Evaluate an AI tool for the specific defense task it will perform—not by its vendor claims or a general benchmark. Define the mission, users, data, operating conditions, consequences of error and permitted actions first. Then assess security, mission-relevant performance, test evidence, human control and acquisition terms against that defined use.
1. Define the mission and the tool’s permitted role
Begin with a written use case. An AI system that drafts a logistics summary, flags a cyber anomaly or supports an intelligence analyst presents different risks and needs different evidence. Performance on an unrelated benchmark does not establish that a tool is suitable for any of those tasks.
The Department of Defense’s reliability principle makes this context-specific approach explicit: “The department’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.”
Write down the operating boundary
- Task and users: What is the tool expected to do, and who will operate it or rely on its output?
- Inputs and data: What information may enter the system, including prompts, files, sensor feeds or connected databases?
- Output and integration: How will people use the output? Can it flow into another system or trigger an action?
- Conditions: Where and when will it run, and what connectivity, time pressure, data quality or environmental constraints apply?
- Failure consequences: What could happen if the output is wrong, incomplete, delayed or unavailable?
- Authority limits: What decisions must remain with a human? What uses are prohibited, and who can approve exceptions?
This boundary gives evaluators a defensible basis for deciding what to test and what level of performance is acceptable. NIST’s AI Risk Management Framework (AI RMF) likewise treats risk and trustworthiness as dependent on context; it is a risk-management aid, not a substitute for mission judgment.
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2. Establish the security and data-handling boundary
Review the whole system that will support the use case—not just the model. That includes its data flows, hosting and storage, users and administrators, connected services, software dependencies, update process and deployment environment. A vendor’s general security materials do not by themselves establish that a product is authorized to handle a particular classification or other restricted data.
Questions for the security review
- Where are prompts, inputs, outputs, logs and related data processed and stored?
- Which people or services can access them, and how are access and administrative privileges controlled?
- What logging and retention apply? Can data be used for other purposes, including model improvement?
- How are software, models and dependencies updated, and how are changes reviewed?
- What happens if a connected service, account, component or data source is compromised or unavailable?
- Which security controls and authorization process apply to the intended deployment?
The Department of Defense’s AI Cybersecurity Risk Management Tailoring Guide, dated July 14, 2025, addresses lifecycle cybersecurity risk management from acquisition and development through use, sustainment, monitoring and disposal. Apply the relevant DoD process and verify the current guide revision and the authorization rules for the particular system and deployment; the guide is not a blanket approval of any commercial service.
3. Test reliability under mission-relevant conditions
Build an evaluation around the task, users, data and operating conditions defined for the tool. Record expected performance, unacceptable failure modes and what should happen when the system is uncertain or fails. A polished demonstration or high score on a general benchmark is not a substitute for repeatable evidence from representative scenarios.
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Design the evaluation
- Assemble representative cases. Include the kinds of inputs, data quality and user workflows expected in operation, while protecting information under the applicable rules.
- Define measures and thresholds. Choose task-relevant measures and specify what counts as acceptable performance, a need for human review or a reason to reject the tool.
- Probe failure conditions. Test edge cases, misleading or incomplete inputs, degraded connectivity, unusual operating conditions and other plausible adverse scenarios.
- Assess uncertainty and recovery. Where meaningful, determine whether confidence indicators help users recognize uncertain outputs. Check how the system behaves after errors, interruptions or changes.
- Make results reproducible. Document the evaluation setup, test cases, results, known limits and changes to the system so later reviewers can interpret or repeat the work.
The DoD strategy calls for evaluation criteria that are both testable and operationally relevant. Its implementation memorandum describes a test and evaluation, verification and validation approach that includes monitoring, confidence measures and user feedback. Use those as prompts to establish evidence for the particular task, rather than as a claim that one test design fits every system.
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4. Assess trustworthiness as a set of connected trade-offs
NIST AI RMF 1.0 offers dimensions to consider together: validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy; and fairness with harmful bias managed. Which dimensions matter most, and how they interact, depends on the use case. For example, added explanation may help an operator scrutinize an output, while privacy, performance or security constraints may affect what information can be exposed.
