Evaluate an AI tool against a defined defense task and operating environment—not a generic benchmark or vendor claim. First set boundaries for the mission, users, data, integrations, and consequences of failure; then test security, mission-specific performance, oversight, and lifecycle support against those boundaries. A tool that cannot be evaluated or controlled adequately for its intended use is not ready for that use.
1. Define the mission and the tool’s permitted role
Start with a written use case. The same model may be suitable for a low-consequence administrative task and unsuitable for a workflow where an incorrect output could affect safety, security, or an operational decision. The Department of Defense (DoD) reliability principle calls for explicit, well-defined uses and testing and assurance of safety, security, and effectiveness within those uses across the system’s life cycle.
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Specify the operational boundary
Record the task the tool will perform, who will use it, what information it may receive, how users will act on its outputs, and which other systems it can access or affect. Describe relevant operating conditions, including connectivity, time pressure, data quality, and any constraints on where processing can occur.
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State what the tool is not permitted to do. For example, distinguish between summarizing material for a human reviewer and making or executing a decision without review. Identify the human decision that remains, who owns it, and what level of authority is required. These boundaries make later security and performance tests meaningful.
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Describe the consequences of error
List plausible harms from incorrect, incomplete, misleading, delayed, or unavailable outputs. Consider both false positives and false negatives where relevant, as well as misuse, data exposure, and unexpected system actions. Set the acceptable risk and escalation path for each material failure mode before a demonstration or pilot.
NIST’s AI Risk Management Framework (AI RMF) likewise treats risk and trustworthiness as context-dependent. It is a voluntary framework, not a DoD authorization or a certification that a system is trustworthy.
2. Establish the security and data-handling boundary
Assess the entire AI service in its intended deployment—not just the model or a vendor’s general security materials. The DoD’s AI Cybersecurity Risk Management Tailoring Guide, dated July 14, 2025, addresses lifecycle stages from acquisition and development through use, sustainment, monitoring, and disposal. Apply the current guide revision and the cybersecurity and authorization processes that govern the particular system and environment.
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Ask the provider and system owner to document where inputs, outputs, logs, and related data are processed and stored; which personnel or services can access them; and what retention, deletion, and backup practices apply. Determine whether data is used to train or improve a model, shared with subprocessors, or transferred across deployment boundaries. Establish how access is authenticated, limited, logged, and reviewed.
Trace dependencies as well as the main model: hosting, interfaces, retrieval sources, plug-ins, software components, update channels, and connected systems can all affect the security boundary. Identify how vulnerabilities, incidents, and material changes are reported and handled.
Match authorization to the actual information and deployment
Confirm which data types the proposed environment is authorized to handle and under what conditions. A commercial security statement or general assurance document does not establish that a product is authorized for classified or otherwise restricted information. Do not place such data into a tool unless the applicable authorization and handling rules permit it.
For a comparison, use the same data-handling scenario and deployment assumptions for each candidate. A tool’s security posture may differ substantially between a locally controlled deployment and a hosted service.
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Ask for evidence tied to the defined task, users, data, and operating conditions. Results from an unrelated benchmark or a polished demonstration do not establish performance in a defense workflow. DoD’s strategy calls for evaluation criteria that are testable and operationally relevant; its responsible-AI implementation guidance describes testing, verification and validation, monitoring, confidence measures, and user feedback.
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Build a representative evaluation
Use test cases that reflect the information and variation the system will actually encounter, including imperfect or ambiguous inputs. Include routine cases, edge cases, and adversarial or otherwise adverse conditions appropriate to the use. Where possible, use a separate evaluation set rather than relying only on examples used to configure or demonstrate the tool.
Define the expected result and how it will be judged before testing. Choose measures that fit the task: for example, error rates, completeness, timeliness, consistency, or the rate at which outputs require correction. Specify acceptance thresholds, escalation thresholds, and conditions that require a stop or additional review. Do not treat a single aggregate score as sufficient if it conceals a serious failure mode.
Examine failure behavior and uncertainty
Test what happens when information is missing, contradictory, out of date, or outside the tool’s intended scope. Check whether the system signals uncertainty, invents unsupported details, fails silently, or produces a plausible but wrong answer. If confidence indicators are available, establish whether they have been validated for this task and what users should do with them; a displayed confidence value is not evidence of correctness by itself.
Record the model and system configuration, test data and conditions, evaluation method, results, known limitations, and any human corrections. Repeat relevant tests after material changes to the model, data sources, integrations, or operating environment.
4. Assess trustworthiness as a set of interacting dimensions
NIST AI RMF 1.0 organizes trustworthiness considerations around validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy; and fairness, with harmful bias managed. Use these dimensions to identify questions and tradeoffs for the specific mission, not as a checklist whose completion proves that a system is safe.
- Validity and reliability: Does the evidence support the stated task and operating conditions, including known limits?
- Safety: What harms can arise from intended use, foreseeable misuse, or system failure, and what controls reduce them?
