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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesBefore deploying an AI model, assess the complete system in its intended setting—not just its benchmark scores. Document who will use it, who may be affected, what can go wrong, and what evidence shows it meets task-specific requirements. Then assign owners, reduce material risks, record any risk that remains, and approve, conditionally approve, or reject the deployment. Keep assessing after launch: a go-live decision is not a one-time safety guarantee.
1. Define the proposed system and its purpose
Start by describing what people will actually encounter. A model rarely operates alone: it may be connected to company data, software tools, prompts, human review, and decisions or services. Assess that full AI-enabled system and workflow, not only the model in isolation.
Record the model and version, whether it is internally developed or supplied by a third party, connected tools and data, degree of autonomy, intended purpose, users, affected groups, operating environment, and what happens when an output is wrong, unavailable, or acted on without review. Include the expected benefit and plausible non-AI alternatives; sometimes the right decision is not to use a model.
NIST’s AI Risk Management Framework (AI RMF) calls this contextual work part of its Map function. Context should be sufficient to support an initial go/no-go decision before the organization commits to deployment.
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2. Establish governance and accountability
Before testing begins, name a person or body accountable for the deployment decision and assemble the expertise needed to assess it. Depending on the system, that can include product or process owners, engineering, security, privacy, legal, compliance, accessibility, and representatives familiar with affected users. An assessment without a decision-maker or owners for follow-up actions is difficult to act on.
Set out the operating rules for the assessment and for the system’s lifecycle:
- Who can approve deployment, accept residual risk, require changes, or stop use?
- What risk tolerance and task-specific acceptance criteria apply?
- Who is responsible for human oversight and for responding when a person challenges an AI-assisted decision?
- How will model, supplier, data, and system versions be inventoried and documented?
- How often will performance and impacts be reviewed, and what events require an earlier review?
- What conditions trigger a pause, rollback, or retirement?
- What evidence and controls are required from suppliers and other third parties?
NIST’s AI RMF treats Govern as a cross-cutting function: governance responsibilities continue throughout the lifecycle rather than ending at launch. The framework is voluntary guidance, not a substitute for applicable law.
3. Map benefits, affected people, and foreseeable harms
List the expected benefits alongside possible adverse impacts, including impacts on people who are not the system’s direct users. Consider the actual setting and workflow: who supplies inputs, who sees outputs, who makes the final decision, and who bears the consequences if the system fails or is unavailable?
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Choose risks that fit the use case rather than mechanically applying a generic checklist. Potential areas include:
- Performance and reliability: inaccurate, incomplete, inconsistent, or out-of-scope outputs; failures under unusual conditions; and the effect of system downtime.
- Fairness and access: materially different outcomes for relevant groups, exclusion, or barriers to challenging or correcting an AI-assisted result.
- Privacy and security: exposure or misuse of personal or confidential information, unauthorized access, and threats to connected tools or data.
- Human factors: over-reliance, automation bias, unclear responsibility, or an oversight process that cannot realistically catch errors.
- Transparency and explainability: whether users and affected people receive information they need to understand the system’s role and act on or contest its output.
- Misuse and wider effects: foreseeable use outside the intended purpose and relevant safety, environmental, or societal impacts.
For each material risk, note the assumptions behind the assessment and what is uncertain. A strong result on a general benchmark does not establish that a system is appropriate for a particular organization, population, or decision.
4. Measure performance and risk in the intended context
Decide what counts as acceptable performance before running evaluations. Criteria should be tied to the task and its consequences: a tolerable error rate for a low-impact drafting aid may be unacceptable for a system that influences access to an important service. Specify how errors, uncertainty, edge cases, and human intervention will be assessed.
Use data and test conditions that are suitable for the proposed setting. Keep a record of the evaluation design, data availability and limitations, validation method, results, and uncertainty. Depending on the use, evaluate accuracy, representativeness, robustness, subgroup effects, failure modes, privacy, security, and the interaction between people and the system. OECD guidance emphasizes examining test and evaluation evidence—including experimental design, data suitability and representativeness, accuracy, trustworthiness, and whether the evaluation measures the intended construct.
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Testing should reflect the complete workflow, not just an isolated model response. Check what happens when inputs are missing or unusual, when a user disagrees with an output, when a human reviewer is under time pressure, and when the system or a dependency is unavailable. Record material limits that users or decision-makers need to understand.
Additional assessment for generative AI
For a generative AI system, use the base AI RMF together with NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1), released in 2024. The profile addresses risks unique to or amplified by generative AI and organizes suggested actions around the same four functions. Tailor those actions to the system’s purpose, risk tolerance, and available resources; using the profile is not, by itself, proof that a system is safe or fit for deployment.