Do not turn these dimensions into a universal pass score. NIST notes that trustworthiness characteristics can conflict and that people must use judgment in choosing measures and thresholds. Use the framework to identify relevant risks, trade-offs and evidence needs—not to claim that checking every category proves a system trustworthy.
NIST describes AI RMF 1.0, released January 26, 2023, as voluntary guidance and says it is being revised. Its resources also include a Generative AI Profile released in July 2024. Neither the framework nor a claim of alignment with it is, by itself, a DoD requirement or a certification that a system is fit for a defense purpose.
5. Make oversight and intervention operational
Human oversight should be designed into the workflow rather than left as a general promise that a person is “in the loop.” The Department of Defense’s principles include responsibility and governability, while its strategy and implementation memorandum point to documentation, monitoring and lifecycle assurance.
Assign clear responsibilities
- Name the accountable owner and the authority that approves the tool for the defined use.
- Specify operator training and what users must understand about the system’s limits and uncertainty.
- Set who monitors behavior, reviews incidents and decides whether deployment should continue.
- Define how users report unexpected outputs and how incidents are assessed and escalated.
- Where applicable, verify how the system can be restricted, disengaged or deactivated, and who has authority to do so.
Document the information and traceability relevant personnel need to understand the technology, its development and its operational methods. Establish conditions for stopping, limiting or reverting use before deployment, not only after a problem occurs.
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6. Put evidence, access and remedies in acquisition terms
Evaluation is harder to sustain if the buyer cannot obtain needed information or test the system after delivery. The DoD’s 2022 strategy identifies acquisition provisions as tools for securing resources and support. For a particular procurement, consider specifying rights and responsibilities such as:
- Access for government or independent evaluation, including repeatable testing where appropriate.
- Vendor documentation, operator training and information about relevant system changes.
- Data deliverables and rights, defined for the intended use and applicable rules.
- Performance monitoring, reporting and change-notification arrangements over the system lifecycle.
- Remediation commitments if agreed requirements are not met, and a process for reassessing performance after material changes.
These are procurement considerations, not a claim that every term is mandatory in every acquisition. Tailor them to the mission, applicable policy and contracting authority, and ensure the agreement supports the evaluation and oversight the use case requires.
Dates matter when using oversight findings. In its report published June 29, 2023, GAO-23-105850 found that DoD did not then have department-wide AI acquisition guidance. That is a finding about the period GAO assessed, not proof of the current state. GAO’s 2026 report recommends systematic lessons learned from AI acquisitions, including contract and testing practices. Use each report for its dated finding or recommendation rather than treating either as a substitute for checking current policy.
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7. Compare tools against the same use case
Compare candidate systems only after defining the same task, users, data boundary and operating conditions for each. Record evidence and unresolved issues by axis; a single combined score can conceal a security weakness or a failure mode that matters disproportionately to the mission.
| Comparison axis | What to compare | Evidence to seek |
|---|---|---|
| Security and data handling | Data flows, access, retention, deployment boundary, dependencies and applicable authorization process | System-specific security and data-handling documentation, reviewed against the intended deployment |
| Reliability in intended use | Task performance, failure behavior, limits and uncertainty under representative conditions | Repeatable, mission-relevant evaluation results, including adverse and edge cases |
| Testability and evidence | Documentation, independent evaluation access and ability to monitor performance | Test records and contract terms that enable evaluation and ongoing review |
| Oversight and control | Operator understanding, approval authority, incident paths and intervention capability | Defined roles, training and tested procedures for escalation or disengagement |
| Acquisition and lifecycle support | Data rights, training, documentation, change management, monitoring and remediation | Procurement terms and vendor commitments suited to the intended use |
| Trade-offs | How privacy, explainability, performance, security and mission utility interact | A use-specific rationale for priorities and thresholds, rather than an assumed universal ranking |
Conclusion
A defensible evaluation links every decision to a defined use: the data and deployment must fit the security boundary; performance must be demonstrated under relevant conditions; accountable people must be able to monitor and intervene; and acquisition terms must preserve access to evidence and lifecycle support. DoD principles and guidance and NIST AI RMF can structure that work, but neither replaces the organization’s responsibility to assess the actual system in its operational context.
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