The role of Artificial Intelligence (AI)-enabled firms in security cooperation requires a concept that captures more than ordinary contracting. This article calls that concept epistemic delegation.
Epistemic delegation occurs when states rely on private systems to produce, organize, and interpret the operational knowledge on which military decisions depend. The term does not imply that private firms replace commanders, governments, or human judgment. Nor does it suggest that software alone decides when force is used. Rather, it identifies a subtler form of influence: private firms increasingly shape the informational environment within which military actors see the battlefield, assess relevance, identify threats, and decide which options appear actionable.
This matters because military decisions are never made from raw reality. They are made through representations of reality: maps, intelligence feeds, sensor inputs, targeting data, operational pictures, risk assessments, and recommendations. In earlier eras, those representations were produced largely through military intelligence structures, government agencies, and state-controlled command systems. Today, however, commercial platforms can play an increasingly important role in collecting, fusing, ranking, and displaying the information that makes military action possible.
In this sense, the issue is not only who controls a weapon system or who authorizes a strike. It is also who helps determine what appears on the screen before a decision is made. Which data streams are included? Which are excluded? How are satellite images, drone feeds, radar, thermal imaging, and ground reports integrated into a common picture? Which targets or threats are elevated as urgent? Which options appear efficient, feasible, or operationally attractive? These are not simply technical questions. These are questions about how knowledge is produced in war.
Epistemic delegation, therefore, extends familiar concerns about privatized security into a new domain. Earlier debates about contractors often focused on the delegation of action: who trains partner forces, who moves equipment, who maintains platforms, who provides security, or who supports operations. AI-enabled firms raise questions about the delegation of perception and interpretation. They may not apply force directly, but they can help structure the knowledge through which force becomes intelligible and actionable.
That distinction is especially important in security cooperation. When an external provider supports a partner force, oversight already depends on imperfect visibility into the manner in which assistance is used. If a private AI platform becomes central to the partner’s operational picture, the oversight problem changes. Provider states and democratic institutions may not only need to monitor weapons, funding, or training. They may also need to understand how private systems mediate the information environment through which a partner force interprets threats and acts.
The key accountability problem follows from this intermediary position. An AI-enabled firm may shape what military users see and prioritize without being the formal decision-maker. The recipient state may act on the resulting operational picture, while the provider state may have enabled or tolerated the broader relationship. If a contested outcome follows, responsibility becomes difficult to assign. The firm did not fire the weapon, the platform did not formally authorize the decision, and the military user may still have exercised individual judgment. Yet the knowledge environment that made the decision appear reasonable may have been partly structured by a private system.
Epistemic delegation names this gap. It highlights a form of private influence that sits between raw information and military action. If private firms increasingly help produce the operational knowledge on which the directed use of force depends, then oversight and accountability need to adapt accordingly. Security cooperation can no longer be understood only as the transfer of equipment, training, or support. It must also be understood as the possible delegation of the battlefield picture itself.
How Epistemic Delegation Works
Epistemic delegation becomes visible through four recurring mechanisms. These mechanisms do not describe every possible effect of private AI systems in war. Rather, they identify the main ways that defense technology firms like Palantir may shape how battlefield knowledge is produced, interpreted, and acted upon.
Information Filtering
The first mechanism is information filtering. AI-enabled platforms do not simply collect information; they organize it. In Ukraine, Palantir’s systems have been described as integrating satellite imagery, drone feeds, radar, thermal imaging, sensor data, and ground reporting into a shared operational picture. That matters because military actors rarely encounter the battlespace in an unmediated form. They encounter a processed representation of it. When a private platform selects, combines, and structures large volumes of battlefield information, it helps determine what becomes visible and usable to decision-makers.
This does not mean that the platform fabricates reality or replaces human interpretation. But it does mean that the system plays an active role in turning dispersed data into operational knowledge. A battlefield picture is never neutral in a simple sense. It reflects choices about what data are included, how they are weighted, how they are displayed, and what kinds of relationships become easier or harder to see. The firm that provides the platform, therefore, participates in the first stage of military judgment: defining what the battlefield looks like.
Prioritization and Salience
The second mechanism is prioritization and salience. Once information enters a system, not all of it bears equal weight. Some targets appear more urgent. Some threats appear more immediate. Some courses of action appear more feasible or effective. This is where AI-enabled platforms may move from displaying information to shaping attention. Reporting on Palantir’s role in Ukraine has described systems that support target identification, retrieve coordinates, and present targeting-relevant options to users. Even if commanders and operators remain responsible for final decisions, the system can influence which possibilities appear most relevant before a decision is made.
This form of salience-making is especially important because attention is a scarce resource in war. Commanders and analysts face overwhelming amounts of information, much of it incomplete, contradictory, or time sensitive. A system that helps determine which signals rise above the noise is not simply assisting with efficiency. It is shaping the operational field of perception. What appears on the screen, what is highlighted, and what is rendered actionable can influence the direction of judgment even when human beings remain formally in control.
