An AI safety evaluation report should make its evidence useful for a decision: identify the system and intended use, explain which risks were assessed and how, present findings and uncertainty, and show what mitigations or deployment conditions follow. There is no universal report template in the guidance cited here; the outline below is a practical synthesis, not a compliance checklist.
Start with the decision and the system in scope
Open with a concise executive summary that lets a decision-maker understand what was evaluated and what action is being considered. Include the system or application version, intended use, evaluation date, decision sought, headline findings, key residual risks, and the person or group accountable for the decision.
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Then describe the system and its operating context: the model and relevant components or interfaces, deployment setting, expected users, use constraints, and how people interact with or oversee the system. A result about a model in isolation may not describe the behavior of an application that adds retrieval, tools, filters, or human review.
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Explain which risks were assessed—and why
List the harms and failure modes in scope, explain why they matter for this use, and state how they were prioritized. Make risk thresholds or acceptance criteria explicit where they exist. Identify important exclusions and explain their rationale so readers do not mistake an untested risk for a risk that was ruled out.
The NIST AI Risk Management Framework (AI RMF) is voluntary and use-case agnostic, intended to support trustworthiness considerations across AI design, development, use, and evaluation. NIST says the framework is being revised. It can inform a report, but it does not supply a universal reporting form or make this outline a legal requirement. NIST AI Risk Management Framework
Document methods so readers can interpret the evidence
For each evaluation activity, describe the procedure and conditions closely enough that another evaluator can understand what the result does—and does not—mean. Include test materials, metrics, tools, prompts or scenarios, evaluator roles, sampling, and relevant environment or configuration details. Record the system version and any changes made during testing.
NIST’s ARIA Evaluation Planning Manual, published September 18, 2026, describes model testing, red teaming, and user testing as evaluation types. NIST’s 2025 ARIA pilot report describes model testing, red teaming, and field testing. These are complementary approaches, not interchangeable labels; report the actual activities performed rather than implying that one automatically covers the others. NIST ARIA Evaluation Planning Manual · NIST ARIA Pilot Evaluation Report
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Model testing
Report the selected tests, data or prompts, scoring method, metrics, and conditions. Explain what the tests measure and how they relate to the risks in scope. Benchmark scores can be useful, but they are evidence under selected conditions—not a complete account of safety in deployment.
Red teaming
Describe the goals of the exercise, attack or misuse scenarios, tools and access provided, evaluator expertise, and how findings were recorded and validated. Note whether the exercise was bounded by particular policies or technical controls; those boundaries shape what the results can reveal.
User or field testing
When realistic interaction matters, explain who participated, the setting and tasks, how interactions were observed or annotated, and how feedback or outcomes were measured. NIST’s ARIA pilot report describes dialogue annotation, tester questionnaires, and measurement trees as part of its evaluation approach. Field or user testing adds context that a benchmark may miss, but its findings remain tied to the participants and conditions studied.
NIST states that its AI Risk Management Framework specifically calls for a Test, Evaluation, Verification, and Validation (TEVV) methodology. The TEVV-Athlon page presents an adaptable framework for assessing real-world impacts and outcomes across varied AI systems. NIST TEVV-Athlon Framework
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Organize results so a reader can trace each important risk from the test method to the observed evidence and its implications. Include quantitative and qualitative findings where relevant, notable failure cases, comparisons used, and any retest results after changes. Distinguish observed outcomes from interpretation or inference.
State the limits of the evidence beside the findings it qualifies: coverage gaps, assumptions, possible measurement bias, validity constraints, and how far results may generalize to other users, settings, or versions. Explain what the evaluation cannot establish. The International AI Safety Report 2026 says evidence about the real-world effectiveness of current AI risk-management practices remains limited; a report should not present a successful test as proof that risk has been eliminated. International AI Safety Report 2026
Connect results to mitigations and a deployment decision
For each material finding, document the response: changes made, controls applied, retest evidence, and any vulnerability or uncertainty that remains. State the resulting decision and its rationale, including deployment or access conditions where relevant. If risk is accepted, identify who owns that decision and why the remaining risk falls within the stated tolerance.
A clear report distinguishes “mitigated” from “fixed.” A mitigation may reduce likelihood or impact without removing the failure mode, and its effectiveness may depend on configuration or operating conditions. Tie each condition to an accountable owner where possible.
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Specify monitoring and incident response after evaluation
Pre-deployment tests cannot show how every real-world use will unfold. Set out post-deployment indicators, who monitors them, review cadence, escalation paths, and thresholds for intervention, rollback, or renewed evaluation. Explain how incidents will be documented and reported, and how new evidence will feed back into the risk decision. The International AI Safety Report 2026 identifies monitoring and incident reporting among relevant transparency and risk-management practices.
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Make the report legible to its intended audience
Provide enough system and evaluation information for appropriate scrutiny, while explaining any sensitive details withheld and why. Model or system cards can communicate basic model details, pre-deployment results, and limitations; broader transparency reporting and information sharing can also help others assess claims. Choose a level of detail suited to the audience, but avoid a summary that hides material caveats.
NIST’s ARIA pilot report covered five organizations and seven AI applications. Those figures describe that pilot’s participants and submissions only; they are not a benchmark for the scale or completeness of AI safety evaluations generally.
A practical report outline
- Executive decision summary: system, intended use, version and evaluation date, decision sought, headline findings, residual risks, and decision owner.
- System and context: model or application, components and interfaces in scope, deployment setting, users, constraints, and human-AI configuration.
- Risk scope and criteria: harms considered, prioritization rationale, thresholds or tolerance, exclusions, and their rationale.
- Methods and materials: tests and exercises performed, test sets, metrics, tools, prompts or scenarios, evaluator roles, sampling, and conditions.
- Results: findings by risk and method, quantitative and qualitative evidence, failure cases, comparisons, and uncertainty.
- Limitations: coverage gaps, assumptions, validity constraints, what the evaluation cannot establish, and limits on generalization.
- Mitigations and residual risk: changes, retest results, remaining vulnerabilities, deployment conditions, and decision rationale.
- Monitoring and incident response: indicators, owner, review cadence, escalation or rollback triggers, and incident-reporting process.
- Transparency appendix: information needed for appropriate external scrutiny, with reasons for any sensitive omissions.
This outline is a practical way to make an evaluation understandable and actionable. Adapt it to the system, use, audience, and any applicable sector or jurisdiction requirements; the cited NIST guidance is not itself evidence of a universal legal reporting obligation.
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