Assess an AI system in the specific setting where it will be used—not as an abstract model. Before launch, define its purpose and users, identify affected people and likely failure modes, check applicable rules, test against pre-set criteria, and document who accepts any remaining risk. Then put monitoring, incident response, and rollback arrangements in place. NIST’s voluntary AI Risk Management Framework (AI RMF) offers a practical structure: Govern, Map, Measure, and Manage.
Start with the deployment, not just the model
A model’s general capabilities do not tell an organization whether a particular application is appropriate. The assessment needs to cover the system as people will encounter it: its workflow, operating environment, data inputs and outputs, connected services, and the decisions it may influence. That context determines who could be harmed, how serious a failure could be, and what evidence is relevant.
NIST describes its AI RMF as voluntary guidance to help organizations manage AI risks; it is not a safety certification or proof of legal compliance. Its four functions—Govern, Map, Measure, and Manage—can organize work across the system’s lifecycle. NIST says AI RMF 1.0 is being revised, and its Playbook is due to be updated following that revision, so check the NIST AI RMF overview and AI Resource Center for current materials. The NIST AI RMF Playbook supplies suggested actions and documentation practices that organizations can adapt.
A practical pre-deployment assessment
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Define the system and intended use
Record the intended purpose, users, operating conditions, degree of automation, and decisions or services the system can affect. Describe the people and groups who may be affected, including those who are not direct users. Map inputs, outputs, data flows, model and vendor dependencies, and the boundary between the AI system and the surrounding workflow. Note foreseeable secondary uses and misuse rather than assuming the original purpose will always be followed.
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Set ownership, authority, and risk tolerance
Name a business owner and involve appropriate technical, privacy, security, legal, and domain reviewers. Decide who can approve deployment, impose conditions, escalate concerns, or stop use. Set risk tolerance and guidelines before evaluating results, including explicit rejection or pause conditions. For generative AI, NIST recommends comparing outputs with predefined organizational risk tolerance, guidelines, and principles in its Generative AI Profile.
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Map impacts and plausible failure modes
Consider expected use, foreseeable misuse, downstream reliance, unequal impacts, safety consequences, and privacy and security exposures. Identify what happens when the system is wrong, unavailable, manipulated, or used outside its intended conditions. Consult people with relevant domain expertise and, where appropriate, knowledge of affected communities; technical performance alone may not reveal the consequences of a workflow decision.
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Check the rules for this use and organizational role
Legal duties depend on the deployment, jurisdiction, and whether the organization acts as a provider, deployer, or another relevant actor. For an EU deployment, determine whether the system or use case falls into a regulated category and which obligations apply to the organization’s role. The European Commission’s high-risk classification guidance is draft and non-binding, not a substitute for the law or legal advice.
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Test against criteria tied to the consequences
Before testing, define acceptance thresholds and what evidence will be sufficient for the stakes involved. Evaluate representative cases, edge cases, distribution shifts, misuse, and recovery from failure. Include relevant users and operating conditions where feasible. Select measures suited to each risk: a single aggregate score can conceal weak performance for a subgroup or a high-severity failure mode.
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Make and record a deployment decision
Choose to deploy, deploy with conditions, defer, or reject. Record the evidence, its limitations, mitigations, residual risks, decision rationale, accountable owners, and any dissent. Specify required human review, access controls, fallback behavior, escalation routes, and rollback procedures before release.
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Set monitoring and reassessment triggers
Define what will be monitored, who reviews it, and what changes require retesting, escalation, suspension, or a fresh assessment. Triggers can include performance changes, complaints, incidents, drift, security events, or a change to the model, data, vendor, intended use, operating conditions, or applicable rules. OECD principles call for systematic risk management across the lifecycle and traceability of datasets, processes, and decisions; see the OECD AI principles.
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What to evaluate
NIST identifies characteristics relevant to trustworthy AI, including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. Their importance varies by context, and trade-offs can arise; considering the characteristics separately does not itself establish that a system is trustworthy. See the NIST AI RMF FAQs.
| Risk area | Questions and evidence to consider | Possible assessment focus |
|---|---|---|
| Validity and reliability | Does the system perform the intended task under expected conditions? Where and how often does it fail, and how consequential are those failures? | Use-case-specific performance, edge cases, changing conditions, and whether outputs are dependable enough for the task. |
| Safety | Could an output or action cause physical, psychological, financial, or other harm? What is the severity and plausible path to that harm? | Hazard scenarios, safeguards, escalation, fail-safe behavior, and recovery. |
| Fairness and harmful bias | Do outcomes or error patterns differ across relevant groups? Could the workflow create unequal access or impact? | Subgroup performance, disparate impacts, data limitations, and review by people familiar with affected populations. |
| Privacy and data handling | What personal or sensitive information enters, is retained, or may be exposed through outputs or connected services? | Data flows, access, retention, disclosure risks, and applicable privacy requirements. |
| Security and resilience | Can the system or its dependencies be manipulated, abused, disrupted, or accessed without authorization? | Threat scenarios, abuse resistance, access controls, dependencies, incident handling, and continuity plans. |
| Transparency, explainability, and accountability | Can users understand the system’s role and limitations? Can responsible staff investigate an outcome and identify who is accountable? | Disclosure, auditability, records, explanations appropriate to the decision, and clear responsibility. |
| Human control and fallback | Can a person intervene meaningfully, and is there a workable alternative if the AI is uncertain, unavailable, or wrong? | Human review design, authority to override, escalation, and fallback behavior. |
When comparing candidate systems, assess them on the same task and operating assumptions. Include integration and vendor dependency, monitoring needs, and the total effort required for mitigation and oversight alongside model performance. Explain why the selected evidence and thresholds are adequate for the deployment’s stakes.
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Additional checks for generative AI
Generative systems require testing beyond whether an answer sounds plausible. Assess confabulation or inaccurate output, harmful content, information integrity and provenance, privacy and intellectual-property exposure, harmful bias, and adversarial or malicious use. The NIST Generative AI Profile recommends reviewing and testing generated content against predefined guidance and documenting training-data sources for provenance where applicable. Identify who verifies outputs before they are acted on, and test how the system behaves when a prompt or input is misleading, sensitive, or outside scope.
What records should the organization keep?
Maintain a versioned assessment record that makes the decision traceable and reviewable. Retain the materials relevant to the system and its risk, including:
- Purpose, system boundaries, intended users, and affected groups.
- Data and model provenance where available, plus vendor and dependency information.
- Impact, threat, and failure-mode analyses.
- Test plans, results, limitations, and the rationale for chosen thresholds.
- Risk ratings, mitigations, residual risks, approvals, and recorded dissent.
- Human oversight design, monitoring metrics and thresholds, and review dates.
- Incident response, escalation, fallback, and rollback procedures.
For each material risk, connect an accountable owner to a control and evidence that the control works. A documented assessment is useful only if it stays aligned with the deployed system and the conditions in which people rely on it.
EU AI Act timing is role- and category-dependent
The European Commission reports that the AI Act became applicable on August 2, 2026, subject to exceptions; provider obligations for general-purpose AI models became applicable in August 2025. Following the AI Omnibus agreement, the Commission reports later application dates for certain high-risk use cases—December 2, 2027—and for relevant systems embedded in regulated products—August 2, 2028. These dates concern specified categories and obligations, not every AI system in the same way. Consult the Commission’s current AI Act overview and verify the law and guidance for the actual system, organizational role, and deployment before acting.
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