You cannot establish that an AI system is safe by checking its model against a benchmark alone. Evaluate the complete system in its intended setting: define who will use and be affected by it, identify likely harms and misuse, test the system and its human workflow against context-specific criteria, and make a documented release decision. Then monitor it and reevaluate when the system or its operating conditions change.
What should a pre-deployment safety evaluation cover?
Safety is a property of a system in a particular use, not a universal rating attached to a model. A model that performs acceptably in a controlled test may behave differently when connected to tools, supplied with different data, used by people under time pressure, or placed in a workflow where its output influences consequential decisions.
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Start by evaluating the configured system that people will actually encounter. Depending on the product, that may include the model, user interface, prompts, retrieval or other data sources, tools and integrations, third-party components, human review, escalation paths, and the organization’s operating procedures. The National Institute of Standards and Technology (NIST) treats context and the people and systems interacting across the lifecycle as essential inputs to risk management. Its AI Risk Management Framework (AI RMF) says that contextual knowledge should inform an initial decision about whether to design, develop, or deploy a system: NIST AI RMF Core.
The evaluation should produce more than a score. It should give decision-makers documented evidence about expected performance, plausible harms, uncertainties, mitigations, and the conditions under which release would or would not be acceptable.
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How do you define the system and its deployment context?
Before choosing tests, write a short, specific description of the proposed deployment. Avoid broad descriptions such as “an AI assistant for staff.” State what the system will do, where it will run, who will rely on it, and how its output could affect people.
- Purpose and boundaries: Describe intended tasks, prohibited tasks, users, affected individuals or groups, and decisions the system may influence. State what the system is not designed to do.
- Operating conditions: Record the environment, languages, data sources, expected input quality, workload, access controls, and the actual model and software configuration to be evaluated.
- People and oversight: Explain who reviews outputs, what expertise they need, whether they can reject or correct a result, and what happens when a reviewer is uncertain or the system fails.
- Dependencies and workflow: Include relevant interfaces, tools, integrations, third-party software or data, and the steps before and after an AI output. A technically correct output can still create risk if the surrounding workflow encourages people to over-rely on it.
- Foreseeable misuse and assumptions: Consider how users might use the system outside its intended purpose, how malicious actors might try to exploit it, and which assumptions about data, users, or operating conditions could prove false.
Record what is unknown as well as what is known. This context map determines which risks matter and whether there is enough information to proceed to testing.
How should you identify and prioritize risks?
List plausible benefits and harms for both intended use and reasonably foreseeable misuse. Consider the people who may experience consequences, not just the organization deploying the system. NIST’s AI RMF organizes risk work around governing, mapping, measuring, and managing risks; its core and voluntary playbook provide a framework for connecting context to evaluation and action (AI RMF Core).
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Useful areas to examine include:
- Safety and reliability: Could an incorrect, incomplete, or delayed result contribute to physical, financial, legal, or other serious harm?
- Privacy and security: Could the system expose personal or confidential information, enable unauthorized access, or be manipulated through inputs, integrations, or misuse?
- Fairness and distribution of impact: Could error rates, access, or consequences differ materially across relevant groups?
- Transparency and accountability: Can users understand the system’s role and limitations, and can the organization trace, investigate, and address an outcome?
- Human-AI interaction: Might people mistake a suggestion for a verified fact, defer to automation, or lack a realistic way to challenge an output?
Prioritize by considering both the likelihood of a failure and its severity. A low-frequency failure that could cause serious harm may deserve stricter controls than a frequent but minor inconvenience. Include the scale of exposure and whether harm can be detected and reversed. Where a risk cannot yet be measured credibly, record it as an evidence gap rather than treating it as absent.
How do you turn risks into testable release criteria?
For each prioritized risk, define the evidence needed before running the evaluation. This prevents teams from selecting a convenient benchmark after the fact and treating its result as a universal pass mark.
- State the evaluation question. For example: “Can the system distinguish cases it should escalate from cases it may handle within its approved scope?”
- Choose a method suited to the risk. This might be a performance test, workflow simulation, security assessment, expert review, user study, or a combination.
- Specify the data and conditions. Document how test cases were selected, what they represent, which relevant subgroups or scenarios will be examined, and what is outside the test’s scope.
- Define the result and threshold in advance. Identify what outcome triggers mitigation, restricted use, a deferred decision, or a no-go. Set thresholds in light of the use and the severity of potential harm, not a generic industry score.
- Assign ownership. Name who runs the test, who reviews the evidence, who owns remediation, and who has authority to approve, restrict, defer, or stop release.
Use qualitative findings where a numeric score would be misleading or where no meaningful measure is available. Explain how those findings inform the decision, and document risks that remain unmeasured. NIST’s AI RMF 1.0 emphasizes that safety approaches need to be tailored to context and potential severity (NIST AI RMF 1.0).
What should you test under deployment-like conditions?
