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Assess the system people will actually use
A model’s risk depends on more than its technical capability. The same model may present different risks when used for different purposes, by different people, or in workflows with different consequences. Set the assessment boundary before choosing tests or controls.
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- Model: Identify the model and version, its capabilities, known limitations, and how it was developed or obtained.
- Application: Include prompts, retrieval or other connected components, tools, interfaces, data flows, access controls, and output handling.
- Deployed workflow: Record who operates the system, who relies on its output, where human review occurs, and what decisions or actions may follow.
Describe the intended purpose in operational terms. Include the deployment setting, users and their ability to assess outputs, affected people, input and output data, integrations, degree of automation, and consequences if the system is wrong, biased, unavailable, manipulated, or misunderstood. Also consider foreseeable use outside the intended purpose.
Use a lifecycle framework without confusing guidance with law
NIST’s AI Risk Management Framework (AI RMF) is voluntary, cross-sector guidance. Its four functions—Govern, Map, Measure, and Manage—provide a useful structure for organizing accountability, context, evaluation, and response. NIST’s Playbook offers suggested actions to support the framework’s outcomes; it is not a prescribed checklist. NIST marks the AI RMF as under revision, so consult its official materials for current status.
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For generative AI, NIST AI 600-1 supplements the AI RMF with generative-AI-focused risks and suggested actions, including attention to content provenance, pre-deployment testing, and incident disclosure. NIST SP 800-218A adapts secure software development practices to generative AI and dual-use foundation model development and acquisition, for model and system producers and acquirers.
| Reference | Legal force and scope | Useful role in an assessment |
|---|---|---|
| NIST AI RMF 1.0 | Voluntary, cross-sector risk-management guidance; NIST says it is under revision. | Organize work around Govern, Map, Measure, and Manage. |
| NIST AI 600-1 | Generative AI profile that supplements the AI RMF with GAI-focused risks and suggested actions. | Consider generative-AI issues such as provenance, pre-deployment testing, and incident disclosure. |
| NIST SP 800-218A | Security-development companion for generative AI and dual-use foundation model development and acquisition. | Bring secure development practices into model and system development or acquisition. |
| EU AI Act, Article 9 | Binding requirement only for covered high-risk AI systems within the Act’s applicable scope. | For covered systems, address the legal risk-management and testing duties, alongside other applicable obligations. |
The EU AI Act is not a universal requirement for every model or deployment. Applicability depends on the system’s classification, the operator’s role, jurisdiction, and relevant dates. The consolidated Regulation (EU) 2024/1689 text dated 27 July 2026 is the relevant legal reference here; consult current official implementation material and determine whether the specific system and organization are in scope. Distinguish provider, deployer, and other operator roles rather than assuming every party has the same duties.
Run an AI risk assessment before deployment
1. Assign owners and define the release boundary
Name a business owner and a technical owner. Identify who can approve the deployment, who can accept residual risk, and who has authority to block or pause release. Record the model and version, system components, provider and deployer roles, interfaces, human review points, intended purpose, and boundaries of approved use.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →2. Map hazards, affected people, and consequences
For each plausible hazard, describe its cause and the people or organizations that could be affected. Consider technical failure, biased or harmful outputs, privacy or security exposure, organizational misuse, and harms caused by automation or over-reliance. Include failures of availability, manipulation, and misunderstanding where relevant to the use case.
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Use a risk register that connects each hazard to its cause, affected party, plausible consequence, existing controls, accountable owner, and disposition. Set risk tolerance before interpreting evaluation results. Avoid relying on one aggregate score if it could hide a severe failure mode. These fields are a practical working format, not a mandated NIST form.
3. Set an evaluation plan and decision criteria
Choose tests that reflect the intended purpose and consequences. Depending on the system, include representative cases, edge cases, subgroup checks, adversarial tests, and simulations of real operating conditions. Define metrics and acceptable thresholds before examining results; record test data, assumptions, limitations, and who reviewed the evidence.
For EU AI Act high-risk systems, Article 9 requires testing with prior-defined metrics and probabilistic thresholds appropriate to the intended purpose. It provides that testing is to be performed, as appropriate, during development and, in any event, before the system is placed on the market or put into service. This is a rule for the covered category, not a global testing mandate.
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4. Reduce risk and state operating conditions
Prefer changes to the design or intended use that prevent or reduce harm. For remaining risks, choose controls suited to the workflow, such as limiting access, constraining outputs, adding escalation paths or human review, giving users clear instructions, and providing a way to roll back or stop the system. Assign someone to maintain each control and make its operating conditions clear to users.
For covered EU high-risk systems, Article 9 describes eliminating or reducing risks as far as technically feasible through design, adding mitigation controls for risks that remain, and providing deployers with appropriate information and training. Do not assume that a human reviewer is an effective safeguard unless the reviewer has the information, capability, time, and authority needed for the role.
5. Make and document a go/no-go decision
Document the evidence reviewed, unresolved risks, required safeguards, operating limits, and the person accepting any residual risk. Release only if the results meet the pre-set criteria and the controls can be operated as planned. If a severe risk remains outside tolerance or the evidence is inadequate, delay deployment, narrow the intended use, add safeguards, or choose another approach.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor the deployed system and reassess after change
Before release, decide what to monitor, who responds, how incidents are recorded, and what conditions require escalation or suspension. Monitoring should reflect the system’s risks and applicable obligations; it may include output quality, control failures, user reports, and incidents relevant to the intended purpose.
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Revisit the assessment when a material condition changes—for example, the model or data, prompts, connected tools, user population, workflow, or intended purpose. A prior evaluation may no longer support the same release decision when the system or its operating context has changed. NIST’s generative AI profile includes incident disclosure and treats risk as lifecycle work; specific monitoring duties depend on the system and applicable rules.
Quick Recap
References
- NIST, AI Risk Management Framework and AI RMF Playbook: voluntary framework organized around Govern, Map, Measure, and Manage; the Playbook is based on AI RMF 1.0, released in 2023.
- NIST AI 600-1, Generative AI Profile, published in 2024.
- NIST SP 800-218A, Secure Software Development Practices for Generative AI and Dual-Use Foundation Models.
- Regulation (EU) 2024/1689, including Article 9 on risk management for high-risk AI systems; consolidated text dated 27 July 2026.
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