Evaluate the AI system in the setting where it will actually be used—not just the model in a benchmark. Define the intended use and affected people, identify plausible harms, set tests and launch limits in advance, and decide whether the remaining risk is acceptable to accountable decision-makers. Deployment is not a one-time pass: monitoring, incident response, and reassessment belong in the safety plan from the start.
What counts as an AI safety evaluation?
A safety evaluation is a documented decision process that connects evidence about a system to a particular deployment. It asks what could go wrong, how likely and consequential those failures may be in context, what safeguards reduce the risks, and whether the remaining risks are acceptable.
A model score alone cannot answer those questions. A deployed system may also include prompts, retrieval sources, connected tools, user interfaces, human reviewers, and downstream decisions. Assess those components and their interactions, along with the model itself. NIST’s voluntary AI Risk Management Framework (AI RMF) is designed for risk management across design, development, deployment, use, and evaluation; it is not a safety certification.
NIST’s cross-sector Generative AI Profile, AI 600-1, published July 26, 2024, adds guidance for generative AI risks. Neither document replaces applicable legal, regulatory, or sector-specific requirements.
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How to assess safety before launch
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Define the system and its boundary
Record the model and version, prompts, data sources, connected services or tools, interface, and human review arrangements. Describe the intended use, foreseeable uses outside that intent, user groups, people affected by outputs, and operating conditions. Note where data comes from, where it goes, and which decisions may depend on the system.
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Assign owners and decision authority
Name the people responsible for identifying and managing risks, responding to incidents, and deciding whether to launch. Make clear who can pause or roll back deployment and who has authority to accept residual risk. NIST’s voluntary AI RMF Playbook organizes suggested activities under Govern, Map, Measure, and Manage.
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Map harms in the intended context
Consider which trustworthiness concerns matter for this system: safety, reliability, security and resilience, privacy, fairness and harmful bias, transparency, explainability, and accountability. Look beyond a bad answer from the model itself: harm can arise from misuse, integration choices, human overreliance, or a downstream decision based on an output. The relevant trade-offs depend on the use case, as NIST explains in its AI RMF FAQs.
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Translate risks into testable questions
For each material harm, specify scenarios, evidence to collect, unacceptable outcomes, and who must be notified if a threshold is crossed. Set these before reviewing results so the team does not move the goalposts after seeing a favorable score. For generative AI, the NIST profile calls attention to output validity and safety, harmful bias, privacy violations, intellectual-property infringement, violent or hateful content, misuse, and attempts to circumvent safeguards.
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Test at appropriate levels
Use ordinary performance tests to examine expected tasks, then add adversarial or misuse testing where plausible. Evaluate integrated behavior and the deployment context, not only the base model. NIST’s ARIA program describes model testing, red-teaming, and field testing as distinct evaluation levels, and emphasizes technical and contextual robustness. Choose coverage according to the likely harms and stakes.
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Document the launch decision
Summarize test evidence, known limitations, mitigations, unresolved risks, and the people accountable for the decision. NIST’s Generative AI Profile says a system to be deployed should be demonstrated safe, its residual negative risk should not exceed the organization’s risk tolerance, and it should be able to fail safely—particularly beyond its knowledge limits. The reviewed NIST guidance does not set one universal numerical launch threshold; the organization must define a defensible threshold for its use case.
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Prepare for operation and change
Before release, establish how outputs and performance will be monitored, how users or staff can report problems, how incidents are escalated, and how the system can be contained, repaired, or rolled back. Schedule reevaluation when the model, prompts, connected tools, user population, data, or operating conditions change. NIST’s profile calls for regular safety evaluation and processes for detected errors and anomalies.
Which kinds of testing should be included?
Different evaluation levels answer different questions. They are complementary rather than substitutes; a system can perform well in controlled tests and still behave poorly in its real operating context.
| Evaluation level | What it helps establish | What it cannot establish by itself |
|---|---|---|
| Model testing | How the model behaves on planned tasks and scenarios, including defined measures for expected performance and identified risks. | Whether connected tools, human workflows, or real-world conditions introduce additional failures. |
| Red-teaming | How the system responds to adversarial prompts, misuse attempts, or efforts to circumvent safeguards. | That all attacks or harmful behaviors have been found, or that ordinary deployment conditions are safe. |
| Field or context-aware testing | How the integrated system and its users behave in conditions closer to the intended use. | That performance will remain safe for every population, setting, or future system change. |
NIST ARIA identifies these evaluation levels and the importance of technical and contextual robustness; the precise test design should follow the deployment’s risks rather than a fixed checklist.
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What should the team test for?
Use the system boundary and harm map to create scenarios tied to actual consequences. For a generative system, the NIST profile identifies several risk areas to consider:
- Unsafe or invalid outputs: Can the system produce inaccurate or harmful content in a high-impact context, and is it able to signal uncertainty or decline when appropriate?
- Harmful bias: Do outputs or downstream actions create unfair differences among affected groups in the intended setting?
- Privacy violations: Could prompts, outputs, or connected data expose personal or otherwise protected information?
- Intellectual-property concerns: Could generated or retrieved content infringe intellectual-property rights in the planned use?
- Violent or hateful content: Can the system generate or amplify such content, including through foreseeable misuse?
- Safeguard circumvention: Can users bypass restrictions through adversarial inputs, tool use, or multi-step interactions?
- Integration and workflow failures: Could a tool call, human handoff, interface choice, or downstream decision turn an otherwise limited model error into a material harm?
These are prompts for risk-specific evaluation, not a universal exhaustive list. For each identified risk, choose scenarios that represent both ordinary use and credible edge cases, record observed failures, and test whether mitigations change the outcome.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a team decide whether to launch?
Use the evidence to make a risk decision, not to claim that the system is risk-free. A defensible launch record should let a reviewer see the relationship between the intended use, the harms considered, the tests performed, the safeguards in place, and the risks accepted.
Best Value
- Proceed when the evidence is adequate for the defined use, safeguards address material risks, residual risk fits the organization’s stated tolerance, and operational controls are ready.
- Restrict or stage deployment when risk can be reduced through narrower use, tighter access, human review, or limited rollout, and the revised arrangement is itself evaluated.
- Delay or reject deployment when serious risks remain uncontrolled, the evidence is insufficient for the stakes, safe failure cannot be demonstrated, or no accountable decision-maker is willing to accept the residual risk.
Record the scope of the decision: which version, configuration, users, and operating conditions it covers. A material change can invalidate assumptions behind the evaluation and should trigger review.
What must happen after deployment?
Prelaunch testing is a snapshot; operational evidence shows whether assumptions hold in practice. Monitor relevant outputs and performance, investigate errors and anomalies, and provide a clear route for escalation. Define who can suspend use, what recovery looks like, and how a fix will be checked before restoring service.
Reassess when the model or deployment changes and at intervals appropriate to the risks. The review should consider whether new users, data, integrations, or operating conditions create harms that were not covered by the original evaluation. This makes safety a continuing lifecycle responsibility rather than a one-time approval.
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