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Before deploying an AI model, assess the complete system in its intended setting: define what it will do, who may be affected, how it performs under representative conditions, what controls reduce material risks, and who is accountable for residual risk. Make a documented go, conditional-go, or no-go decision—and plan to monitor and reassess the system after launch. No checklist guarantees safety or legal compliance.
What exactly are you evaluating?
Assess the AI-enabled system, not just the model in isolation. A model’s behavior depends on its version, data, connected tools, user workflow, degree of automation, and operating environment. A model that performs acceptably in a test may behave differently when staff rely on it, when inputs change, or when it is connected to other systems.
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Start a deployment record that captures:
- The model and version, whether it is internally developed or supplied by a third party, and the tools, data sources, and integrations it uses.
- The intended purpose, the tasks it may and may not perform, its users, the people affected by its outputs, and the settings in which it will operate.
- How much autonomy it has, where human review occurs, and what happens when an output is wrong, unavailable, or outside the system’s limits.
- The expected benefit, plausible alternatives, known assumptions, and uncertainties. Include the option of not using AI.
This context-first approach follows the Map function in NIST’s AI Risk Management Framework (AI RMF). NIST describes Map as providing contextual knowledge to inform an initial go/no-go decision; it is not a substitute for the later work of measuring and managing risk.
Who owns the assessment and decision?
Assign one accountable decision-maker and bring together the people who understand the system, the work it supports, and the consequences of failure. Depending on the use, that may include product or operations staff, technical teams, security and privacy specialists, legal or compliance staff, and representatives of affected groups.
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Set the organization’s risk tolerance and review process before testing. Establish who can approve deployment, who is responsible for each control, how findings are documented, and who can pause or retire the system. Include supplier and third-party dependencies in the review rather than treating them as outside the organization’s responsibility.
NIST’s AI RMF organizes risk management into four functions: Govern, Map, Measure, and Manage. Govern is cross-cutting: roles, policies, accountability, documentation, and review should continue throughout the system lifecycle. The framework is voluntary guidance, released in version 1.0 on January 26, 2023; it does not itself establish that a deployment meets legal requirements. NIST’s framework page has reported a revision in connection with the White House AI Action Plan, so check NIST’s current status and materials when adopting the framework.
Which harms and benefits matter in this setting?
List who could benefit and who could be harmed, including people who are not direct users. Consider plausible errors, misuse, over-reliance, and failures in the surrounding workflow—not only whether the model produces a technically correct answer.
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- Accuracy, reliability, and the consequences of incorrect or missing outputs.
- Fairness, exclusion, and differences in performance or impact across affected groups.
- Privacy, data protection, and security, including exposure through connected tools or data.
- Transparency, explainability, and whether users can understand the system’s limits well enough to use it appropriately.
- Human oversight, automation bias, and whether staff can recognize, question, and override an output.
- Safety, resilience, foreseeable misuse, and environmental or broader societal effects where relevant.
Record the assumptions behind each assessment and what remains uncertain. A general benchmark cannot establish that a system is suitable for a specific organization, population, or workflow.
What evidence should testing produce?
Set task-specific acceptance criteria before running tests. Use data and test conditions suited to the proposed deployment, and document the experimental design so decision-makers can understand what the results do—and do not—show.
Depending on the system and its risks, evaluate performance, robustness, failure modes, subgroup effects, privacy and security, and human-system interaction. Record limitations, uncertainty, and cases where the system should defer to a person or fail safely. Test the complete workflow where possible: a model score alone may not reveal how people interpret or act on its output.
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What to add for generative AI
For generative AI, use the base AI RMF alongside NIST AI 600-1, the Generative AI Profile released July 26, 2024. The profile addresses risks unique to or amplified by generative AI and aligns suggested actions with the same four risk-management functions. Tailor those actions to the system’s purpose, risk tolerance, and resources; using the profile alone does not resolve every risk or establish that a system is safe.
How do you choose among models or suppliers?
Compare candidates against the same deployment-specific criteria, not against marketing claims or a single headline benchmark. The following comparison axes are a practical synthesis of NIST’s context and trustworthiness approach and OECD testing and due-diligence guidance; they are not a published ranking.
| Comparison axis | Question to answer |
|---|---|
| Fit to purpose | Does the system support the defined task and operating conditions, with clear limits? |
| Performance and uncertainty | How does it perform on suitable, representative conditions, and where are results uncertain or uneven? |
| Potential impact | Who may be affected, and how serious could an error or misuse be? |
| Privacy and security | What data and integrations are involved, and what protections and dependencies apply? |
| Oversight and usability | Can users understand limitations, intervene when needed, and use the system without inappropriate reliance? |
| Evidence and controls | Is test evidence adequate for the intended use, and can material risks be reduced with controls that have owners and evidence of effectiveness? |
| Operations and legal fit | Can the organization monitor, handle incidents, and meet requirements that apply to its role, use, and jurisdiction? |
| Residual risk | After controls, is the remaining risk within the organization’s approved tolerance? |
How should you mitigate risk and make the deployment decision?
For every material risk, record the proposed control, its owner, how its effectiveness will be checked, and a fallback if it fails. Depending on the risk, responses may include narrowing the use, restricting access, improving data or evaluation, adding human review, informing users, adding safeguards, monitoring outputs, delaying deployment, or rejecting the use.
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- Go: the evidence supports the proposed use and residual risks are within tolerance.
- Conditional go: deployment is allowed only after specified controls or limits are in place, with named owners and follow-up checks.
- No-go: the evidence is insufficient, a material risk is not acceptably controlled, or the use is outside the organization’s tolerance.
NIST frames risk management as iterative: the context established in Map supports an initial decision, while Measure and Manage work continues. A sign-off should therefore identify the approved purpose and conditions, not serve as an unrestricted endorsement of the model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What must be ready before launch—and after it?
Before launch, confirm that the approved system, version, workflow, controls, and responsible owners match the assessment. Make sure users know the system’s intended use and limits, that escalation routes work, and that the organization can pause or roll back the deployment if necessary.
Define what will be monitored, who reviews it, how often review occurs, and what happens when results or reports raise concern. Choose indicators that reflect the actual task and potential harms; a generic accuracy measure may not reveal an emerging problem. Provide a route for users or affected people to report issues, and define incident escalation and response responsibilities.
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How do frameworks relate to legal obligations?
Use the AI RMF as voluntary risk-management guidance, not as a legal classification or proof of compliance. Screen applicable obligations separately for the organization’s role, the system’s intended purpose, and the jurisdictions where it is developed or used.
For an EU-related deployment, assess the system under the EU AI Act and consult relevant European Commission guidance on whether it is high-risk. The Commission’s guidance is intended to help providers and deployers make that assessment. The Act’s surfaced consolidated text states that technical documentation for high-risk AI must be prepared before the system is placed on the market or put into service and kept up to date. Classification, transition dates, and duties depend on the actual case and the current law; obtain appropriate legal advice rather than treating a general framework checklist as a determination.
The OECD’s 2026 Due Diligence Guidance for Responsible AI frames responsible practice as embedding policy and management systems, identifying and assessing adverse impacts, preventing and mitigating them, tracking results, communicating actions, and providing or cooperating in remediation where appropriate. Its examples support due diligence across the AI value chain; they are not an exhaustive checklist or a claim that different frameworks are equivalent.
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