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Evaluate the complete AI system in the context where it will be used—not just the model or a benchmark score. Map who may be affected and how the system could fail, test the risks that matter under deployment-like conditions, set release criteria before testing, and document who can approve or stop deployment. A safety assessment is an evidence-based decision, not proof that a system is risk-free.
1. Define the system and its deployment context
Start by describing the actual product, configuration and workflow you plan to deploy. A model’s behavior can change when it is connected to a user interface, tools, data sources, third-party software or a human decision process. Assess those components together wherever they shape risk.
Record the system’s intended purpose; who will use it; who may be affected by its outputs; the environment in which it will operate; and the decisions it can influence. Include human oversight, third-party components, operating assumptions and foreseeable misuse. Note what is not yet known. NIST’s AI Risk Management Framework (AI RMF) treats this context as a prerequisite to meaningful risk management and an initial go/no-go decision (NIST AI RMF Core).
2. Identify and prioritize possible harms
Consider both the system’s intended use and reasonably foreseeable misuse. For each possible harm, describe who could experience it, how it could happen, and its likely severity and frequency. A rare failure with serious health or safety consequences may deserve more attention than a frequent but minor inconvenience.
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Depending on the use, examine privacy, security, fairness, reliability, transparency, human-AI interaction and effects on groups. Identify potential benefits as well as harms, and record risks that cannot yet be measured rather than treating a lack of evidence as evidence of safety. NIST recommends tailoring risk management to context and the severity of potential harms (AI RMF 1.0).
3. Set evaluation questions and release thresholds before testing
For every prioritized risk, specify what you need to learn, how you will test it, what evidence would be acceptable, and what result would require mitigation or block release. Define the test population, data, metrics, evaluation tools and relevant subgroups in advance. Choose thresholds that reflect the actual use and consequences of failure rather than relying on a generic pass mark.
Not every important question has a meaningful numerical answer. Record qualitative findings and explain risks that remain unmeasured. Assign owners for evaluation and mitigation, identify who has authority to approve a release, and specify who can restrict, defer or stop it. NIST’s Core and companion playbook outline voluntary risk-management practices for organizing this work (AI RMF Core).
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4. Test the system under realistic conditions
Use test data and scenarios that resemble the expected deployment setting. Evaluate the actual system configuration, including its interface, connected tools and human workflow, not only the underlying model. Measure validity, reliability and generalization; examine error types and subgroup results where relevant; and assess whether people and the system together perform the intended task safely.
Test whether the system behaves safely when inputs are ambiguous, conditions shift, or it encounters cases beyond its known limits. Assess the security and resilience of the system, privacy protections, fairness, transparency and accountability. Document limitations and the conditions the system has not been shown to handle. NIST emphasizes lifecycle safety evidence, including simulation, in-domain testing, monitoring, and the ability to intervene, modify or shut a system down when it deviates from expected function (AI RMF 1.0).
5. Challenge the system independently
Routine evaluations can miss failures that emerge from deliberate misuse or unusual inputs. Red-team exercises can probe security weaknesses, unexpected instructions, misuse and failure paths. For generative AI, include risks associated with the system’s outputs and how they will be used; the NIST Generative AI Profile provides additional risk-management guidance for generative AI.
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Where practical, involve evaluators who are not responsible for front-line development, alongside relevant domain experts. Include representative users or affected populations when their experience can reveal risks that technical tests miss. Human-subject evaluation should follow applicable protections and represent the populations relevant to the intended use.
These activities answer different questions. NIST’s ARIA program describes three complementary levels: model testing for controlled technical behavior, red-teaming for adversarial behavior, and field testing in a real or representative environment. None substitutes for the others when each is needed to assess the deployment context (NIST Assessing Risks and Impacts of AI).
6. Document what the tests cannot establish
A passing result is only as informative as the test’s coverage and validity. Record what was not tested, where test data came from, whether they represent the intended setting, and which risks remain difficult to measure. Consider whether public or training-exposed test material could have been encountered during development, because that may make results less informative.
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For example, OpenAI’s Deep Research System Card notes that web browsing can expose a system to answers for some cybersecurity exercises, complicating interpretation. Held-out tests and contamination controls can help preserve the value of an evaluation. Treat a benchmark result as one piece of evidence, not a complete safety ranking.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Make a documented release decision
Have the accountable decision-maker compare the evidence with the criteria set before testing. A decision may be to deploy, deploy with restrictions, defer until mitigations are complete, or stop. Record the rationale, remaining risks, evidence gaps and any restrictions that are conditions of release.
Before launch, establish how the organization will detect and respond to problems: define monitoring signals, incident escalation, user feedback or appeal routes, and a rollback or shutdown path. Decide what changes—such as a shift in system capabilities, use context or observed failure patterns—will trigger a new evaluation. This turns the pre-deployment assessment into a lifecycle process rather than a one-time approval. NIST calls for ongoing evaluation and monitoring; the EU AI Act also provides for continuous risk management for high-risk AI systems (NIST AI RMF Core; Regulation (EU) 2024/1689).
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How to interpret the frameworks and legal requirements
The NIST AI RMF is a voluntary framework, organized around Govern, Map, Measure and Manage. NIST’s official resource is the place to check for the current framework materials and any revision status (NIST AI RMF Core).
In the EU, the AI Act sets obligations for systems classified as high-risk. Article 9 addresses an iterative risk-management system and testing, as appropriate, during development and before market placement or putting into service; Article 43 covers conformity-assessment procedures. Whether a system is in scope, and which obligations or assessment route apply, depends on matters including classification, intended purpose, and whether an organization is acting as provider or deployer. Consult the consolidated law and qualified counsel for a specific compliance determination (Regulation (EU) 2024/1689; European Commission, Article 9).
Sector-specific requirements may also apply in areas such as healthcare and transportation. Treat framework guidance and technical evaluation as parts of a safety process, not as a substitute for checking the rules that govern the particular system and use.
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