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Reduce security risk in defense AI by treating it as a mission-critical system across its entire lifecycle—not just as a model to test once before deployment. Define what the system is allowed to do, protect its data and dependencies, test it against realistic and adversarial conditions, train the people who use or approve it, and prepare to restrict or deactivate it if behavior departs from its intended use.
These are risk-management practices, not proof that any particular fielded defense system is secure or vulnerable. The guidance cited below describes general controls and principles; claims about a specific system require system-specific evidence.
Start by defining the mission and the system’s boundaries
Before evaluating a model, describe the capability it supports and the consequences of an error. A forecasting tool, an intelligence-analysis assistant, and a generative system that drafts operational material have different inputs, users, failure modes, and potential impacts. Do not assume a control suitable for one is sufficient for another.
Write down the system’s intended use and the boundaries that operators, approvers, and connected software are expected to observe. Include:
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- Whether the capability is predictive, generative, or combines both, and which tasks or decisions it supports.
- Who may use it, who approves its use, and which actions it can initiate or influence.
- What data enters the system, where that data comes from, where outputs go, and which systems receive them.
- External models, datasets, software, infrastructure, service providers, and update or retraining paths on which it depends.
- What could happen if the system is wrong, manipulated, unavailable, or used outside its intended purpose.
This boundary-setting is a practical starting point for tailoring controls. The sources cited here do not establish weapon-autonomy rules or settle legal obligations for a particular mission; those questions require the relevant authoritative policy and legal review.
Map the AI-specific and ordinary cyber attack surfaces
AI can inherit familiar software, hardware, workflow, and supply-chain vulnerabilities while adding risks tied to models and data. The joint Guidelines for Secure AI System Development (November 2023) describes adversarial machine learning as exploitation of vulnerabilities in machine-learning components across those areas. It notes that attacks can affect predictions or classifications, enable unauthorized actions, or expose sensitive model information. NIST’s Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (AI 100-2 E2025, March 2025) organizes attack categories and mitigations for predictive and generative AI.
| Risk area | What can go wrong | Security question to ask |
|---|---|---|
| Input manipulation or evasion | An attacker crafts or alters an input so the model produces an incorrect or misleading classification, prediction, or response. | Has the system been evaluated on plausible manipulated inputs as well as expected operating data? |
| Training, fine-tuning, or feedback-data poisoning | Maliciously modified data can degrade performance, introduce bias, or steer a system toward unintended responses. A compromise upstream may be difficult to find, particularly at scale. | Can the organization establish data origin, integrity, labeling quality, and who can change data used for training or updates? |
| Prompt injection and misuse | For relevant generative systems, crafted instructions or misuse may steer behavior beyond intended boundaries or prompt unsafe actions. | What inputs and connected tools can influence the system, and what actions are prevented or require approval? |
| Privacy and information exposure | An attack may seek sensitive information from a model or its surrounding data and workflows. | What sensitive information is exposed to the model, users, providers, logs, or connected services? |
| Software, hardware, workflow, and supplier compromise | Compromised components or processes may undermine model behavior, data integrity, access control, or availability. | Which components and providers are relied upon, and how are their risks assessed and changes monitored? |
These categories are not interchangeable, and mitigations depend on the model, task, and deployment. NIST’s taxonomy discusses countermeasures and their limitations; no single safeguard should be treated as eliminating adversarial risk.
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Protect data, models, and external dependencies
Data controls should cover the full path from collection through use, storage, updates, and any retraining. The DoD-hosted Artificial Intelligence and Machine Learning Supply Chain Risks and Mitigations (March 2026) warns that low-quality or biased data can reduce robustness and produce incorrect classifications or predictions. It also describes poisoning as a way to degrade performance, create bias, or cause unintended or malicious responses.
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- Check quality and integrity: Review data for labeling problems, unexpected changes, and signs of tampering before it is used or incorporated into updates.
- Control access and changes: Limit who can introduce, alter, approve, or export datasets, model artifacts, and configuration changes; retain records that support investigation.
- Secure update paths: Treat fine-tuning, feedback collection, model replacement, and software updates as controlled changes, not routine inputs that automatically deserve trust.
- Assess suppliers and services: Evaluate visibility into external models, datasets, software, hosting, and support, including how providers manage security and notify customers about relevant changes.
NIST SP 800-161 Rev. 1, Cybersecurity Supply Chain Risk Management Practices for Systems and Organizations (published May 2022; updated November 1, 2024), provides a broad approach involving strategy, plans, and risk assessments for products and services. Applying that framework to AI models, datasets, and providers is a practical extension of its general supply-chain guidance, not an AI-specific prescription in the publication’s abstract.
