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Before putting an AI model into operation, define what it will do, who may be affected, and what could go wrong; test those risks against criteria chosen in advance; reduce risks that exceed your tolerance; and document who approves any remaining risk. Then monitor the deployed system and reassess it when important conditions change. The assessment should cover more than the model: an application built around it and the workflow that relies on its outputs can introduce risks of their own.
Decide what you are assessing
“The AI” can mean several different things, and each has a different boundary. A model may behave acceptably in isolation while an application exposes sensitive inputs, connects it to tools, or presents uncertain outputs as authoritative. A workflow may create additional risk if people act on those outputs without checking them.
- Model: the model version, its capabilities and limitations, and how it responds to relevant inputs.
- Application: the interface, prompts, retrieval or other data sources, integrations, access controls, and safeguards surrounding the model.
- Deployed workflow: the people, policies, decisions, and downstream actions that determine how outputs are used and who bears the consequences.
Record which of these are in scope and how they connect. Also identify the provider, deployer, and other relevant operators. Their responsibilities may differ, especially under applicable law.
Choose a framework that fits the job
NIST guidance can provide an operational structure; it is not itself a law. EU AI Act Article 9 sets legal requirements for risk management of covered high-risk AI systems. These sources are complementary rather than competing checklists.
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| Framework or provision | What it is | How to use it |
|---|---|---|
| NIST AI Risk Management Framework (AI RMF) 1.0 | Voluntary, cross-sector guidance organized around Govern, Map, Measure, and Manage. NIST says the framework is under revision. | Use its functions to organize ownership, context mapping, evaluation, and ongoing risk response. The NIST Playbook gives suggested actions, not a mandatory form. |
| NIST AI 600-1, Generative AI Profile | A 2024 NIST profile that supplements the AI RMF with generative-AI risks and suggested actions. | Use it alongside the RMF when the system uses generative AI, with attention to issues including content provenance, pre-deployment testing, and incident disclosure. |
| NIST SP 800-218A | A security-development companion adapting secure software development practices to AI, including generative AI and dual-use foundation models. | Use it to inform secure development and acquisition practices for the model and surrounding system. |
| EU AI Act, Article 9 | A legal risk-management requirement for AI systems classified as high-risk and within the Act’s scope. | Determine whether the system, your role, and the relevant jurisdiction bring it within scope before treating its requirements as applicable. It is not a universal rule for every model. |
The EU law references below are to Regulation (EU) 2024/1689, including a consolidated text dated 27 July 2026. Applicability depends on the Act’s scope, system classification, operator role, and relevant dates; consult current official implementation material for a particular deployment.
Run a pre-deployment assessment
1. Assign owners and define the release boundary
Name a business owner responsible for the use case and a technical owner responsible for the system. Identify who can block release and who has authority to accept residual risk. Define the model and version, interfaces, data flows, connected tools, human review points, and intended use. Include the operational setting: what users can do, what support or training they receive, and what happens when the system is unavailable.
2. Map intended use, affected people, and foreseeable misuse
Describe the purpose in operational terms rather than relying on a broad label such as “assistant” or “decision support.” State who enters information, who sees the output, who acts on it, and who may be affected without using the system directly. Note whether the output informs a consequential decision or triggers an action automatically.
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List plausible failure and misuse scenarios. Consider inaccurate, biased, harmful, or unavailable outputs; manipulated inputs; privacy or security exposure; and outputs that are misunderstood or trusted too readily. For generative AI, consider provenance of content, testing before deployment, and how incidents will be disclosed or handled. Include consequences for people, the organization, and connected systems.
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For each risk, record the hazard, its cause, affected parties, plausible consequence, existing controls, accountable owner, and the decision needed. Separate distinct sources of risk—for example, model error, insecure integration, inappropriate organizational use, and over-reliance on automated output—so one does not hide another.
Set your tolerance before reviewing test results. Assess likelihood and severity with explicit assumptions, and explain uncertainty where evidence is limited. A single combined score can conceal a low-likelihood but severe failure mode; retain the underlying scenarios and use them to guide release decisions.
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4. Test against the intended use and a prior decision rule
Build an evaluation plan around the system’s purpose and the consequences of failure. Define measures and acceptance thresholds before testing, then use cases that represent expected operation as well as boundary conditions. Depending on the use case, include subgroup checks, adversarial inputs, and simulations of the real workflow, including handoffs to people and connected tools.
- Record the test data, system configuration, model version, measures, thresholds, results, and known limitations.
- Check whether the evidence covers the users, settings, and affected groups identified in the assessment.
- Make results understandable to the people with release authority, not only to the evaluation team.
- Test the application and workflow as well as the model where integration or human behavior can change the outcome.
For covered high-risk systems, EU AI Act Article 9 requires testing, as appropriate, during development and in any event before market placement or putting into service. It calls for prior-defined metrics and probabilistic thresholds appropriate to the intended purpose. That specific legal requirement should not be generalized to systems outside the provision’s scope.
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Prefer changes that prevent or reduce harm at its source. Depending on the risk, that may mean narrowing the intended use, changing the design, limiting access or inputs, constraining outputs, adding escalation paths or human review, improving user instructions, or providing a way to disable or roll back the system. A control is useful only if it works in the actual operating context and someone owns it.
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Record residual risks, required operating conditions, unresolved evidence gaps, and the person accepting the remaining risk. Release only when the evidence and controls support the stated use and the residual risk is within the organization’s tolerance. If a severe risk remains outside tolerance or the evidence is inadequate, delay deployment, restrict the use, or choose another approach.
For systems covered by EU AI Act Article 9, the provision describes eliminating or reducing risks as far as technically feasible, addressing remaining risks with mitigation and control measures, and providing deployers with appropriate information and training. These duties apply in that legal context, not automatically to all AI deployments.
6. Monitor operation and revisit assumptions
Before release, decide what signals will be monitored, who reviews them, how incidents are recorded and escalated, and who can pause or change the system. Monitoring should fit the risks and applicable obligations; it may include output quality, user feedback, failures, security events, or patterns of use that differ from the intended purpose.
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Reconsider the assessment when material conditions change. Triggers can include a model or data change, new prompts or connected tools, a different user population, a changed purpose, or a new operating context. Reuse earlier evidence only where it still applies, and document the basis for that judgment. NIST’s lifecycle-oriented guidance and its generative AI profile support ongoing risk management; the specific monitoring plan remains system- and context-dependent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a defensible release record contains
A concise record should let another responsible person understand what was assessed, what evidence supports the decision, and what must remain true for the deployment to be acceptable. Include:
- Purpose, system boundary, model and version, deployment context, roles, and affected parties.
- Risk scenarios, assumptions, likelihood and severity judgments, existing controls, owners, and tolerance criteria.
- Evaluation plan, pre-set metrics and thresholds, test configuration and data, results, limitations, and unresolved gaps.
- Mitigations, required operating conditions, residual risks, approval or rejection decision, and the person accountable for acceptance.
- Monitoring and incident-response responsibilities, plus triggers for reassessment.
This record is a practical governance aid, not a form prescribed by NIST or a substitute for any documentation required by applicable law.
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