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Before deploying an AI model, evaluate the complete system in the context where it will be used—not just the model on a general benchmark. Define who could be affected and what could go wrong, map each risk to documented tests and decision criteria, probe safeguards through controlled red-teaming, and record whether the remaining risk is acceptable. Continue testing and monitoring after launch.
1. Define the system and its risks
Start by describing the intended use and the real deployment context. An evaluation of a model in isolation may miss risks introduced by its interface, data sources, tools, human operators, or surrounding services.
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- What tasks will the system perform, and what decisions or actions can it influence?
- Who will use it, and who else could be affected by its outputs?
- What model, tools, data flows, interfaces, and human review processes make up the system?
- Where and under what operating conditions will it run?
- What harms are plausible, and what level of residual risk will the organization accept?
The NIST AI Risk Management Framework (AI RMF) treats understanding context as an input to measuring and managing risk. The framework is voluntary guidance, not a certification or a universal safety threshold. See the NIST AI RMF overview and its AI RMF Playbook.
2. Turn risks into an evaluation plan
For every material risk, specify how you will look for it, what evidence counts, and who will act on the result. A written plan makes the evaluation repeatable and helps distinguish a meaningful failure from an unexplained score.
- Risk and scenario: describe the harm and a realistic situation in which it could occur.
- Test and measure: choose a quantitative metric, a qualitative rubric, human assessment, or a combination. State what the measure can and cannot show.
- Decision condition: set an acceptance target, escalation condition, or required mitigation before seeing results. Do not treat a single benchmark score as a universal pass mark.
- Owner: name who runs the test, reviews the evidence, and is accountable for the decision.
- Record: preserve test sets, metrics, tools, system configuration, conditions, results, and limitations.
Explain why the tests represent the expected setting and where results may not generalize. NIST’s AI RMF Measure guidance emphasizes documenting evaluation methods and evidence.
3. Test the actual deployment configuration
Run evaluations on the configuration people will encounter, under conditions that resemble intended use. Include relevant model components and human-AI interactions; a generic capability benchmark alone does not establish that a system is safe for a particular deployment.
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Choose coverage based on the risks you mapped. It may include safety, reliability, robustness to expected variation, security and resilience, transparency or accountability where relevant, and behavior near system limits. Check how the system handles uncertainty, errors, unusual inputs, and situations in which it should decline, hand off, or otherwise fail safely.
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4. Red-team adverse behavior and safeguards
Use controlled, scenario-based probing to find ways the system might produce harmful behavior or its safeguards might fail or be bypassed. Red-teaming complements planned tests: it can uncover cases your initial scenarios did not anticipate, but it does not prove that every risk has been found.
- Choose scenarios tied to the system’s intended use and plausible misuse or failure.
- Use evaluators with expertise appropriate to the system and risks. NIST notes that red-team output quality relates to the team’s background and expertise.
- Record the scenarios, configuration, findings, severity, mitigations, and any risk that remains.
- Retest relevant safeguards after changes and document what the follow-up establishes.
NIST’s Generative AI Profile (NIST AI 600-1) discusses red-teaming in the context of generative AI risks. It was published on July 26, 2024, according to NIST’s AI RMF resources.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Make and document the release decision
Compare the evidence with the criteria set in advance and the organization’s risk tolerance. The decision should account for test coverage, uncertainty, known limitations, unresolved issues, mitigations, and who is accountable—not just whether a model passed a benchmark.
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- Proceed with controls: specify required mitigations, human oversight, operating limits, or monitoring and who will verify them.
- Defer or do not deploy: use this outcome when evidence is inadequate or residual risk exceeds tolerance; identify what must change or be tested before reconsideration.
NIST does not establish a universal numerical pass score for deployment. Its AI RMF is voluntary guidance, and the AI RMF 1.0 is being revised; consult the current NIST overview for framework status.
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6. Keep evaluating after launch
Pre-deployment results describe what was tested under stated conditions; they do not guarantee safe behavior as usage, inputs, or the operating environment change. Monitor for failures and changed conditions, retain a way to investigate and respond, and continue testing during operation. Make sure the system can fail safely if a problem appears.
For a broader example of layered evaluations, NIST’s ARIA program describes model testing, red-teaming, and field testing, including attention to technical and contextual robustness. These are useful evaluation layers, not a mandatory checklist for every organization. See NIST ARIA. NIST’s separate GenAI evaluation program describes work on capabilities and limitations, adversarial evaluations, benchmarks, and human studies across modalities.
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