon systems with appropriate care and in accordance with the law of war, applicable treaties, weapon system safety rules, and applicable ROE, in accordance with Paragraph 1.2.b, and employ AI capabilities in autonomous and semi-autonomous weapon systems consistent with the DoD AI Ethical Principles and the DoD Responsible Artificial Intelligence Strategy and Implementation Pathway, in accordance with Paragraph 1.2.f. c. Ensure that autonomous and semi-autonomous weapon systems are not employed or modified to operate in a manner that falls outside the policies in Paragraphs 1.2.d.(1) through 1.2.d.(4) without specific approval in accordance with Paragraph 1.2.c. d. Integrate autonomous and semi-autonomous weapon systems into operational mission planning as appropriate. e. Through the CJCS, identify warfighter priorities and operational needs that may be met by autonomous and semi-autonomous weapon systems.
Section 3: Verification and Validation and Testing and Evaluation of Autonomous and Semi-Autonomous Weapon Systems
Regardless of the acquisition pathway or OSD T&E oversight status for a given weapon system, to ensure autonomous and semi-autonomous weapon systems function as anticipated in realistic operational environments against adaptive adversaries and are sufficiently robust to minimize failures:
a. Systems will go through rigorous hardware and software V&V and realistic system developmental and operational T&E, including analysis of unanticipated emergent behavior. (1) Hardware and software V&V will include iterative cyber T&E in accordance with DoDI 5000.89, to verify that the weapon system is resilient and survivable in contested cyberspace. (2) Systems incorporating AI capabilities will go through rigorous developmental and operational T&E to verify and validate that the AI is robust according to design requirements.
b. T&E of systems incorporating AI capabilities will include testing to confirm that their autonomy algorithms can be rapidly reprogrammed on new input data.
c. After IOT&E, as directed by the DOT&E, system data will be collected and any further changes to the system will undergo appropriate V&V and T&E to ensure that critical safety features have not been degraded. (1) System software will be tested using best-available DoD means and methods to validate that critical safety features have not been degraded. Automated testing tools, such as modeling and simulation, will be used whenever feasible. The testing will identify any new operating states and other relevant changes in the autonomous or semi-autonomous weapon system. (2) As directed by the DOT&E: (a) Each new or revised operating state will undergo appropriate and tailored additional T&E to characterize the system behavior in that new operating state. (b) Changes to the state transition matrix may require whole system follow-on operational T&E.
d. In coordination with the USD(R&E) and DOT&E, the owning Component will provide for monitoring to identify and address when changes to the system design or operational environment require additional T&E to provide sufficient confidence that the system will continue to avoid unintended engagements and resist interference by unauthorized parties.
Section 4: Guidelines for Review of Certain Autonomous Weapon Systems
4.1. Autonomous weapon systems intended to be used in a manner that falls outside the policies in Paragraphs 1.2.d.(1) through 1.2.d.(4) must be approved by the USD(P), USD(R&E), and VCJCS before formal development and by the USD(P), USD(A&S), and VCJCS before fielding. If the weapon system in question is to be developed and then fielded by DoD, it will need to undergo both reviews and receive approvals. A review is not needed if the weapon system is covered by a previous approval for formal development or fielding. Requests for senior review and approval should be submitted to USD(P), attention to the Director of the Emerging Capabilities Policy Office.
a. An autonomous weapon system that is a variant of an existing weapon system previously approved through this review will not be covered by previous approval if changes to the system algorithms, intended mission set, intended operational environments, intended target sets, or expected adversarial countermeasures substantially differ from those applicable to the previously approved weapon system so as to fall outside the scope of what was previously approved in the senior review. Such systems will require a new senior review before their formal development and again before fielding.
b. An autonomous weapon system that is a modification of an existing weapon system not previously approved through this review requires the senior review described in Paragraph 1.2.c unless it is intended to be used in a manner that falls within the policies in Paragraphs 1.2.d.(1) through 1.2.d.(4).
c. Before a decision to enter formal development, the USD(P), USD(R&E), and VCJCS will verify that: (1) The system design incorporates the necessary capabilities to allow commanders and operators to exercise appropriate levels of human judgment over the use of force in the envisioned planning and employment processes for the weapon. (2) The system is designed to complete engagements within a timeframe and geographic area, as well as other applicable environmental and operational parameters, consistent with commander and operator intentions. If unable to do so, the system will terminate engagements or obtain additional operator input before continuing the engagement. (3) The combination of the system's design and concept of employment (e.g., its target selection and engagement logic and other relevant processes or measures) accounts for risks to non-targets, consistent with commander and operator intent. (4) The system design, including system safety, anti-tamper mechanisms, and cybersecurity in accordance with DoDI 8500.01, addresses and minimizes the probability and consequences of failures. (5) Plans are in place for V&V and T&E to establish system reliability, effectiveness, and suitability under realistic conditions, including possible adversary actions, to a sufficient standard consistent with the potential consequences of an unintended engagement or unauthorized parties interfering with the operation of the system. (6) For systems incorporating AI capabilities, plans are in place to ensure consistency with the DoD AI Ethical Principles and the DoD Responsible AI Strategy and Implementation Pathway. (7) A preliminary legal review of the weapon system has been completed in coordination with the GC DoD and in accordance with DoDD 5000.01, DoDD 2311.01 and, where applicable, DoDD 3000.03E.
d. Before fielding, the USD(P), USD(A&S), and VCJCS will verify that: (1) System capabilities, human-machine interfaces, doctrine, TTPs, and training have been demonstrated to allow commanders and operators to exercise appropriate levels of human judgment over the use of force and to employ systems with appropriate care and in accordance with the law of war, applicable treaties, weapon system safety rules, and ROE that are applicable or reasonably expected to be applicable. (2) System safety, anti-tamper mechanisms, cyber survivability, operational resilience, and cybersecurity capabilities have been implemented in accordance with DoDI 5000.83, the Joint Capabilities Integration and Development System Manual, and DoDI 8500.01 to minimize the probability and consequences of failures. A monitoring regime is in place to identify and address changes in operational environment, data inputs, and use that could contribute to such failures. (3) V&V and T&E: (a) Assess system performance, capability, reliability, effectiveness, and suitability under realistic conditions, including possible adversary actions, consistent with the potential consequences of unintended engagement or unauthorized parties interfering with the operation of the system. (b) Have demonstrated that the system can be reprogrammed with sufficient rapidity to enable timely correction of any unintended system behaviors that may be observed or discovered during future system operations. (4) Adequate training, TTPs, and doctrine are available, periodically reviewed, and used by system operators and commanders to understand the functioning, capabilities, and limitations of the system's autonomy in realistic operational conditions. (5) System design and human-machine interfaces are readily understandable to trained operators, provide transparent feedback on system status, and provide clear procedures for trained operators to activate and deactivate system functions. (6) For systems incorporating AI capabilities, the deployment and use of the AI capabilities in the weapon system will be consistent with the DoD AI Ethical Principles and the DoD Responsible AI Strategy and Implementation Pathway. (7) A legal review of the weapon system has been completed, in coordination with the GC DoD, and in accordance with DoDD 5000.01, DoDD 2311.01, and, where applicable, DoDD 3000.03E.
4.2. In cases of urgent military need, the USD(P), USD(A&S), USD(R&E), or VCJCS may request a Deputy Secretary of Defense waiver of the requirements in this section and Paragraph 1.2.c.
Section 5: Autonomous Weapon System Working Group
#### 5.1. General
The Autonomous Weapon System Working Group will: a. Support the USD(P), the USD(R&E), and the VCJCS in considering the full range of relevant DoD interests during the review of autonomous weapon systems before formal development. b. Support the USD(P), the USD(A&S), and the VCJCS in considering the full range of relevant DoD interests during the review of autonomous weapon systems before fielding. c. When requested by appropriate representatives of the Secretaries of the Military Departments; the Commander, USSOCOM; or, when applicable, a Director of a Defense Agency or a DoD Field Activity: (1) Advise whether a given weapon system requires senior-level approval in accordance with this directive. (2) Help identify and advise on addressing potential issues presented by a given weapon system during a potential senior-level review in accordance with this directive.
