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What should an AI risk evaluation cover?
The object of evaluation is the deployed system: the model, data, interfaces, human decisions, connected services, operating conditions, and the consequences of its outputs. A model score alone cannot show whether a particular deployment is appropriate.
The National Institute of Standards and Technology (NIST) describes the purpose of its AI RMF 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 framework spans design, development, use, evaluation, and deployment; its four functions are connected activities, not a one-time final test.
There is no universal test suite, risk score, or pass threshold that settles every deployment decision. Set criteria for the particular use, compare evidence against those criteria, and make uncertainty and residual risk visible.
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How to evaluate an AI system before deployment
1. Define the deployment and its boundaries
Write down what the system is for and what it is not for. Record the workflow it enters, where its outputs go, and what decisions or actions they may influence. Include the product and its operating process, not only the model.
- Identify intended users and the people or groups affected, including people who may never interact directly with the system.
- Describe operating conditions, expected volume, human involvement, and what happens when the system is unavailable or produces an unusable result.
- List input and output data, their provenance and quality, and any upstream models, vendors, or other dependencies.
- Note foreseeable misuse, changes after launch, and assumptions the evaluation depends on.
- Clarify the expected benefits as well as potential harms, so tests can address whether the system achieves its purpose without unacceptable consequences.
Tailor the scope to the application and the organization’s requirements, resources, and risk tolerance. NIST presents the AI RMF as adaptable rather than a fixed set of required actions.
2. Assign governance and decision authority
Name a business owner and the people accountable for evaluation, security, privacy, legal review, operations, incident response, and approval. A review is not actionable if no one owns its findings.
- Specify who can approve, limit, pause, or stop deployment.
- Set a process for exceptions, including who approves them and what evidence is required.
- Assign owners and due dates to mitigations and unresolved risks.
- Define which changes—such as a new purpose, data source, model, vendor, user group, or workflow—require reassessment.
The AI RMF’s Govern function provides an organizing structure for accountability. The framework itself is voluntary; laws, contracts, or sector-specific requirements may impose separate obligations.
3. Map trustworthiness concerns and possible harms
Use the deployment context to identify how the system could fail or affect people. Make assumptions explicit, including who is represented in available data and who may be left out.
- Performance and reliability: Could errors, unstable results, or out-of-scope use cause consequential decisions or service failures?
- Safety: Could an output or downstream action create physical, financial, or other harm?
- Security and resilience: Could an attacker, malicious input, or dependency failure alter behavior, expose information, or disrupt service?
- Privacy: Is personal or sensitive information processed, exposed, inferred, or retained in ways that matter to the use?
- Bias and accessibility: Could performance or access differ across affected groups, or could people with accessibility needs be excluded?
- Human interaction: Will users understand the system’s role, limitations, and outputs? Could they over-rely on it or be unable to challenge its recommendation?
- Transparency and explainability: Can relevant people understand enough about the system and its outputs to use, review, or contest them appropriately?
- Misuse and wider effects: Could the system be used for a different purpose, or create effects beyond the immediate user and organization?
These prompts reflect NIST’s trustworthiness characteristics: validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and harmful-bias management. They help organize inquiry; a checklist alone does not demonstrate that a system is trustworthy.
4. Turn requirements into tests and thresholds
Before reviewing results, translate the deployment’s requirements into measurable questions and decision criteria. Define what counts as an acceptable result, what evidence is insufficient, and which failures require mitigation or a no-go decision. A threshold should reflect the consequences of error in this use case rather than a generic benchmark.
Use representative data and realistic workflows. Record the test data, methods, assumptions, results, limitations, and enough detail to reproduce the evaluation. Depending on the system and its risks, examine:
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- Overall performance and, where relevant, results for affected subgroups.
- Expected and edge-case inputs, failure modes, and behavior outside the intended scope.
- Robustness under realistic operating changes and dependency failures.
- Security threats, privacy leakage, and the effectiveness of relevant safeguards.