Test the system against its intended use and plausible attacks
Assurance should span development, acquisition, deployment, operation, and maintenance. The 2023 joint secure-development guidance says cybersecurity is necessary for AI safety, resilience, privacy, fairness, efficacy, and reliability, and should be a core requirement across the system lifecycle. The DoD’s five AI principles also describe lifecycle testing and assurance, traceability, reliability, and governability.
Build a test plan around the system’s actual mission boundary rather than a generic pass/fail claim. Include representative operating conditions and adversarial conditions relevant to the deployment. Depending on the system, evaluate:
- Whether predictions or generated outputs remain reliable when inputs are incomplete, unusual, misleading, or deliberately manipulated.
- Whether data pipelines, model updates, and external dependencies preserve integrity and expected behavior.
- Whether access restrictions, connected tools, and approval steps prevent an output from triggering an unintended action.
- Whether users understand uncertainty, limitations, and the conditions under which they should challenge or escalate an output.
- Whether logs and traceability records are sufficient to review an incident and identify affected components or data.
Red-team exercises can probe whether tools or workflows can be misused. A June 2021 DoD Joint AI Center briefing transcript records discussion of red-team and machine-learning red-team testing, including a question about vetting externally sourced data for poisoning. That transcript is a historical discussion, not a binding current requirement. The cited sources do not prescribe one universal testing protocol or establish that any particular exercise will find every vulnerability. Record test conditions, findings, unresolved issues, and residual risk so decision-makers know what the evidence does—and does not—show.
Keep trained people accountable for context-aware decisions
Human oversight is meaningful only when the people using or approving a system understand its capability limits and have a clear role in decisions. The DoD account of measures endorsed for global militaries (November 2023) calls for training personnel to understand limits, make context-informed judgments, and mitigate automation bias—the tendency to over-rely on automated outputs.
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For each use, define when a person must independently assess an output, when to seek a second review, and when to stop or escalate. Train users on how the system can fail and on what to do when an output conflicts with other evidence or falls outside the intended use. Preserve clear responsibility for decisions rather than treating a model’s output as its own authorization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plan to detect, contain, and disengage from unintended behavior
Operational controls should make it possible to notice and respond when behavior changes or departs from the approved boundary. The DoD’s five AI principles—responsible, equitable, traceable, reliable, and governable—include the ability to detect unintended consequences and disengage or deactivate systems that exhibit unintended behavior. The department’s 2020 summary states: “The department will design and engineer AI capabilities to fulfill their intended functions while possessing the ability to detect and avoid unintended consequences, and to disengage or deactivate deployed systems that demonstrate unintended behavior.”
Translate that principle into system-specific procedures before deployment. Decide what signals warrant investigation, who can restrict access or suspend a function, how operators safely transfer work to an alternative process, and how the system can be disengaged or deactivated if needed. Exercise those procedures and ensure that monitoring, logs, and decision authority work in the operational environment. The right trigger and recovery path depend on the mission and system; the principle does not supply a universal technical mechanism.
Compare acquisition options on mission-relevant evidence
When comparing models, suppliers, or deployment approaches, apply the same questions to each candidate rather than relying on a broad claim that a system is “secure.” The cited guidance supports these comparison dimensions, but it does not rank products or prescribe universal weights.
| Dimension | Evidence to request or examine |
|---|---|
| Intended use and consequence of error | Documented task boundary, users, connected actions, and likely impact of incorrect or unavailable outputs. |
| Data provenance and poisoning exposure | Source and handling records, integrity protections, labeling and quality practices, and controls on updates or retraining. |
| Attack surface and dependency visibility | Information about model, software, hardware, workflow, provider, and service dependencies and how changes are managed. |
| Performance and robustness | Results under representative operating conditions and relevant adversarial tests, including the scope and limits of testing. |
| Privacy and information exposure | What information the system receives, retains, shares, or exposes through outputs, logs, and external services. |
| Traceability and auditability | Records sufficient to understand inputs, outputs, model or configuration versions, approvals, and incidents. |
| Human oversight | Defined responsibilities, training, escalation paths, and safeguards against automation bias. |
| Lifecycle support and response | Supplier support and update arrangements, monitoring approach, and demonstrated means to contain or deactivate the capability. |
Ask for evidence tied to the proposed deployment and its stated use. A general taxonomy, policy principle, supplier statement, or test result from a different configuration does not by itself establish that the system is secure, compliant, or effective in a specific defense setting.
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