#### 5.2. Membership
In addition to representatives of the USD(P), the Autonomous Weapon System Working Group will consist of representatives of each of the following officials listed below. All members of the working group will be full time Federal Government employees, permanent part-time Federal Government employees, or Service members on active duty. The parent organizations for the representatives will be responsible for any expenses, to include travel related expenses, associated with participation in the working group: a. USD(A&S). b. USD(R&E). c. GC DoD. d. CDAO. e. DOT&E. f. CJCS representatives from: (1) Director for Strategy, Plans and Policy (Joint Staff J5). (2) Director, Command, Control, Communications and Computers/Cyber, Chief Information Officer (Joint Staff J6). (3) Director for Force Structure, Resources and Assessment (Joint Staff J8). (4) Legal Counsel to the Chairman of the Joint Chiefs of Staff.
Glossary
#### G.1. Acronyms
AI — artificial intelligence CJCS — Chairman of the Joint Chiefs of Staff CDAO — Chief Digital and Artificial Intelligence Officer DoDD — DoD directive DoDI — DoD instruction DOT&E — Director of Operational Test and Evaluation GC DoD — General Counsel of the Department of Defense IOT&E — initial operational test and evaluation ROE — rules of engagement T&E — test and evaluation TTPs — tactics, techniques, and procedures USD(A&S) — Under Secretary of Defense for Acquisition and Sustainment USD(P) — Under Secretary of Defense for Policy USD(R&E) — Under Secretary of Defense for Research and Engineering USSOCOM — United States Special Operations Command VCJCS — Vice Chairman of the Joint Chiefs of Staff V&V — verification and validation
#### G.2. Definitions
autonomous weapon system — A weapon system that, once activated, can select and engage targets without further intervention by an operator. This includes, but is not limited to, operator-supervised autonomous weapon systems that are designed to allow operators to override operation of the weapon system, but can select and engage targets without further operator input after activation.
failure — An actual or perceived degradation or loss of intended functionality or inability of the system to perform as intended or designed. Failure can result from a number of causes, including, but not limited to, human error, faulty human-machine interaction, malfunctions, communications degradation, software coding errors, enemy cyber-attacks or infiltration into the industrial supply chain, jamming, spoofing, decoys, other enemy countermeasures or actions, or unanticipated situations on the battlefield. For the purposes of this issuance, minimizing the probability and consequences of failure means reducing the probability and consequences of unintended engagements to acceptable levels while meeting mission objectives and does not mean achieving the lowest possible level of risk by never engaging targets.
fielding — Making a weapon system available for, or placing it into, operational use (rather than testing, exercises, or experiments), regardless of the acquisition approach employed for the weapon system, including major defense acquisition programs, middle tier acquisitions, or prototyping efforts such as joint concept technology demonstrations.
formal development — Begins at "Milestone B," as described in Paragraph 3.10 of DoDI 5000.85, in the case of major defense acquisition programs. For cases other than major defense acquisition programs, begins after the preliminary design review that correlates with the end of the technology maturation and risk reduction phase.
materiel — Defined in the DoD Dictionary of Military and Associated Terms.
operator-supervised autonomous weapon system — An autonomous weapon system that is designed to provide operators with the ability to intervene and terminate engagements, including in the event of a weapon system failure, before unacceptable levels of damage occur.
operating state — A variable or vector reflecting the status of the system.
operator — A person who operates a platform or weapon system.
remotely operated platform — An air, land, surface, subsurface, or space platform that is actively controlled by an operator who is not physically on the platform.
robust AI — Defined in the DoD Responsible Artificial Intelligence Strategy and Implementation Pathway.
semi-autonomous weapon system — A weapon system that, once activated, is intended to only engage individual targets or specific target groups that have been selected by an operator. This includes: Weapon systems that employ autonomy for engagement-related functions including, but not limited to, acquiring, tracking, and identifying potential targets; cuing potential targets to operators; prioritizing selected targets; timing of when to fire; or providing terminal guidance to home in on selected targets, provided that operator control is retained over the decision to select individual targets and specific target groups for engagement. "Fire and forget" or lock-on-after-launch homing munitions that rely on TTPs to maximize the probability that the only targets within the seeker's acquisition basket when the seeker activates are those individual targets or specific target groups that have been selected by an operator.
specific target group — A discrete group of potential targets, such as a particular flight of enemy aircraft, a particular formation of enemy tanks, or a particular flotilla of enemy vessels. A general class of targets or a specific type of target, such as a particular model of tank or aircraft, does not constitute a specific target group.
state transition matrix — A matrix that characterizes the ability of a system to transition from one operating state to another.
target selection — The identification of an individual target or a specific group of targets for engagement.
unintended engagement — The use of force against persons or objects that commanders or operators did not intend to be the targets of U.S. military operations, including unacceptable levels of collateral damage beyond those consistent with the law of war, ROE, and commander's intent.
weapon system — Defined in the DoD Dictionary of Military and Associated Terms.
weapon system safety rules — Guidance for personnel, issued by competent authority, focused on addressing weapon safety issues and concerns that present significant mishap risk and is applied with a view towards ensuring freedom from conditions that can cause occupational illness, unintended death or injury, unintended damage to or loss of equipment or property, or unintended damage to the environment.
Artificial intelligence (AI) technologies have significant potential to transform society and people’s lives – from commerce and health to transportation and cybersecurity to the environment and our planet. AI technologies can drive inclusive economic growth and support scientific advancements that improve the conditions of our world. AI technologies, however, also pose risks that can negatively impact individuals, groups, organizations, communities, society, the environment, and the planet. Like risks for other types of technology, AI risks can emerge in a variety of ways and can be characterized as long- or short-term, high- or low-probability, systemic or localized, and high- or low-impact.
The AI RMF refers to an AI system as an engineered or machine-based system that can, for a given set of objectives, generate outputs such as predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy (Adapted from: OECD Recommendation on AI:2019; ISO/IEC 22989:2022).
While there are myriad standards and best practices to help organizations mitigate the risks of traditional software or information-based systems, the risks posed by AI systems are in many ways unique (See Appendix B). AI systems, for example, may be trained on data that can change over time, sometimes significantly and unexpectedly, affecting system functionality and trustworthiness in ways that are hard to understand. AI systems and the contexts in which they are deployed are frequently complex, making it difficult to detect and respond to failures when they occur. AI systems are inherently socio-technical in nature, meaning they are influenced by societal dynamics and human behavior. AI risks – and benefits – can emerge from the interplay of technical aspects combined with societal factors related to how a system is used, its interactions with other AI systems, who operates it, and the social context in which it is deployed.
These risks make AI a uniquely challenging technology to deploy and utilize both for organizations and within society. Without proper controls, AI systems can amplify, perpetuate, or exacerbate inequitable or undesirable outcomes for individuals and communities. With proper controls, AI systems can mitigate and manage inequitable outcomes.
AI risk management is a key component of responsible development and use of AI systems. Responsible AI practices can help align the decisions about AI system design, development, and uses with intended aim and values. Core concepts in responsible AI emphasize human centricity, social responsibility, and sustainability. AI risk management can drive responsible uses and practices by prompting organizations and their internal teams who design, develop, and deploy AI to think more critically about context and potential or unexpected negative and positive impacts. Understanding and managing the risks of AI systems will help to enhance trustworthiness, and in turn, cultivate public trust.
Social responsibility can refer to the organization’s responsibility “for the impacts of its decisions and activities on society and the environment through transparent and ethical behavior” (ISO 26000:2010). Sustainability refers to the “state of the global system, including environmental, social, and economic aspects, in which the needs of the present are met without compromising the ability of future generations to meet their own needs” (ISO/IEC TR 24368:2022). Responsible AI is meant to result in technology that is also equitable and accountable. The expectation is that organizational practices are carried out in accord with “professional responsibility,” defined by ISO as an approach that “aims to ensure that professionals who design, develop, or deploy AI systems and applications or AI-based products or systems, recognize their unique position to exert influence on people, society, and the future of AI” (ISO/IEC TR 24368:2022).