Opacity
The third mechanism is opacity. AI-enabled firms may improve what users can see while making it harder for outside actors to understand how that visibility is produced. This is the central tension of the Palantir case. A platform may give military users a clearer and more integrated battlefield picture, while provider states, legislatures, publics, auditors, or external overseers may have limited ability to inspect how the system processes inputs, ranks outputs, or generates recommendations.
Opacity does not have to mean everything is secret. The problem can be more subtle. External actors may know that a platform is being used and may even understand what it allows users to do, but still lack meaningful access to the internal logic through which outputs are generated. Proprietary systems, classified operational arrangements, and wartime urgency all intensify this problem. In such conditions, accountability grows difficult because it is not enough to know that a decision was made. Overseers must also be able to reconstruct how the relevant operational picture was produced and how private systems shaped that picture.
Epistemic Dependence
The fourth mechanism is epistemic dependence. A private AI system becomes more consequential when military users come to rely on it as a central source of operational knowledge. Occasional use of a platform is one thing. Dependence is different. If a system becomes embedded in targeting workflows, battlefield awareness, intelligence fusion, or the development of future AI-enabled capabilities, it becomes harder to remove, contest, or replace without affecting mission effectiveness.
This is where the oversight problem becomes most serious. A system that is useful, but peripheral, can be audited, limited, or replaced more easily. A system that becomes central to how a partner force sees and acts in war creates a different kind of dependency. Provider states and recipient governments may become reluctant to impose constraints if doing so risks compromising battlefield effectiveness. The private firm’s role can therefore become entrenched not because it has formal command authority, but because its platform becomes practically indispensable to the production of operational knowledge.
These mechanisms reinforce one another. Information filtering shapes what enters the operational picture. Prioritization and salience shape what appears important within that picture. Opacity makes it difficult for outside actors to understand how that picture is produced. Dependence makes the system harder to remove once it becomes central to military workflows. Together, these mechanisms show why epistemic delegation is not simply a theoretical concern. It is a practical oversight problem created when private AI firms help produce the knowledge environment through which force is understood and applied.
The issue is not that these systems are inherently harmful. They may improve speed, coordination, targeting precision, and battlefield awareness. In some cases, they may also create better records of operational activity than fragmented intelligence processes would allow. But those benefits do not eliminate the accountability problem. They sharpen it. The more useful such systems become, the more important it will be to ask who can inspect them, who can challenge their outputs, who can understand their role in decision-making, and who bears responsibility when AI-mediated knowledge contributes to contested outcomes.
Why This Matters for Oversight and Accountability
The central oversight problem created by epistemic delegation is that AI-enabled systems may increase visibility for users while decreasing visibility for the institutions responsible for overseeing them. A battlefield platform that integrates satellite imagery, drone feeds, radar, thermal imaging, and ground reporting may give commanders a clearer operational picture. It may help users identify targets, track threats, and coordinate action more quickly than fragmented systems would allow. From inside the operational environment, the platform can appear to improve awareness and control.
But that same improvement in operational visibility does not automatically translate into institutional accountability. Provider states, legislatures, auditors, publics, and even some military authorities may know that a system is being used without being able to determine how it structures the information on which decisions are based. They may see the output without being able to inspect the process: a target was identified, but not how different data streams were weighted, which assumptions shaped the system’s presentation, or why certain options appeared more actionable than others.
This distinction between operational visibility and process visibility matters because oversight depends on more than access to outcomes. It requires some ability to monitor, review, and constrain the process through which consequential judgments are made. In traditional security cooperation, oversight often focuses on relatively concrete objects: weapons transferred, equipment delivered, training conducted, funds spent, or end-use restrictions observed. These mechanisms are imperfect, but they are at least designed around visible forms of support. AI-enabled decision-support systems are different. The object of oversight is not only a platform or a contract; it is an informational process.
Accountability becomes more complicated for the same reason. If a contested strike, targeting error, escalation risk, or misuse of intelligence follows from an AI-mediated process, responsibility may be distributed across several actors. The recipient state may have acted on the operational picture. The provider state may have enabled the wider security cooperation environment. The private firm may have supplied the system that filtered, integrated, and prioritized the relevant information. Military users may still have exercised human judgment. Yet no single actor may fully own the chain through which raw information became actionable knowledge.
This does not mean that responsibility disappears. It means that responsibility becomes harder to locate. A private firm may shape what military users see without formally authorizing the use of force. A platform may influence which targets appear salient without making the final decision. A partner force may remain legally responsible for its actions while relying on a privately structured information environment. The accountability gap lies in this intermediary space between data and decision.
That gap is especially important in security cooperation because outside providers already face limits in monitoring how partners use assistance. If private AI systems become embedded in a partner force’s operational workflows, those limits may deepen. The provider may be supporting a partner not only through weapons, training, or funding, but through a private informational architecture that shapes battlefield interpretation itself. Existing oversight tools are not well designed for that problem. End-use monitoring can verify the location or use of a weapon. Contract review can assess whether a service was delivered. But neither easily answers how a private platform shaped what a partner force perceived, prioritized, or treated as actionable.
