Before deploying an AI system, assess the complete system in the conditions where people will use it—not just the model’s benchmark score. Define its purpose and accountable owners, map affected people and potential harms, test performance and failure modes against the intended use, mitigate and document residual risks, and set up monitoring and reassessment. NIST’s voluntary AI Risk Management Framework (AI RMF) organizes this work around Govern, Map, Measure, and Manage; legal duties depend on jurisdiction, use, and whether your organization is a provider or deployer.
What exactly are you evaluating?
Set the assessment boundary around the deployed product and workflow. A model can perform well in isolation while the larger system fails because of its data, integrations, interface, operating conditions, or the way people rely on its output.
Record the system’s intended purpose and foreseeable uses; who operates it and who is affected; its inputs, outputs, and decision consequences; the human role; operating conditions; relevant data provenance and quality; upstream models, vendors, and dependencies; and foreseeable changes after launch. Make assumptions explicit, including what the system is not intended to do.
NIST AI RMF 1.0 is intended for voluntary use across AI design, development, use, evaluation, and deployment. It can be adapted to an organization’s application, resources, requirements, and risk tolerance. NIST says it is being revised, so check whether a newer edition has appeared before relying on version 1.0 as current.
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Who is accountable for the risk decision?
Assign a business owner and name the people responsible for evaluation, security, privacy, legal review, operations, incident response, and deployment approval. Define who can pause or limit deployment, how exceptions are approved, and which changes require a new assessment. A risk review without a named decision-maker can identify problems without ensuring anyone has authority to address them.
Governance is not only a final sign-off. NIST’s Govern function is part of the framework’s organizing structure, alongside Map, Measure, and Manage. The framework itself is voluntary; separate laws, regulations, or contracts may still impose binding obligations.
Which risks and affected people should you map?
Consider the system’s benefits as well as its potential harms, and identify who could experience either. Examine intended and foreseeable uses, affected groups, the consequences of incorrect or delayed output, accessibility needs, human-AI interaction, data quality and provenance, privacy, security threats, and likely misuse. Where decisions affect people, consider whether errors or uneven performance could burden some groups more than others.
NIST’s trustworthiness characteristics offer useful prompts: validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and management of harmful bias. They are dimensions to investigate, not a checklist that by itself proves a system is trustworthy.
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How should you measure risk for the intended use?
Turn requirements into measurable questions and set thresholds before reviewing results. Use representative data and realistic workflows. Where relevant, examine overall and subgroup performance, failure modes, robustness, security, privacy leakage, accessibility, and whether people can use human oversight effectively. Record the test data, methods, assumptions, results, limitations, and enough detail to make the evaluation reproducible.
Different tests reveal different kinds of evidence. NIST’s ARIA planning approach combines model testing, red teaming, and user testing; its TEVV-Athlon framework is designed to be customized to evaluation objectives.
| Evaluation approach | What it can help examine | Practical consideration |
|---|---|---|
| Model testing | Performance and behavior against defined tasks and evaluation objectives. | Use data and conditions that reflect intended use; a model result alone does not establish whether the full deployment is acceptable. |
| Red teaming | Potential adversarial misuse and failure under challenging inputs or conditions. | Choose challenges relevant to the system’s likely exposure and record test limits and findings. |
| User testing | How people interact with the system and its outputs in practice. | Include realistic workflows and examine whether users can understand and exercise their intended oversight role. |
These approaches complement rather than replace one another. Compare methods by the risks they reveal, realism of the test environment, representation of people and edge cases, measurement and independent review, reproducibility, retesting after mitigation, and how findings feed into launch and monitoring decisions.
For generative AI, add tests where they matter to the use case: unsupported or fabricated outputs, harmful content, misuse, prompt attacks, and downstream effects. NIST’s Generative AI Profile, issued July 26, 2024, is a cross-sector companion to AI RMF 1.0 that describes generative-AI risks and suggested actions across the four functions.
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NIST’s ARIA Evaluation Planning Manual, dated September 18, 2026, describes holistic evaluation through model testing, red teaming, and user testing. NIST’s TEVV-Athlon announcement described an initial public draft on August 7, 2026, with comments sought through October 6, 2026. Because that comment period has ended, check NIST’s current publication before treating the draft as the latest status.
How should you decide whether to deploy?
Compare observed risks with the tolerances and obligations set before testing. Decide whether evidence supports deployment as designed, deployment with constraints or additional human review, further testing, or no deployment. If evidence is inadequate or residual risk is unacceptable, limit or decline deployment rather than treating a missing result as a passing result.
Document the evidence and uncertainty, unresolved risks, mitigations and their owners, approval, and conditions that trigger reassessment. Retest mitigations to see whether they address the risks without introducing new ones. NIST’s framework does not supply a universal risk score or pass threshold that applies to every AI system.
NIST’s AI Risk Management Framework FAQ describes the framework’s purpose this way: “The NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0) is intended to help developers, users and evaluators of AI systems better manage AI risks which could affect individuals, organizations, society, or the environment.” The NIST AI Resource Center says the framework was developed with more than 240 contributing organizations over 18 months. Those figures describe framework development, not the effectiveness of an individual AI system or proof that an assessment reduces risk.
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Which legal requirements apply?
Check the rules for the system’s jurisdiction, intended use, and your organization’s role. A voluntary risk framework does not replace a legal assessment, and the same system may face different duties in different jurisdictions.
European Union
The European Commission’s AI Act FAQ says providers must complete a conformity assessment for high-risk systems before placing them on the EU market or putting them into service. It describes deployer duties that include following instructions, monitoring the system, acting on risks or serious incidents, and assigning human oversight by people with the necessary competence and authority.
The Commission’s FAQ also says certain public bodies, public-service providers, and operators using high-risk AI for creditworthiness or life and health insurance assessments must conduct a fundamental-rights impact assessment. Where relevant, that assessment can be carried out alongside a required data-protection impact assessment.
The Commission’s high-risk guidance reports updated application dates of December 2, 2027, for specified high-risk areas and August 2, 2028, for AI integrated into certain products. Its guidance is time-sensitive: confirm the current dates and whether the system falls into the relevant category. The Commission states that Article 50 transparency obligations apply from August 2, 2026, for certain interactive AI systems and AI-generated content; scope and exceptions depend on the current guidance.
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The UK Information Commissioner’s Office says Article 35 of the UK GDPR requires a data protection impact assessment (DPIA) when processing personal data—particularly using new technologies—is likely to result in high risk to individuals, and advises completing it before processing. This is a trigger based on the processing risk, not a rule that every AI deployment automatically requires a DPIA.
Other jurisdictions
Check the relevant regulator and applicable law for each place where the system will be offered or used. The requirements above describe EU and UK guidance; they do not establish what is required elsewhere or determine whether a particular deployment is compliant.
What should you monitor after launch?
Assessment continues in operation. Define what to track, who reviews it, and what action follows. Include performance drift, incidents, complaints, changes in context or data, and security events. Set alert thresholds, escalation routes, incident handling, reassessment intervals, and conditions for rollback, suspension, or restricted use.
Reassess when the system, its data, its operating context, or the way people use it changes materially. NIST places trustworthiness considerations across the AI lifecycle. For EU high-risk systems, the Commission describes continuing provider and deployer monitoring and action on identified risks or serious incidents.
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