As directed by the National Artificial Intelligence Initiative Act of 2020 (P.L. 116-283), the goal of the AI RMF is to offer a resource to the organizations designing, developing, deploying, or using AI systems to help manage the many risks of AI and promote trustworthy and responsible development and use of AI systems. The Framework is intended to be voluntary, rights-preserving, non-sector-specific, and use-case agnostic, providing flexibility to organizations of all sizes and in all sectors and throughout society to implement the approaches in the Framework.
The Framework is designed to equip organizations and individuals – referred to here as AI actors – with approaches that increase the trustworthiness of AI systems, and to help foster the responsible design, development, deployment, and use of AI systems over time. AI actors are defined by the Organisation for Economic Co-operation and Development (OECD) as “those who play an active role in the AI system lifecycle, including organizations and individuals that deploy or operate AI” [OECD (2019) Artificial Intelligence in Society—OECD iLibrary] (See Appendix A).
The AI RMF is intended to be practical, to adapt to the AI landscape as AI technologies continue to develop, and to be operationalized by organizations in varying degrees and capacities so society can benefit from AI while also being protected from its potential harms.
The Framework and supporting resources will be updated, expanded, and improved based on evolving technology, the standards landscape around the world, and AI community experience and feedback. NIST will continue to align the AI RMF and related guidance with applicable international standards, guidelines, and practices. As the AI RMF is put into use, additional lessons will be learned to inform future updates and additional resources.
The Framework is divided into two parts. Part 1 discusses how organizations can frame the risks related to AI and describes the intended audience. Next, AI risks and trustworthiness are analyzed, outlining the characteristics of trustworthy AI systems, which include valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy enhanced, and fair with their harmful biases managed.
Part 2 comprises the “Core” of the Framework. It describes four specific functions to help organizations address the risks of AI systems in practice. These functions – GOVERN, MAP, MEASURE, and MANAGE – are broken down further into categories and subcategories. While GOVERN applies to all stages of organizations’ AI risk management processes and procedures, the MAP, MEASURE, and MANAGE functions can be applied in AI system-specific contexts and at specific stages of the AI lifecycle.
Additional resources related to the Framework are included in the AI RMF Playbook, which is available via the NIST AI RMF website: https://www.nist.gov/itl/ai-risk-management-framework.
Development of the AI RMF by NIST in collaboration with the private and public sectors is directed and consistent with its broader AI efforts called for by the National AI Initiative Act of 2020, the National Security Commission on Artificial Intelligence recommendations, and the Plan for Federal Engagement in Developing Technical Standards and Related Tools. Engagement with the AI community during this Framework’s development – via responses to a formal Request for Information, three widely attended workshops, public comments on a concept paper and two drafts of the Framework, discussions at multiple public forums, and many small group meetings – has informed development of the AI RMF 1.0 as well as AI research and development and evaluation conducted by NIST and others. Priority research and additional guidance that will enhance this Framework will be captured in an associated AI Risk Management Framework Roadmap to which NIST and the broader community can contribute.
3. AI Risks and Trustworthiness
For AI systems to be trustworthy, they often need to be responsive to a multiplicity of criteria that are of value to interested parties. Approaches which enhance AI trustworthiness can reduce negative AI risks. This Framework articulates the following characteristics of trustworthy AI and offers guidance for addressing them. Characteristics of trustworthy AI systems include: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. Creating trustworthy AI requires balancing each of these characteristics based on the AI system’s context of use. While all characteristics are socio-technical system attributes, accountability and transparency also relate to the processes and activities internal to an AI system and its external setting. Neglecting these characteristics can increase the probability and magnitude of negative consequences.
Trustworthiness characteristics (shown in Figure 4) are inextricably tied to social and organizational behavior, the datasets used by AI systems, selection of AI models and algorithms and the decisions made by those who build them, and the interactions with the humans who provide insight from and oversight of such systems. Human judgment should be employed when deciding on the specific metrics related to AI trustworthiness characteristics and the precise threshold values for those metrics.
Addressing AI trustworthiness characteristics individually will not ensure AI system trustworthiness; tradeoffs are usually involved, rarely do all characteristics apply in every setting, and some will be more or less important in any given situation. Ultimately, trustworthiness is a social concept that ranges across a spectrum and is only as strong as its weakest characteristics.
When managing AI risks, organizations can face difficult decisions in balancing these characteristics. For example, in certain scenarios tradeoffs may emerge between optimizing for interpretability and achieving privacy. In other cases, organizations might face a tradeoff between predictive accuracy and interpretability. Or, under certain conditions such as data sparsity, privacy-enhancing techniques can result in a loss in accuracy, affecting decisions about fairness and other values in certain domains. Dealing with tradeoffs requires taking into account the decision-making context. These analyses can highlight the existence and extent of tradeoffs between different measures, but they do not answer questions about how to navigate the tradeoff. Those depend on the values at play in the relevant context and should be resolved in a manner that is both transparent and appropriately justifiable.
There are multiple approaches for enhancing contextual awareness in the AI lifecycle. For example, subject matter experts can assist in the evaluation of TEVV findings and work with product and deployment teams to align TEVV parameters to requirements and deployment conditions. When properly resourced, increasing the breadth and diversity of input from interested parties and relevant AI actors throughout the AI lifecycle can enhance opportunities for informing contextually sensitive evaluations, and for identifying AI system benefits and positive impacts. These practices can increase the likelihood that risks arising in social contexts are managed appropriately.
Understanding and treatment of trustworthiness characteristics depends on an AI actor’s particular role within the AI lifecycle. For any given AI system, an AI designer or developer may have a different perception of the characteristics than the deployer.
Trustworthiness characteristics explained in this document influence each other. Highly secure but unfair systems, accurate but opaque and uninterpretable systems, and inaccurate but secure, privacy-enhanced, and transparent systems are all undesirable. A comprehensive approach to risk management calls for balancing tradeoffs among the trustworthiness characteristics. It is the joint responsibility of all AI actors to determine whether AI technology is an appropriate or necessary tool for a given context or purpose, and how to use it responsibly. The decision to commission or deploy an AI system should be based on a contextual assessment of trustworthiness characteristics and the relative risks, impacts, costs, and benefits, and informed by a broad set of interested parties.
3.1 Valid and Reliable
Validation is the “confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled” (Source: ISO 9000:2015). Deployment of AI systems which are inaccurate, unreliable, or poorly generalized to data and settings beyond their training creates and increases negative AI risks and reduces trustworthiness.
Reliability is defined in the same standard as the “ability of an item to perform as required, without failure, for a given time interval, under given conditions” (Source: ISO/IEC TS 5723:2022). Reliability is a goal for overall correctness of AI system operation under the conditions of expected use and over a given period of time, including the entire lifetime of the system.
Accuracy and robustness contribute to the validity and trustworthiness of AI systems, and can be in tension with one another in AI systems.
Accuracy is defined by ISO/IEC TS 5723:2022 as “closeness of results of observations, computations, or estimates to the true values or the values accepted as being true.” Measures of accuracy should consider computational-centric measures (e.g., false positive and false negative rates), human-AI teaming, and demonstrate external validity (generalizable beyond the training conditions). Accuracy measurements should always be paired with clearly defined and realistic test sets – that are representative of conditions of expected use – and details about test methodology; these should be included in associated documentation. Accuracy measurements may include disaggregation of results for different data segments.
Robustness or generalizability is defined as the “ability of a system to maintain its level of performance under a variety of circumstances” (Source: ISO/IEC TS 5723:2022). Robustness is a goal for appropriate system functionality in a broad set of conditions and circumstances, including uses of AI systems not initially anticipated. Robustness requires not only that the system perform exactly as it does under expected uses, but also that it should perform in ways that minimize potential harms to people if it is operating in an unexpected setting.