- Security and resilience: Can the system and its dependencies resist, detect, and recover from relevant attacks or disruptions?
- Accountability and transparency: Can responsible personnel identify who owns decisions and understand enough about the system and its operation to review its use?
- Explainability and interpretability: Does the information provided help the relevant user evaluate an output and decide whether to rely on it?
- Privacy: Are collection, access, use, retention, and disclosure of personal or sensitive information appropriate for the use?
- Fairness and harmful bias: Could performance differ materially across relevant populations, contexts, or data conditions, and how would that be detected and addressed?
These characteristics can conflict. A more interpretable approach may have different performance or security tradeoffs; greater data access may improve a task while increasing privacy or exposure risks. Document which qualities are critical, which tradeoffs are acceptable, and who has authority to accept them. NIST says the AI RMF is voluntary and is being revised; its framework page also lists a Generative AI Profile released in July 2024.
5. Make oversight and intervention workable
Human oversight is meaningful only if people have the authority, information, time, and training needed to act. DoD’s principles include responsibility and governability; its strategy and implementation guidance also emphasize documentation, monitoring, and lifecycle assurance.
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Name the accountable owner, the personnel authorized to approve use, the operators responsible for reviewing outputs, and the people who handle incidents and changes. Provide training that covers the system’s intended use, limits, common failure modes, data-handling rules, and escalation procedures. Specify when a human must independently verify an output rather than merely acknowledge it.
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Define monitoring and intervention rules
Decide what behavior or performance will be monitored, how issues will be reported, and what triggers restriction, suspension, rollback, or a fresh evaluation. Where the system can take actions or connect to other systems, establish controls that let authorized personnel interrupt, disengage, or deactivate it when it behaves unexpectedly. Test these procedures rather than assuming they will work under operational conditions.
Include a process for reviewing user feedback and incidents. A change in model behavior, input data, integration, or mission context can invalidate earlier assurance, so define who determines whether the change requires retesting or reapproval.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Put evaluation evidence and remedies into acquisition terms
Acquisition terms should support the evaluation and control plan throughout the contract and system life cycle. DoD’s 2022 AI strategy identifies contract provisions as a way to support independent government testing, vendor documentation and training, performance monitoring, data deliverables and rights, and remediation. Make requirements specific enough to verify and assign responsibility for meeting them.
Specify access, deliverables, and change control
- Define access for government testing and the documentation needed to reproduce or review evaluations, subject to applicable security and rights constraints.
- Identify required technical, operational, security, and user documentation, plus training for the roles that will use or oversee the system.
- Set out data deliverables and rights, including any limits on government use, retention, transfer, or independent evaluation.
- Require notice of material changes to models, data sources, dependencies, hosting, or interfaces, and state when those changes require review or retesting.
- Establish performance reporting, incident notification, correction timelines, and remedies if agreed requirements are not met.
GAO’s report GAO-23-105850, published June 29, 2023, found that DoD then lacked department-wide AI acquisition guidance. That is a finding about the period GAO assessed, not evidence of the department’s present policy status. GAO’s 2026 report recommends systematic lessons learned from AI acquisitions, including contract and testing practices. Use these dated reports as reasons to make acquisition evidence and learning explicit, not as substitutes for checking current policy.
7. Compare candidate tools on the same use case
Compare tools only after fixing the task, data, deployment, users, and operating conditions. A side-by-side assessment can use the following questions; the answers should be supported by evidence for the same scenario rather than vendor claims that use different assumptions.
| Evaluation axis | Evidence or question |
|---|---|
| Security and data handling | What are the data flows, access controls, retention practices, deployment boundaries, dependencies, and applicable authorization conditions? |
| Reliability in intended use | How does each tool perform on representative tasks and failure cases, and what limitations or uncertainty does the evidence show? |
| Testability and evidence | Can evaluators inspect relevant documentation, repeat tests, obtain monitoring evidence, and conduct independent assessment? |
| Oversight and control | Can operators understand the limits, identify responsible approvers, report incidents, and intervene or disengage when needed? |
| Acquisition and lifecycle support | Do terms cover training, documentation, data rights, material changes, performance monitoring, and remediation? |
| Tradeoffs | How do privacy, explainability, security, performance, and mission utility interact for this particular task? |
Do not collapse these dimensions into a universal score. A high result on one axis cannot compensate automatically for a disqualifying gap on another—for example, strong task performance does not resolve an unacceptable data-handling boundary or the absence of a workable intervention path.
8. Use the frameworks for what they establish
The DoD AI principles, strategy, implementation guidance, and July 2025 cybersecurity tailoring guide provide defense-specific anchors for use boundaries, assurance, oversight, and lifecycle risk management. NIST AI RMF offers a broader vocabulary for evaluating risks and trustworthiness. Neither a framework reference nor a completed checklist, by itself, demonstrates that a particular tool is suitable or authorized for a particular mission.
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