5. Mitigate risks and make a documented decision
For each material risk, identify a response, a responsible owner, and evidence that the response works. A control should address the mechanism of harm rather than merely restating a policy. Possible responses include narrowing the purpose or user group, improving data or evaluations, adding meaningful human review, restricting access, informing users, adding technical safeguards, monitoring outputs, or choosing not to deploy.
After controls are in place, assess the remaining risk against the organization’s approved tolerance. Record why it is acceptable or what further action is required. A useful decision record contains:
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- the system, version, intended use, and scope of approval;
- the evidence reviewed, evaluation conditions, and known limitations;
- material benefits and risks, including affected groups and unresolved uncertainty;
- mitigations, owners, and evidence of their effectiveness;
- residual risks and the person or body authorized to accept them;
- the decision—go, conditional go, or no-go—plus any conditions and review date.
A conditional approval should state what must be completed, by whom, and before which uses are permitted. Do not treat an unassigned action or an undocumented exception as a mitigation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Plan monitoring, incident response, and reassessment
Deployment changes the evidence available: real users, data, and operating conditions may differ from those seen in evaluation. Define how the organization will detect deterioration or harm and what it will do when a signal appears.
Set owners and procedures for monitoring performance and impact, receiving user feedback, escalating incidents, investigating causes, and pausing or rolling back the system. Decide what changes require a fresh assessment. Practical triggers include a new model or prompt, changed data or tools, a new user group, a changed purpose, unexpected behavior, a serious incident, or a relevant change in legal requirements. These triggers should be specific to the system and its risk; monitoring frequency should be proportionate to the potential impact and rate of change.
NIST describes risk management as continuous across the AI system lifecycle, while OECD due-diligence guidance emphasizes tracking results and using findings to strengthen management systems. Revisit the deployment decision when evidence or context changes, rather than assuming launch approval remains valid indefinitely.
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How the main guidance and legal sources fit together
| Source | What it contributes | Boundary to keep in mind |
|---|---|---|
| NIST AI RMF 1.0, released in 2023 | A voluntary risk-management structure organized around Govern, Map, Measure, and Manage. | It is guidance, not a legal classification or a guarantee of safety or compliance. NIST’s AI RMF page reports that the framework is being revised; check its current status when applying it. |
| NIST Generative AI Profile, AI 600-1, released in 2024 | Suggested actions for risks specific to or exacerbated by generative AI, aligned with the AI RMF functions. | It complements rather than replaces context-specific assessment, organizational risk decisions, or legal review. |
| OECD Due Diligence Guidance for Responsible AI, published in 2026 | A due-diligence approach: embed policies and management systems, identify and assess adverse impacts, prevent or mitigate them, track results, communicate actions, and provide or cooperate in remediation where appropriate. | Its implementation examples are not an exhaustive checklist and do not establish that different frameworks are equivalent. |
| EU AI Act and relevant European Commission guidance | For EU-related use, guidance can help providers and deployers assess whether an AI system is high-risk. The Act’s high-risk technical-documentation requirement calls for documentation to be prepared before placing a system on the market or putting it into service and kept up to date. | Determine the organization’s role, intended purpose, classification, applicable requirements, and current transition rules for the actual case. A framework assessment does not settle these legal questions. |
The NIST AI RMF Playbook supplies suggested actions organized around the four functions; it is also voluntary. Treat these resources as ways to structure and strengthen an assessment, not as interchangeable certifications or automatic approval routes.
If you are choosing between models or vendors
Evaluate every candidate against the same deployment-specific criteria, using evidence relevant to the planned use. A comparison can include:
- fit to the intended purpose and workflow;
- performance and uncertainty in representative conditions, including relevant subgroup results;
- potential impact severity and who may be affected;
- privacy, security, explainability, and practical human oversight;
- integration requirements and supplier or third-party dependencies;
- quality and scope of evaluation evidence;
- available mitigations, monitoring, and incident support;
- fit with legal requirements for the jurisdiction and use; and
- residual risk against the organization’s tolerance.
Compare like with like: differences in test data, task definitions, or operating conditions can make headline scores misleading. A model with stronger average performance may still be a worse choice if its limitations, integration dependencies, or oversight requirements are unsuitable for the deployment.
When is an AI system ready to deploy?
Deploy only when the organization can explain the system’s purpose and limits, has evidence that it meets pre-set requirements in relevant conditions, has assigned and tested controls for material risks, and has a named authority accepting any remaining risk. The organization should also be able to monitor outcomes, respond to incidents, and reassess when the system or context changes. If those conditions are absent—or applicable legal obligations remain unresolved—the decision should be to defer or reject deployment, not to treat a checklist as clearance.
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