Validity and reliability for deployed AI systems are often assessed by ongoing testing or monitoring that confirms a system is performing as intended. Measurement of validity, accuracy, robustness, and reliability contribute to trustworthiness and should take into consideration that certain types of failures can cause greater harm. AI risk management efforts should prioritize the minimization of potential negative impacts, and may need to include human intervention in cases where the AI system cannot detect or correct errors.
3.2 Safe
AI systems should “not under defined conditions, lead to a state in which human life, health, property, or the environment is endangered” (Source: ISO/IEC TS 5723:2022). Safe operation of AI systems is improved through:
responsible design, development, and deployment practices;
clear information to deployers on responsible use of the system;
responsible decision-making by deployers and end users; and
explanations and documentation of risks based on empirical evidence of incidents.
Different types of safety risks may require tailored AI risk management approaches based on context and the severity of potential risks presented. Safety risks that pose a potential risk of serious injury or death call for the most urgent prioritization and most thorough risk management process.
Employing safety considerations during the lifecycle and starting as early as possible with planning and design can prevent failures or conditions that can render a system dangerous. Other practical approaches for AI safety often relate to rigorous simulation and in-domain testing, real-time monitoring, and the ability to shut down, modify, or have human intervention into systems that deviate from intended or expected functionality.
AI safety risk management approaches should take cues from efforts and guidelines for safety in fields such as transportation and healthcare, and align with existing sector- or application-specific guidelines or standards.
3.3 Secure and Resilient
AI systems, as well as the ecosystems in which they are deployed, may be said to be resilient if they can withstand unexpected adverse events or unexpected changes in their environment or use – or if they can maintain their functions and structure in the face of internal and external change and degrade safely and gracefully when this is necessary (Adapted from: ISO/IEC TS 5723:2022). Common security concerns relate to adversarial examples, data poisoning, and the exfiltration of models, training data, or other intellectual property through AI system endpoints. AI systems that can maintain confidentiality, integrity, and availability through protection mechanisms that prevent unauthorized access and use may be said to be secure. Guidelines in the NIST Cybersecurity Framework and Risk Management Framework are among those which are applicable here.
Security and resilience are related but distinct characteristics. While resilience is the ability to return to normal function after an unexpected adverse event, security includes resilience but also encompasses protocols to avoid, protect against, respond to, or recover from attacks. Resilience relates to robustness and goes beyond the provenance of the data to encompass unexpected or adversarial use (or abuse or misuse) of the model or data.
3.4 Accountable and Transparent
Trustworthy AI depends upon accountability. Accountability presupposes transparency. Transparency reflects the extent to which information about an AI system and its outputs is available to individuals interacting with such a system – regardless of whether they are even aware that they are doing so. Meaningful transparency provides access to appropriate levels of information based on the stage of the AI lifecycle and tailored to the role or knowledge of AI actors or individuals interacting with or using the AI system. By promoting higher levels of understanding, transparency increases confidence in the AI system.
This characteristic’s scope spans from design decisions and training data to model training, the structure of the model, its intended use cases, and how and when deployment, post-deployment, or end user decisions were made and by whom. Transparency is often necessary for actionable redress related to AI system outputs that are incorrect or otherwise lead to negative impacts. Transparency should consider human-AI interaction: for example, how a human operator or user is notified when a potential or actual adverse outcome caused by an AI system is detected. A transparent system is not necessarily an accurate, privacy-enhanced, secure, or fair system. However, it is difficult to determine whether an opaque system possesses such characteristics, and to do so over time as complex systems evolve.
The role of AI actors should be considered when seeking accountability for the outcomes of AI systems. The relationship between risk and accountability associated with AI and technological systems more broadly differs across cultural, legal, sectoral, and societal contexts. When consequences are severe, such as when life and liberty are at stake, AI developers and deployers should consider proportionally and proactively adjusting their transparency and accountability practices. Maintaining organizational practices and governing structures for harm reduction, like risk management, can help lead to more accountable systems.
Measures to enhance transparency and accountability should also consider the impact of these efforts on the implementing entity, including the level of necessary resources and the need to safeguard proprietary information.
Maintaining the provenance of training data and supporting attribution of the AI system’s decisions to subsets of training data can assist with both transparency and accountability. Training data may also be subject to copyright and should follow applicable intellectual property rights laws.
As transparency tools for AI systems and related documentation continue to evolve, developers of AI systems are encouraged to test different types of transparency tools in cooperation with AI deployers to ensure that AI systems are used as intended.
3.5 Explainable and Interpretable
Explainability refers to a representation of the mechanisms underlying AI systems’ operation, whereas interpretability refers to the meaning of AI systems’ output in the context of their designed functional purposes. Together, explainability and interpretability assist those operating or overseeing an AI system, as well as users of an AI system, to gain deeper insights into the functionality and trustworthiness of the system, including its outputs. The underlying assumption is that perceptions of negative risk stem from a lack of ability to make sense of, or contextualize, system output appropriately. Explainable and interpretable AI systems offer information that will help end users understand the purposes and potential impact of an AI system.
Risk from lack of explainability may be managed by describing how AI systems function, with descriptions tailored to individual differences such as the user’s role, knowledge, and skill level. Explainable systems can be debugged and monitored more easily, and they lend themselves to more thorough documentation, audit, and governance.
Risks to interpretability often can be addressed by communicating a description of why an AI system made a particular prediction or recommendation. (See “Four Principles of Explainable Artificial Intelligence” and “Psychological Foundations of Explainability and Interpretability in Artificial Intelligence” found here.)
Transparency, explainability, and interpretability are distinct characteristics that support each other. Transparency can answer the question of “what happened” in the system. Explainability can answer the question of “how” a decision was made in the system. Interpretability can answer the question of “why” a decision was made by the system and its meaning or context to the user.
3.6 Privacy-Enhanced
Privacy refers generally to the norms and practices that help to safeguard human autonomy, identity, and dignity. These norms and practices typically address freedom from intrusion, limiting observation, or individuals’ agency to consent to disclosure or control of facets of their identities (e.g., body, data, reputation). (See The NIST Privacy Framework: A Tool for Improving Privacy through Enterprise Risk Management.)
Privacy values such as anonymity, confidentiality, and control generally should guide choices for AI system design, development, and deployment. Privacy-related risks may influence security, bias, and transparency and come with tradeoffs with these other characteristics. Like safety and security, specific technical features of an AI system may promote or reduce privacy. AI systems can also present new risks to privacy by allowing inference to identify individuals or previously private information about individuals.
Privacy-enhancing technologies (“PETs”) for AI, as well as data minimizing methods such as de-identification and aggregation for certain model outputs, can support design for privacy-enhanced AI systems. Under certain conditions such as data sparsity, privacy-enhancing techniques can result in a loss in accuracy, affecting decisions about fairness and other values in certain domains.
3.7 Fair – with Harmful Bias Managed
Fairness in AI includes concerns for equality and equity by addressing issues such as harmful bias and discrimination. Standards of fairness can be complex and difficult to define because perceptions of fairness differ among cultures and may shift depending on application. Organizations’ risk management efforts will be enhanced by recognizing and considering these differences. Systems in which harmful biases are mitigated are not necessarily fair. For example, systems in which predictions are somewhat balanced across demographic groups may still be inaccessible to individuals with disabilities or affected by the digital divide or may exacerbate existing disparities or systemic biases.
Bias is broader than demographic balance and data representativeness. NIST has identified three major categories of AI bias to be considered and managed: systemic, computational and statistical, and human-cognitive. Each of these can occur in the absence of prejudice, partiality, or discriminatory intent. Systemic bias can be present in AI datasets, the organizational norms, practices, and processes across the AI lifecycle, and the broader society that uses AI systems. Computational and statistical biases can be present in AI datasets and algorithmic processes, and often stem from systematic errors due to non-representative samples. Human-cognitive biases relate to how an individual or group perceives AI system information to make a decision or fill in missing information, or how humans think about purposes and functions of an AI system. Human-cognitive biases are omnipresent in decision-making processes across the AI lifecycle and system use, including the design, implementation, operation, and maintenance of AI.
