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If an AI model fails a safety test, pause or restrict the affected use when the failure could cause harm beyond your organization’s risk tolerance. Preserve the evidence, investigate the failure’s scope, assign an owner to remediate it, and retest in conditions that reflect intended use. Resume only when documented results support accepting the remaining risk—and keep monitoring and a way to disengage the system.
First, decide what the failure means in context
A failed test is a decision point, not a complete risk assessment by itself. Its significance depends on what the model is meant to do, who may be affected, what the test actually measured, and how serious and likely the relevant harm is in the real operating context. A failure on a narrow benchmark may not establish that every use of a system is unsafe; a passing score on a benchmark does not establish that a particular deployment is safe.
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Start by matching the finding to the proposed use. Ask whether the tested behavior could plausibly occur with the real users, inputs, tools, integrations, languages, and operating conditions involved. Consider what happens if the model produces the unsafe output, how many people might be exposed, whether the output could be acted on without review, and whether a safe fallback exists. Risks of serious injury or death warrant the highest urgency and most thorough risk management.
NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance, not a universal legal ruling or a fixed pass/fail standard. It calls for safety and residual risk to be assessed in context and against an organization’s risk tolerance. There is no universal failure threshold, number of required tests, or fixed remediation deadline established by the cited NIST materials. Your organization should apply its own documented criteria and any obligations specific to the use and jurisdiction.
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Contain exposure when the risk calls for it
Establish whether the model or affected capability is already in use. If the failure indicates a plausible harm beyond your tolerance, do not proceed with the release or leave the exposed capability running while the investigation waits. Choose a containment measure proportionate to the risk and available controls:
- Before release: hold deployment or remove the affected feature from the release scope.
- In a live system: restrict access, disable the affected capability, route cases to human review, or switch to a safe fallback if one is available and effective.
- When escalation criteria are met: have the designated risk authority disengage, deactivate, or decommission the system under the organization’s established process.
Record who made the decision, what was restricted, when the change took effect, and what exposure remains. A control such as human review is useful only if it can reliably catch the relevant failure before it causes harm; do not treat the presence of a nominal safeguard as proof that exposure is contained.
NIST’s AI RMF Core says: “Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use.” This makes shutdown authority and a workable mechanism part of risk management, not an improvised response after a failure.
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Preserve the evidence and determine the failure’s scope
Before changing the system, preserve what is needed to understand and reproduce the finding, subject to appropriate privacy and security controls. NIST calls for documented evaluation and incident procedures, but it does not prescribe one universal failure-record form. A practical incident record should capture:
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- The model and surrounding system versions, including relevant configuration, retrieval sources, tools, and integrations.
- The test method, evaluation data or scenario, intended use, operating conditions, and known limitations.
- The prompts or inputs, expected behavior, actual output, and the reason the result was judged a failure.
- Whether the result reproduced, how often it occurred, and any conditions that appeared to trigger or prevent it.
- Known real-world impact, potentially affected use cases and people, containment actions, decision-makers, and communications.
Keep enough information to compare the original and remediated versions. Protect sensitive user data and access to the record; preservation does not mean retaining or circulating personal information without a legitimate need.
Work out whether the issue is isolated or systemic
Reproduce the failure where possible, then vary relevant conditions deliberately: inputs, user roles, language, context length, tool access, retrieval content, and other components that are part of the intended deployment. Determine whether the cause appears to be the model, its configuration or data, an integration, an operational process, or an interaction among them. Test nearby cases as well as the exact failing example. A fix that blocks one prompt but leaves the same unsafe behavior available through a different phrasing or workflow has not resolved the underlying risk.
Also identify the boundary of the finding. Does it affect one task, one user group, one integration, or every use that depends on the same capability? Keep conclusions tied to the evidence: label untested areas as unknown rather than assuming the failure is either universal or harmlessly narrow.
Assign remediation and check for unintended effects
Give the issue an accountable owner with authority to coordinate technical changes and operational controls. Set milestones appropriate to severity, define who must approve a release, and decide how affected stakeholders will be informed. NIST’s Generative AI Profile recommends procedures to remediate issues that trigger incident response and to communicate remediation timelines to stakeholders; it does not supply a single deadline for every failure.