The point is not that AI-enabled systems necessarily weaken accountability in every instance. In some cases, they may preserve logs, consolidate records, and make parts of the decision process easier to reconstruct. That possibility should not be dismissed. But it should also not obscure the wider institutional tension. A system can make war more legible to its users while making the production of that legibility harder for outsiders to govern.
That is why epistemic delegation matters. It identifies a form of private influence that does not fit neatly into older categories of contractor accountability. The problem is not only whether private firms support military operations. The problem is whether they help produce the operational knowledge through which military action becomes possible, while remaining partially outside the oversight structures designed to govern the use of force.
Implications: Toward AI-Aware Security Cooperation Oversight
If private AI firms increasingly shape the informational environment of war, then understandings and oversight of security cooperation must evolve beyond frameworks created primarily for weapons transfers, training missions, sustainment packages, and end-use monitoring. Those tools remain necessary, but they are not sufficient for systems that filter information, generate operational pictures, prioritize threats, or support targeting-relevant judgment. The challenge is no longer only to monitor what capabilities are transferred or how partner forces use them. It is also to understand how private platforms mediate the knowledge through which those capabilities are employed. Several implications follow for how security cooperation should be governed
A first implication is that security cooperation frameworks should distinguish between ordinary contractor support and AI-enabled systems that shape operational judgment. Not every digital tool creates the same oversight problem. A software system used for logistics or administration differs from a platform that fuses intelligence streams, ranks threats, or supports target identification. The more directly a system contributes to classification, prioritization, recommendation, or targeting support, the more demanding the oversight requirements should be.
Second, provider states and partner governments should place greater emphasis on documentation, logging, auditability, and record preservation. If AI-enabled platforms help structure battlefield knowledge, then users and overseers need ways to reconstruct how key outputs were generated and how they entered decision-making processes. This does not require making every proprietary detail public or exposing sensitive wartime information. But it does require mechanisms that allow appropriate authorities to review system use, preserve decision trails, and evaluate whether outputs were relied upon in ways consistent with legal, operational, and policy requirements.
Third, security cooperation involving AI-enabled decision-support systems should clarify the role of human decision-making. Human oversight can’t just be treated like a slogan. It must be tied to identifiable practices: who reviews outputs, who can challenge recommendations, who is authorized to override the system, and how disagreement with system-generated outputs is recorded. Without such practices, “human-in-the-loop” language may conceal rather than resolve the accountability problem.
Fourth, existing responsible-AI frameworks ought to be adapted to security cooperation contexts. Guidance from the National Institute of Standards and Technology (NIST), the Office of Management and Budget (OMB), and the Department of Defense (DoD) already emphasizes risk management, testing, auditability, documentation, and human oversight for high-risk AI systems. The problem is that these frameworks themselves do not always map cleanly onto wartime partnerships involving private firms, provider states, and recipient forces. Security cooperation, therefore, needs a bridge between traditional assistance oversight and AI governance: a way to assess not only whether a partner acquired a capability, but how AI-enabled systems shape the decisions that capability supports.
Recent reporting about a fully autonomous drone strike in Ukraine underscores why this problem cannot be treated as a distant or hypothetical concern. Whether or not that specific account is ultimately verified in the form reported, it should not be surprising that the final moment of human confirmation is now under pressure. Long before a weapon selects and engages a target without direct approval, human judgment may already have been progressively narrowed by systems that filter battlefield data, rank threats, recommend targets, and define what appears actionable.
The autonomous strike is therefore not a complete break from the logic described here. It is one possible endpoint of it. A decision that appears to have “removed the human” at the final stage may rest on a longer process in which human agency was already distributed across interfaces, algorithms, vendors, commanders, and partner institutions. Oversight should therefore focus not only on whether a human confirms the final strike, but on how the preceding informational architecture shapes what that human is asked to confirm.
The case for stronger oversight does not require treating private AI firms as inherently illegitimate actors in war. Their systems may improve battlefield awareness, speed, coordination, and even precision. But operational utility does not eliminate the need for oversight. In fact, the more useful such systems become, the more important governance becomes. A marginal platform can be removed or ignored. A platform that becomes central to how a partner force sees the battlefield is much harder to constrain after the fact.
The broader lesson from Ukraine is therefore not simply that AI is changing war. It is that AI-enabled security cooperation may change where consequential agency resides. Private firms may not command forces or authorize strikes, but they can help shape the operational knowledge on which those decisions depend. If that role continues to expand, policymakers will need to treat information mediation as a core object of oversight, not a technical detail left to vendors and operators.
Security cooperation can no longer focus only on weapons, training, funding, and end-use. It must also account for the private infrastructures that make force intelligible in the first place.