Bias exists in many forms and can become ingrained in the automated systems that help make decisions about our lives. While bias is not always a negative phenomenon, AI systems can potentially increase the speed and scale of biases and perpetuate and amplify harms to individuals, groups, communities, organizations, and society. Bias is tightly associated with the concepts of transparency as well as fairness in society. (For more information about bias, including the three categories, see NIST Special Publication 1270, Towards a Standard for Identifying and Managing Bias in Artificial Intelligence.)
Part 2: Core and Profiles
5. AI RMF Core
The AI RMF Core provides outcomes and actions that enable dialogue, understanding, and activities to manage AI risks and responsibly develop trustworthy AI systems. As illustrated in Figure 5, the Core is composed of four functions: GOVERN, MAP, MEASURE, and MANAGE. Each of these high-level functions is broken down into categories and subcategories. Categories and subcategories are subdivided into specific actions and outcomes. Actions do not constitute a checklist, nor are they necessarily an ordered set of steps.
Risk management should be continuous, timely, and performed throughout the AI system lifecycle dimensions. AI RMF Core functions should be carried out in a way that reflects diverse and multidisciplinary perspectives, potentially including the views of AI actors outside the organization. Having a diverse team contributes to more open sharing of ideas and assumptions about purposes and functions of the technology being designed, developed, deployed, or evaluated – which can create opportunities to surface problems and identify existing and emergent risks.
An online companion resource to the AI RMF, the NIST AI RMF Playbook, is available to help organizations navigate the AI RMF and achieve its outcomes through suggested tactical actions they can apply within their own contexts. Like the AI RMF, the Playbook is voluntary and organizations can utilize the suggestions according to their needs and interests. Playbook users can create tailored guidance selected from suggested material for their own use and contribute their suggestions for sharing with the broader community. Along with the AI RMF, the Playbook is part of the NIST Trustworthy and Responsible AI Resource Center.
Framework users may apply these functions as best suits their needs for managing AI risks based on their resources and capabilities. Some organizations may choose to select from among the categories and subcategories; others may choose and have the capacity to apply all categories and subcategories. Assuming a governance structure is in place, functions may be performed in any order across the AI lifecycle as deemed to add value by a user of the framework. After instituting the outcomes in GOVERN, most users of the AI RMF would start with the MAP function and continue to MEASURE or MANAGE. However users integrate the functions, the process should be iterative, with cross-referencing between functions as necessary. Similarly, there are categories and subcategories with elements that apply to multiple functions, or that logically should take place before certain subcategory decisions.
5.1 Govern
The GOVERN function:
cultivates and implements a culture of risk management within organizations designing, developing, deploying, evaluating, or acquiring AI systems;
outlines processes, documents, and organizational schemes that anticipate, identify, and manage the risks a system can pose, including to users and others across society – and procedures to achieve those outcomes;
incorporates processes to assess potential impacts;
provides a structure by which AI risk management functions can align with organizational principles, policies, and strategic priorities;
connects technical aspects of AI system design and development to organizational values and principles, and enables organizational practices and competencies for the individuals involved in acquiring, training, deploying, and monitoring such systems; and
addresses full product lifecycle and associated processes, including legal and other issues concerning use of third-party software or hardware systems and data.
GOVERN is a cross-cutting function that is infused throughout AI risk management and enables the other functions of the process. Aspects of GOVERN, especially those related to compliance or evaluation, should be integrated into each of the other functions. Attention to governance is a continual and intrinsic requirement for effective AI risk management over an AI system’s lifespan and the organization’s hierarchy.
Strong governance can drive and enhance internal practices and norms to facilitate organizational risk culture. Governing authorities can determine the overarching policies that direct an organization’s mission, goals, values, culture, and risk tolerance. Senior leadership sets the tone for risk management within an organization, and with it, organizational culture. Management aligns the technical aspects of AI risk management to policies and operations. Documentation can enhance transparency, improve human review processes, and bolster accountability in AI system teams.
After putting in place the structures, systems, processes, and teams described in the GOVERN function, organizations should benefit from a purpose-driven culture focused on risk understanding and management. It is incumbent on Framework users to continue to execute the GOVERN function as knowledge, cultures, and needs or expectations from AI actors evolve over time.
Practices related to governing AI risks are described in the NIST AI RMF Playbook. Table 1 lists the GOVERN function’s categories and subcategories.
| Category | Subcategory | |---|---| | GOVERN 1: Policies, processes, procedures, and practices across the organization related to the mapping, measuring, and managing of AI risks are in place, transparent, and implemented effectively. | GOVERN 1.1: Legal and regulatory requirements involving AI are understood, managed, and documented. | | | GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices. | | | GOVERN 1.3: Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization’s risk tolerance. | | | GOVERN 1.4: The risk management process and its outcomes are established through transparent policies, procedures, and other controls based on organizational risk priorities. | | | GOVERN 1.5: Ongoing monitoring and periodic review of the risk management process and its outcomes are planned and organizational roles and responsibilities clearly defined, including determining the frequency of periodic review. | | | GOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities. | | | GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization’s trustworthiness. | | GOVERN 2: Accountability structures are in place so that the appropriate teams and individuals are empowered, responsible, and trained for mapping, measuring, and managing AI risks. | GOVERN 2.1: Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization. | | | GOVERN 2.2: The organization’s personnel and partners receive AI risk management training to enable them to perform their duties and responsibilities consistent with related policies, procedures, and agreements. | | | GOVERN 2.3: Executive leadership of the organization takes responsibility for decisions about risks associated with AI system development and deployment. | | GOVERN 3: Workforce diversity, equity, inclusion, and accessibility processes are prioritized in the mapping, measuring, and managing of AI risks throughout the lifecycle. | GOVERN 3.1: Decision-making related to mapping, measuring, and managing AI risks throughout the lifecycle is informed by a diverse team (e.g., diversity of demographics, disciplines, experience, expertise, and backgrounds). | | | GOVERN 3.2: Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems. | | GOVERN 4: Organizational teams are committed to a culture that considers and communicates AI risk. | GOVERN 4.1: Organizational policies and practices are in place to foster a critical thinking and safety-first mindset in the design, development, deployment, and uses of AI systems to minimize potential negative impacts. | | | GOVERN 4.2: Organizational teams document the risks and potential impacts of the AI technology they design, develop, deploy, evaluate, and use, and they communicate about the impacts more broadly. | | | GOVERN 4.3: Organizational practices are in place to enable AI testing, identification of incidents, and information sharing. | | GOVERN 5: Processes are in place for robust engagement with relevant AI actors. | GOVERN 5.1: Organizational policies and practices are in place to collect, consider, prioritize, and integrate feedback from those external to the team that developed or deployed the AI system regarding the potential individual and societal impacts related to AI risks. | | | GOVERN 5.2: Mechanisms are established to enable the team that developed or deployed AI systems to regularly incorporate adjudicated feedback from relevant AI actors into system design and implementation. | | GOVERN 6: Policies and procedures are in place to address AI risks and benefits arising from third-party software and data and other supply chain issues. | GOVERN 6.1: Policies and procedures are in place that address AI risks associated with third-party entities, including risks of infringement of a third-party’s intellectual property or other rights. | | | GOVERN 6.2: Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk. |
5.2 Map
The MAP function establishes the context to frame risks related to an AI system. The AI lifecycle consists of many interdependent activities involving a diverse set of actors (See Figure 3). In practice, AI actors in charge of one part of the process often do not have full visibility or control over other parts and their associated contexts. The interdependencies between these activities, and among the relevant AI actors, can make it difficult to reliably anticipate impacts of AI systems. For example, early decisions in identifying purposes and objectives of an AI system can alter its behavior and capabilities, and the dynamics of deployment setting (such as end users or impacted individuals) can shape the impacts of AI system decisions. As a result, the best intentions within one dimension of the AI lifecycle can be undermined via interactions with decisions and conditions in other, later activities.