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Possible responses include changing system configuration or data, adding effective controls around outputs, strengthening human review, narrowing the permitted use, or removing the affected use entirely. Choose a response that addresses the cause and the risk—not simply the test score. Record the rationale and how each change is expected to reduce exposure.
After a change, reassess risks rather than carrying forward the old evaluation as if nothing changed. Fine-tuning, retrieval augmentation, prompt or policy changes, new tools, and altered workflows can change behavior. Check whether a mitigation introduces a different problem, weakens performance on a necessary task, or shifts the failure to an edge case or another group of users. For third-party models, evaluate the model as used in your own application and context; an initial vendor or model evaluation does not establish safety for a different deployment.
Retest with methods suited to the risk
Before release, use repeatable measures and scenarios that resemble intended use. NIST’s evaluation guidance emphasizes context-relevant assessment and ongoing testing after deployment. Its ARIA Evaluation Planning Manual, published September 18, 2026, organizes holistic evaluation across model testing, red teaming, and user testing. These approaches examine different questions and work best as complementary evidence rather than interchangeable certificates.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Evaluation approach | What it can help reveal | What to make representative |
|---|---|---|
| Model testing | Behavior on defined tasks and scenarios, including whether the original failure recurs after a change. | Inputs, task mix, success and failure criteria, languages, and relevant system configuration. |
| Red teaming | How the system behaves under adversarial or deliberately challenging use, including attempts to bypass safeguards. | Threat scenarios, access and tool permissions, attack paths, and the likely capabilities of users in scope. |
| User testing | How people experience and act on system outputs in a real or realistic workflow, including contextual harms that a model-only test may miss. | Relevant users, tasks, interfaces, handoffs, and operating conditions. |
For each evaluation, document the method, measures, data or scenarios, results, limitations, and system version. Where a result is important to a release decision, make it possible to reproduce and compare it. A test that is repeatable in a laboratory but omits the actual retrieval system, user interface, review process, or tool permissions may not answer whether the deployed system is safe in context.
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Set release criteria before interpreting the retest
Use the organization’s established risk tolerance and escalation criteria, not a threshold selected after seeing whether the new result is convenient. Ask whether the original failure is resolved, whether related failure modes remain, whether the mitigation works in the intended workflow, and what uncertainty remains. Document residual risk and the reason it is or is not acceptable, including the evidence and the person or authority accepting it.
Do not treat a single successful retest as proof that all risk is gone. The evidence supports only the tested conditions and the limits of the method. A release decision should account for severity, coverage, repeatability, safeguards, fallback options, and operational readiness—not only an aggregate score.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make a documented go, restricted-go, or no-go decision
The accountable decision-maker should choose among holding the system, approving a restricted use with effective controls, or resuming the intended use. NIST’s AI RMF Core states: “The AI system is evaluated regularly for safety risks – as identified in the map function.” That supports continuing assessment rather than treating pre-release testing as the final safety checkpoint.
- Hold or stop: use this when the failure exceeds tolerance, containment is unreliable, or the available evidence is not adequate to support exposure.
- Restricted use: consider it only if the limited scope and safeguards materially reduce the risk, the limits can be enforced, and someone is responsible for watching for failures.
- Resume: do so when documented evaluation supports the residual-risk decision, required controls are operational, and release approval follows the organization’s process.
Keep the decision record with the evaluation and remediation evidence. It should make clear what was approved, for which version and use, under what controls, by whom, and what conditions would trigger renewed escalation.
Monitor after release and keep a recovery path ready
Controlled deployment does not end the safety work. Monitor for recurrence, negative impacts, near misses, user reports, and relevant system or operating changes. Provide a clear path for users and staff to report problems, and define who reviews those reports and how quickly severe signals are escalated. Use monitoring suited to the risk and context; an absence of reports is not, by itself, evidence that no harm is occurring.
Maintain practical incident response and recovery procedures. Staff should know how to override or disengage the system, route work to a fallback, preserve incident evidence, communicate with affected stakeholders, and restore service only after the release decision is reconsidered. If performance or outcomes become inconsistent with intended use, apply the established criteria to restrict, deactivate, or decommission the system.
Reporting duties are not uniform. Whether an incident must be reported to a regulator, customer, affected person, or contractual partner depends on the jurisdiction, sector, product, and agreements involved. Have qualified legal and compliance staff review those requirements for the particular deployment rather than assuming one universal notification rule.
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