Test the configured system using conditions that resemble its expected use, not only clean inputs or idealized tasks. The evaluation should cover how people and components interact with the system as well as how the model performs in isolation.
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Measure whether the system completes its intended tasks, how often it makes different kinds of errors, and how performance changes with realistic variation in inputs or operating conditions. Review results by relevant subgroup where appropriate. Include human-AI task performance when the outcome depends on a person interpreting or acting on the system’s output.
Robustness, security, and privacy
Probe for behavior under unexpected, poor-quality, or adversarial inputs; test relevant security weaknesses and failure paths; and assess whether the system handles sensitive data as intended. For generative AI, include output and interaction risks specific to the system and its use. NIST’s Generative AI Profile provides additional risk-management guidance for generative AI contexts (NIST Generative AI Profile).
Transparency, oversight, and safe failure
Check whether users can recognize the system’s role and limits, whether meaningful human intervention is possible, and whether the system can be modified, restricted, or shut down if it deviates from expected behavior. NIST describes lifecycle safety work that includes rigorous simulation, in-domain testing, real-time monitoring, and the ability to intervene. Applicable sector-specific requirements may also matter, including in areas such as healthcare or transportation (NIST AI RMF 1.0).
Keep a record of the configuration, test data, methods, metrics, results, and limitations. A result is only useful to the extent that the test represents the deployment and measures the risk it claims to address.
Why use red-teaming, independent review, and user input?
Routine testing can miss failures that arise from deliberate misuse, unusual inputs, or assumptions shared by the development team. Red-teaming adds structured challenge: evaluators try to expose security weaknesses, misuse pathways, unexpected instructions or inputs, and failure modes before release. Use evaluators who are not responsible for front-line development where practical, and involve relevant domain experts.
Where system effects depend on how people use it, include representative users or affected populations in evaluation as appropriate. Human-subject work must follow applicable protections and should represent the populations relevant to the deployment; feedback from a narrow group should not be presented as evidence about everyone affected.
NIST’s Assessing Risks and Impacts of AI (ARIA) program distinguishes complementary levels of evaluation: model testing, red-teaming, and field testing. Together, they help separate controlled technical behavior from adversarial behavior and performance in a real or representative environment (NIST ARIA).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you judge the limits of the test results?
A passing result is not proof of safety beyond the test’s coverage. Document what was not tested, which populations or conditions were not represented, what risks could not be measured, and how the test data were selected. Note whether evaluators may have encountered the test material during training or through public exposure, since familiarity with answers can make results less informative.
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Test contamination is a practical concern, not just a theoretical one. For example, the OpenAI Deep Research System Card describes how internet browsing can reveal answers to some cybersecurity exercises and complicate interpretation; held-out tests and contamination controls can help preserve evidential value (Deep Research System Card). Treat such controls as part of test design, and state remaining uncertainty plainly.
When comparing two systems or deployment designs, use the same context and protocol for both. Compare severity-weighted failures and residual risk, performance and reliability under expected conditions, subgroup variation, resilience to shifts and misuse, privacy and security, human oversight, ability to detect and recover from failure, evaluation independence and coverage, and the operational burden of monitoring. No single benchmark score can substitute for these dimensions.
What should the release decision and ongoing monitoring include?
Before deployment, an accountable decision-maker should review results against the criteria defined in advance. Record the decision and its rationale, including unresolved evidence gaps and residual risks. Possible outcomes include deployment as proposed, deployment with restrictions, deferral until mitigation is complete, or stopping the release.
For any approved deployment, the record should specify the conditions of use, required mitigations, rollback or shutdown path, monitoring signals, incident escalation route, and triggers for reassessment. Give users or affected people a practical way to report problems or challenge an outcome where appropriate.
After launch, monitor behavior and incidents in the operating environment and investigate unexpected patterns. Reevaluate when capabilities, data, integrations, user behavior, deployment context, or risks change. NIST’s AI RMF calls for ongoing evaluation, monitoring, and tracking of emergent risks (NIST AI RMF Core).
Which frameworks and legal obligations apply?
The NIST AI RMF is a voluntary risk-management framework, not a certification that makes a system safe or a substitute for applicable law. Its core uses the functions Govern, Map, Measure, and Manage; NIST’s associated playbook offers suggested tactical actions. Consult NIST’s official resource for the current status of the framework and its materials (NIST AI RMF Core).
In the European Union, the AI Act imposes obligations on systems classified as high-risk. Article 9 describes an iterative risk-management system, and says high-risk systems should be tested as appropriate throughout development and, in any event, before market placement or putting into service. Article 43 sets out conformity-assessment procedures. Whether a particular system is in scope, and which route applies, depends on factors including classification, intended purpose, and whether an organization is acting as provider or deployer. Check the consolidated regulation and obtain qualified legal advice for a specific compliance decision: Regulation (EU) 2024/1689 and the European Commission’s Article 9 guidance page.
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