This complexity and varying levels of visibility can introduce uncertainty into risk management practices. Anticipating, assessing, and otherwise addressing potential sources of negative risk can mitigate this uncertainty and enhance the integrity of the decision process. The information gathered while carrying out the MAP function enables negative risk prevention and informs decisions for processes such as model management, as well as an initial decision about appropriateness or the need for an AI solution. Outcomes in the MAP function are the basis for the MEASURE and MANAGE functions. Without contextual knowledge, and awareness of risks within the identified contexts, risk management is difficult to perform. The MAP function is intended to enhance an organization’s ability to identify risks and broader contributing factors.
Implementation of this function is enhanced by incorporating perspectives from a diverse internal team and engagement with those external to the team that developed or deployed the AI system. Engagement with external collaborators, end users, potentially impacted communities, and others may vary based on the risk level of a particular AI system, the makeup of the internal team, and organizational policies. Gathering such broad perspectives can help organizations proactively prevent negative risks and develop more trustworthy AI systems by:
improving their capacity for understanding contexts;
checking their assumptions about context of use;
enabling recognition of when systems are not functional within or out of their intended context;
identifying positive and beneficial uses of their existing AI systems;
improving understanding of limitations in AI and ML processes;
identifying constraints in real-world applications that may lead to negative impacts;
identifying known and foreseeable negative impacts related to intended use of AI systems; and
anticipating risks of the use of AI systems beyond intended use.
After completing the MAP function, Framework users should have sufficient contextual knowledge about AI system impacts to inform an initial go/no-go decision about whether to design, develop, or deploy an AI system. If a decision is made to proceed, organizations should utilize the MEASURE and MANAGE functions along with policies and procedures put into place in the GOVERN function to assist in AI risk management efforts. It is incumbent on Framework users to continue applying the MAP function to AI systems as context, capabilities, risks, benefits, and potential impacts evolve over time.
Practices related to mapping AI risks are described in the NIST AI RMF Playbook. Table 2 lists the MAP function’s categories and subcategories.
| Category | Subcategory | |---|---| | MAP 1: Context is established and understood. | MAP 1.1: Intended purposes, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented. Considerations include: the specific set or types of users along with their expectations; potential positive and negative impacts of system uses to individuals, communities, organizations, society, and the planet; assumptions and related limitations about AI system purposes, uses, and risks across the development or product AI lifecycle; and related TEVV and system metrics. | | | MAP 1.2: Interdisciplinary AI actors, competencies, skills, and capacities for establishing context reflect demographic diversity and broad domain and user experience expertise, and their participation is documented. Opportunities for interdisciplinary collaboration are prioritized. | | | MAP 1.3: The organization’s mission and relevant goals for AI technology are understood and documented. | | | MAP 1.4: The business value or context of business use has been clearly defined or – in the case of assessing existing AI systems – re-evaluated. | | | MAP 1.5: Organizational risk tolerances are determined and documented. | | | MAP 1.6: System requirements (e.g., “the system shall respect the privacy of its users”) are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account to address AI risks. | | MAP 2: Categorization of the AI system is performed. | MAP 2.1: The specific tasks and methods used to implement the tasks that the AI system will support are defined (e.g., classifiers, generative models, recommenders). | | | MAP 2.2: Information about the AI system’s knowledge limits and how system output may be utilized and overseen by humans is documented. Documentation provides sufficient information to assist relevant AI actors when making decisions and taking subsequent actions. | | | MAP 2.3: Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection (e.g., availability, representativeness, suitability), system trustworthiness, and construct validation. | | MAP 3: AI capabilities, targeted usage, goals, and expected benefits and costs compared with appropriate benchmarks are understood. | MAP 3.1: Potential benefits of intended AI system functionality and performance are examined and documented. | | | MAP 3.2: Potential costs, including non-monetary costs, which result from expected or realized AI errors or system functionality and trustworthiness – as connected to organizational risk tolerance – are examined and documented. | | | MAP 3.3: Targeted application scope is specified and documented based on the system’s capability, established context, and AI system categorization. | | | MAP 3.4: Processes for operator and practitioner proficiency with AI system performance and trustworthiness – and relevant technical standards and certifications – are defined, assessed, and documented. | | | MAP 3.5: Processes for human oversight are defined, assessed, and documented in accordance with organizational policies from the GOVERN function. | | MAP 4: Risks and benefits are mapped for all components of the AI system including third-party software and data. | MAP 4.1: Approaches for mapping AI technology and legal risks of its components – including the use of third-party data or software – are in place, followed, and documented, as are risks of infringement of a third party’s intellectual property or other rights. | | | MAP 4.2: Internal risk controls for components of the AI system, including third-party AI technologies, are identified and documented. | | MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized. | MAP 5.1: Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or deployed the AI system, or other data are identified and documented. | | | MAP 5.2: Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback about positive, negative, and unanticipated impacts are in place and documented. |
5.3 Measure
The MEASURE function employs quantitative, qualitative, or mixed-method tools, techniques, and methodologies to analyze, assess, benchmark, and monitor AI risk and related impacts. It uses knowledge relevant to AI risks identified in the MAP function and informs the MANAGE function. AI systems should be tested before their deployment and regularly while in operation. AI risk measurements include documenting aspects of systems’ functionality and trustworthiness.
Measuring AI risks includes tracking metrics for trustworthy characteristics, social impact, and human-AI configurations. Processes developed or adopted in the MEASURE function should include rigorous software testing and performance assessment methodologies with associated measures of uncertainty, comparisons to performance benchmarks, and formalized reporting and documentation of results. Processes for independent review can improve the effectiveness of testing and can mitigate internal biases and potential conflicts of interest.
Where tradeoffs among the trustworthy characteristics arise, measurement provides a traceable basis to inform management decisions. Options may include recalibration, impact mitigation, or removal of the system from design, development, production, or use, as well as a range of compensating, detective, deterrent, directive, and recovery controls.
After completing the MEASURE function, objective, repeatable, or scalable test, evaluation, verification, and validation (TEVV) processes including metrics, methods, and methodologies are in place, followed, and documented. Metrics and measurement methodologies should adhere to scientific, legal, and ethical norms and be carried out in an open and transparent process. New types of measurement, qualitative and quantitative, may need to be developed. The degree to which each measurement type provides unique and meaningful information to the assessment of AI risks should be considered. Framework users will enhance their capacity to comprehensively evaluate system trustworthiness, identify and track existing and emergent risks, and verify efficacy of the metrics. Measurement outcomes will be utilized in the MANAGE function to assist risk monitoring and response efforts. It is incumbent on Framework users to continue applying the MEASURE function to AI systems as knowledge, methodologies, risks, and impacts evolve over time.
Practices related to measuring AI risks are described in the NIST AI RMF Playbook. Table 3 lists the MEASURE function’s categories and subcategories.
| Category | Subcategory | |---|---| | MEASURE 1: Appropriate methods and metrics are identified and applied. | MEASURE 1.1: Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for implementation starting with the most significant AI risks. The risks or trustworthiness characteristics that will not – or cannot – be measured are properly documented. | | | MEASURE 1.2: Appropriateness of AI metrics and effectiveness of existing controls are regularly assessed and updated, including reports of errors and potential impacts on affected communities. | | | MEASURE 1.3: Internal experts who did not serve as front-line developers for the system and/or independent assessors are involved in regular assessments and updates. Domain experts, users, AI actors external to the team that developed or deployed the AI system, and affected communities are consulted in support of assessments as necessary per organizational risk tolerance. | | MEASURE 2: AI systems are evaluated for trustworthy characteristics. | MEASURE 2.1: Test sets, metrics, and details about the tools used during TEVV are documented. | | | MEASURE 2.2: Evaluations involving human subjects meet applicable requirements (including human subject protection) and are representative of the relevant population. | | | MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s). Measures are documented. | | | MEASURE 2.4: The functionality and behavior of the AI system and its components – as identified in the MAP function – are monitored when in production. | | | MEASURE 2.5: The AI system to be deployed is demonstrated to be valid and reliable. Limitations of the generalizability beyond the conditions under which the technology was developed are documented. | | | MEASURE 2.6: The AI system is evaluated regularly for safety risks – as identified in the MAP function. The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, particularly if made to operate beyond its knowledge limits. Safety metrics reflect system reliability and robustness, real-time monitoring, and response times for AI system failures. | | | MEASURE 2.7: AI system security and resilience – as identified in the MAP function – are evaluated and documented. | | | MEASURE 2.8: Risks associated with transparency and accountability – as identified in the MAP function – are examined and documented. | | | MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context – as identified in the MAP function – to inform responsible use and governance. | | | MEASURE 2.10: Privacy risk of the AI system – as identified in the MAP function – is examined and documented. | | | MEASURE 2.11: Fairness and bias – as identified in the MAP function – are evaluated and results are documented. | | | MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities – as identified in the MAP function – are assessed and documented. | | | MEASURE 2.13: Effectiveness of the employed TEVV metrics and processes in the MEASURE function are evaluated and documented. | | MEASURE 3: Mechanisms for tracking identified AI risks over time are in place. | MEASURE 3.1: Approaches, personnel, and documentation are in place to regularly identify and track existing, unanticipated, and emergent AI risks based on factors such as intended and actual performance in deployed contexts. | | | MEASURE 3.2: Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently available measurement techniques or where metrics are not yet available. | | | MEASURE 3.3: Feedback processes for end users and impacted communities to report problems and appeal system outcomes are established and integrated into AI system evaluation metrics. | | MEASURE 4: Feedback about efficacy of measurement is gathered and assessed. | MEASURE 4.1: Measurement approaches for identifying AI risks are connected to deployment context(s) and informed through consultation with domain experts and other end users. Approaches are documented. | | | MEASURE 4.2: Measurement results regarding AI system trustworthiness in deployment context(s) and across the AI lifecycle are informed by input from domain experts and relevant AI actors to validate whether the system is performing consistently as intended. Results are documented. | | | MEASURE 4.3: Measurable performance improvements or declines based on consultations with relevant AI actors, including affected communities, and field data about context-relevant risks and trustworthiness characteristics are identified and documented. |
5.4 Manage
The MANAGE function entails allocating risk resources to mapped and measured risks on a regular basis and as defined by the GOVERN function. Risk treatment comprises plans to respond to, recover from, and communicate about incidents or events.
Contextual information gleaned from expert consultation and input from relevant AI actors – established in GOVERN and carried out in MAP – is utilized in this function to decrease the likelihood of system failures and negative impacts. Systematic documentation practices established in GOVERN and utilized in MAP and MEASURE bolster AI risk management efforts and increase transparency and accountability. Processes for assessing emergent risks are in place, along with mechanisms for continual improvement.
After completing the MANAGE function, plans for prioritizing risk and regular monitoring and improvement will be in place. Framework users will have enhanced capacity to manage the risks of deployed AI systems and to allocate risk management resources based on assessed and prioritized risks. It is incumbent on Framework users to continue to apply the MANAGE function to deployed AI systems as methods, contexts, risks, and needs or expectations from relevant AI actors evolve over time.
Practices related to managing AI risks are described in the NIST AI RMF Playbook. Table 4 lists the MANAGE function’s categories and subcategories.
| Category | Subcategory | |---|---| | MANAGE 1: AI risks based on assessments and other analytical output from the MAP and MEASURE functions are prioritized, responded to, and managed. | MANAGE 1.1: A determination is made as to whether the AI system achieves its intended purposes and stated objectives and whether its development or deployment should proceed. | | | MANAGE 1.2: Treatment of documented AI risks is prioritized based on impact, likelihood, and available resources or methods. | | | MANAGE 1.3: Responses to the AI risks deemed high priority, as identified by the MAP function, are developed, planned, and documented. Risk response options can include mitigating, transferring, avoiding, or accepting. | | | MANAGE 1.4: Negative residual risks (defined as the sum of all unmitigated risks) to both downstream acquirers of AI systems and end users are documented. | | MANAGE 2: Strategies to maximize AI benefits and minimize negative impacts are planned, prepared, implemented, documented, and informed by input from relevant AI actors. | MANAGE 2.1: Resources required to manage AI risks are taken into account – along with viable non-AI alternative systems, approaches, or methods – to reduce the magnitude or likelihood of potential impacts. | | | MANAGE 2.2: Mechanisms are in place and applied to sustain the value of deployed AI systems. | | | MANAGE 2.3: Procedures are followed to respond to and recover from a previously unknown risk when it is identified. | | | MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use. | | MANAGE 3: AI risks and benefits from third-party entities are managed. | MANAGE 3.1: AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented. | | | MANAGE 3.2: Pre-trained models which are used for development are monitored as part of AI system regular monitoring and maintenance. | | MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored regularly. | MANAGE 4.1: Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management. | | | MANAGE 4.2: Measurable activities for continual improvements are integrated into AI system updates and include regular engagement with interested parties, including relevant AI actors. | | | MANAGE 4.3: Incidents and errors are communicated to relevant AI actors, including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented. |
The Chief Digital and Artificial Intelligence Office (CDAO) is the Department of Defense's principal office for AI, data, and digital transformation. The CDAO was established to accelerate the DoD's adoption of AI, data, cloud, and digital solutions.
Current Initiatives and Programs
Competitive mechanism to iteratively discover, test, and scale novel ways of fighting with and against AI-enabled capabilities.
Unleashing AI agent development and experimentation for AI-enabled battle management and decision support.
Accelerating AI-enabled simulation capabilities to ensure we stay ahead of AI-enabled adversaries.
Accelerating the TechINT-to-capability development pipeline, turning intel into weapons in hours not years.
Enabling transformation of deterrence from static postures and speculation to dynamic pressure with interpretable results.
Putting America's world-leading AI models directly in the hands of our civilian and military personnel, at all classification levels to empower AI experimentation and transformation.
Building a playbook for rapid and secure AI agent development and deployment to transform enterprise workflows.
Tactical AI Platform
A tactical AI platform built to analyze and fuse sensor data for real-time object detection, tracking, and decision support in combat operations.
Data Integration
Designed to focus on and expand the core data integration layer; providing standardized data access that streamlines secure, rapid development and integration of agentic AI and other applications Department-wide.
We award contracts to companies offering solutions to national security challenges across a variety of technology areas.
— John Sundberg, CEO, Kinetic Data
"Our experience working with DIU has been a game-changer for Kinetic Data. DIU's guidance not only helped us prove our capabilities, but also made us more accessible within the government sector."
Our Acquisition Process
We engage across the Department of Defense to identify and understand critical national security challenges that can be solved with leading edge commercial technology within 12 to 24 months. Through our Commercial Solutions Opening (CSO) process, we competitively solicit proposals for innovative solutions that meet the needs of our DoD partners.
DIU leverages Other Transaction authority (10 USC 4022) to award prototype agreements in as few as 60-90 days. More importantly, vendors that execute a prototyping project to the satisfaction of the sponsoring Government customer and their contract commitments are awarded a Success Memo for their technology solution. This Success Memo for the company's solution that was deemed a "success" enables any Federal Government Agency to leverage this solution without having to recompete. There is no time restriction on the use of the Success Memo.
How We Work with Commercial Companies
We make it easy for companies that have never before done business with the Department of Defense — or the U.S. government — to win contracts based on merit and implement solutions at commercial speeds. DIU has already introduced more than 100 first-time vendors to the DoD. Need help learning how to work with the DOW? We have a guide for that.
DIU delivers revenue through flexible prototype contracts that apply commercial innovations to solve national security challenges. Whether seeking hardware, software, or service solutions, DIU lowers the barriers to entry and makes it faster and easier for companies of any size to do business with the Department of Defense.
1. We solicit commercial solutions on our website that address current needs of our DoD partners. (View all open solicitations. Learn more about our Challenges.) 2. You send us a short brief about your solution. (View our Solution Brief Guidance.) 3. We'll reach out if we're interested in scheduling a pitch. If we're not interested, we'll strive to let you know ASAP. 4. Do you have a National Security Solution?
How We Work with the DoD
At DIU, projects begin with a demand signal from you, our DoD partners. In a fraction of the time it traditionally takes, we evaluate and select projects with viable commercial solutions that have the potential to make a difference across DoD and have strong indicators for adoption.
We deliver leading commercial technology to our partners quickly by facilitating prototype contracts between companies and DoD entities. After a successful prototype, any interested DoD entity has the authority to enter into non-competitive follow on production contracts or agreements to procure the prototyped solution(s).
1. You get in touch with us about a mission-critical challenge your organization is facing. We'll explore next steps if you can offer a dedicated DIU liaison and funding to prototype solutions. 2. Our team works with you to translate your challenge into a competitive, commercial solicitation designed to deliver innovative proposals. 3. We'll solicit commercial solutions, and together we'll award contracts for one or more prototype projects and begin to chart a path toward technology adoption. 4. Do you have a National Security Challenge?
Common Questions
#### Who is eligible to submit solution briefs?
Any individual or commercial entity is eligible to respond to a DIU solicitation. Whether you are experienced with selling to government or this is your first time, we encourage all entities with applicable commercial solutions to submit to open DIU solicitations.
#### What form will this agreement (contract) take?
DIU principally leverages the flexibility of Other Transaction (OT) authority to award or prototype OT agreements. OT procedures allow for successful prototypes to transition into large volume defense contracts. Please refer to the CSO Guide for more information.
#### When can I communicate with DIU?
Any time. Please visit the Contact page to reach out. We encourage communication with DIU before, during, and after the CSO process.
#### How is intellectual property treated and protected?
Virtually every aspect of an OT contract or agreement is negotiable, including IP. Prior to the start of a project, it is important that a company identify rights in pre-existing data. In general, companies retain ownership of IP assets created during the effort. DoD usually licenses certain rights to use these assets in accordance with the OT contract terms and conditions. These rights control, inter alia, how DoD can use, disclose, or reproduce company-owned proprietary information.
#### How do I get paid?
Payment will be made by the Defense Finance and Accounting Services and will be based upon milestones agreed to by DoD and the company and incorporated into the OT contract or agreement. Your company will be responsible for submitting invoices into the Invoicing, Receiving, Acceptance and Property Transfer (iRAPT) module of the Wide Area Workflow (WAWF) application. More information on iRAPT can be found here.
#### Are there opportunities for future business and follow-on work?
Yes. In accordance with 10 USC 4022, and upon a determination that the prototype project has successfully been completed, any competitively awarded prototype-OT contract or agreement may result in the award of a follow-on production contract or agreement without the use of additional competitive procedures.
— CAPT Jonathan Haase, Program Executive Officer, PMS 408
"DIU is PMS-408's key partner to evaluate emerging technology and rapidly transition next-generation capability inside the acquisition system and budget cycle."
December 3, 2025 (Washington, DC) — Today, Defense Innovation Unit (DIU) announces the formal transition of the Department's first vetted UAS list, the Blue UAS Cleared List over to the Defense Contract Management Agency (DCMA). Created by DIU, the Blue UAS Cleared List contains commercial drones that have undergone security and performance assessments for UAS by the Department of Defense and government agencies. The Blue List and its related assets will officially transition on December 3, 2025, ahead of the original January 1st deadline and in alignment with Secretary of War Pete Hegseth's directive to achieve "small UAS domain dominance" by the end of 2027.
This transition is a crucial step in addressing the need to scale secure drone procurement for the U.S. military. Secretary Hegseth initiated this change through his July 2025 "Unleashing U.S. Military Drone Dominance" memo to accelerate the safe commercialization of drone technologies and fully integrate UAS into the National Airspace System. The Blue List aims to move from a certification program into a true marketplace where servicemembers can rapidly acquire trusted drone technology.
"Since the launch of Blue UAS List five years ago, the importance of drones on the modern battlefield continues to grow — as the War in Ukraine makes clear," said David Payne, Director of DIU's Autonomy Portfolio. "Drones are an essential part of a modern warfighter's kit, and we need an easy way to buy trusted, cutting-edge drone technology from a rapidly expanding industrial base."
As part of the handoff, Blue UAS List management will move from DIU's headquarters in Mountain View, California, to DCMA's new Unmanned Systems–Experimental Command, or US-X, at Palmdale, California, led by Air Force Col. Dustin Thomas, US-X commander. All related online resources will now be available on the DCMA Blue List website.
"It has been a pleasure partnering with DIU on the Blue List transition. DIU built the foundation, and DCMA is expanding and scaling that innovation to reshape how the Department delivers capability to our warfighters faster and smarter," said Col. Thomas.
The Blue UAS transition includes all stakeholders in the drone ecosystem, including the 81 unique companies DIU has processed onto the Blue List to date, established partnerships with military units seeking secure, operational platforms, and the cohort of Recognized Assessors, a group of third-party organizations evaluating drone platforms and related components for compliance with NDAA. DIU will continue as a partner to Blue UAS, providing expertise to shape standards and update checklists, while DCMA will assume primary responsibility for list management and expansion.
Launched in 2020, the Blue UAS List is the DoD's trusted catalog of secure, NDAA-compliant drones and components, and the digital Blue List platform will remain accessible to all authorized users regardless of location, maintaining accessibility for warfighters and procurement officials.
As of November 19, 2025 the Blue UAS List has provided over 39 certified Blue UAS systems and 165 components for users across the Department and U.S. Government. The Blue UAS effort will continue its holistic and continuous approach in rapidly vetting commercial unmanned aerial systems to ensure compliance with the NDAA and other applicable policy.
Full public-domain text, hosted on this site for reading. Source: U.S. Army (army.mil).
PICATINNY ARSENAL, N.J. — The U.S. Army's Joint Program Executive Office Armaments & Ammunition and U.S. Army Contracting Command – Rock Island have awarded a contract action with a ceiling of $435 million to REPKON USA – Defense LLC to design, build and commission a TNT production facility in Graham, Kentucky. The contract is being executed as a sole-source undefinitized contract action under the authority of Section 3204(a)(7) of Title 10, United States Code (10 U.S.C. 3204(a)(7)), and Subsection 1244(a)(2) of the James M. Inhofe National Defense Authorization Act for Fiscal Year 2023.
TNT is essential for various military applications such as ammunition, bombs and grenades, and serves as the primary explosive fill for 155 mm artillery shells. This award will reestablish TNT production swiftly and at scale on U.S. soil for the first time in decades.
"This is a major step forward in rebuilding our industrial base and ensuring we have the critical capabilities to support our warfighters," said Douglas Bush, Assistant Secretary of the Army for Acquisition, Logistics and Technology. "Reshoring TNT production gives us the ability to control and secure our supply chain for this vital component, especially in an era of increasing global challenges."
Establishing a domestic TNT production capability is vital for national defense, as the current supply chain is entirely reliant on overseas sources. This accelerated timeline supports the Army's goal of rapidly replenishing its critical munitions inventory and maintaining readiness for future conflicts.
Maj. Gen. John T. Reim, Joint Program Executive Officer for Armaments and Ammunition, emphasized the strategic importance of this project.
"This new state-of-the-art facility is essential to the JPEO A&A's mission to develop, procure and field safe, reliable and lethal munitions to our joint warfighters and international partners," said Reim. "This project will also further strengthen our defense industrial base, enabling munitions production at speed and scale."
This award is part of JPEO A&A's efforts to address potential supply chain vulnerabilities and enhance national security by securing uninterrupted access to crucial materials. By reshoring essential resources like rare earth minerals, chemicals and electronics, the Army seeks to reinforce its defense industrial base against global uncertainties.
For more information, please contact Mr. Jamal Beck at jamal.b.beck.civ@army.mil.