- Accessibility and how users understand, rely on, or override system outputs.
- Whether mitigations actually improve results when the system is retested.
For generative AI, add use-relevant tests for unsupported or fabricated outputs, harmful content, misuse, prompt attacks, and downstream effects. Do not assume that a model’s fluent answer is evidence that it is correct or suitable for the workflow.
5. Combine methods rather than relying on one test
Different evaluation methods reveal different risks. NIST’s AI RMF Evaluation Program (ARIA) planning manual describes a holistic approach combining model testing, red teaming, and user testing. NIST’s TEVV-Athlon framework is intended to be customized to evaluation objectives and to collect evidence about performance and impact.
| Method | What it can help reveal | What to check when using it |
|---|---|---|
| Model testing | Capability and performance on defined tasks and test data. | Whether data, metrics, and cases reflect the intended use and affected groups; whether limitations are documented. |
| Red teaming | Potential misuse, adversarial behavior, or weaknesses in safeguards. | Whether the scenarios reflect credible threats and whether findings lead to mitigation and retesting. |
| User testing | How people interact with the system and interpret or act on its outputs. | Whether participants and workflows represent the intended setting, including oversight and accessibility needs. |
| Privacy, security, or impact-focused assessment | Risks that may not show up in a task-performance score, such as information exposure or effects on individuals. | Whether the assessment addresses the actual data, system boundary, people affected, and applicable obligations. |
When choosing or combining methods, check whether the test environment resembles intended use, which people and edge cases are represented, how results are measured and independently reviewed, whether the process can be reproduced, and how findings connect to launch decisions and monitoring.
6. Decide, mitigate, and document residual risk
Compare evidence with the criteria set before testing and with applicable legal, contractual, and organizational obligations. If evidence is inadequate or residual risk is unacceptable, constrain the system, add safeguards or human review, delay launch, or decline deployment.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsKeep a decision record that identifies the evidence considered, uncertainties, unresolved risks, mitigation owners, approval authority, and conditions that require reassessment. NIST’s framework is flexible and voluntary; it does not supply a universal score or pass line.
7. Monitor after launch and reassess when conditions change
Deployment changes the environment in which the system operates. Establish a monitoring plan that tracks performance drift, incidents, complaints, changes in data or context, security events, and whether human oversight works in practice.
- Set alert thresholds and name who reviews alerts.
- Define escalation, incident handling, rollback, and suspension conditions.
- Set a reassessment cadence and triggers, including material system or workflow changes.
- Use complaints, observed failures, and post-launch outcomes to revisit assumptions and mitigations.
NIST places trustworthiness considerations across the AI lifecycle. For high-risk AI systems in the EU, the European Commission describes continuing provider and deployer monitoring and action on identified risks or serious incidents.
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NIST AI RMF and generative AI guidance
NIST AI RMF 1.0 was released on January 26, 2023, for voluntary use. It organizes outcomes under Govern, Map, Measure, and Manage and is intended for AI products, services, and systems. NIST says the framework is being revised, so check NIST’s current AI RMF page for a newer edition before relying on version 1.0 as current.
NIST’s AI Resource Center reports that more than 240 organizations contributed to the framework’s development over 18 months. That figure describes how the framework was developed; it is not evidence that a particular evaluation method reduces risk or that a system is safe.
NIST’s Generative AI Profile, issued July 26, 2024, is a cross-sector companion to AI RMF 1.0. It describes generative-AI risks and suggested actions across the four functions, making it a relevant starting point when the deployment uses generative AI.
NIST ARIA and TEVV-Athlon
NIST’s ARIA Evaluation Planning Manual, dated September 18, 2026, describes evaluation through model testing, red teaming, and user testing. NIST announced an initial public draft of TEVV-Athlon on August 7, 2026, with comments sought through October 6, 2026. Because that comment period has ended, check NIST for a later publication before treating the draft’s status as current.
European Union: AI Act duties depend on role and system category
The European Commission’s AI Act FAQ says providers must complete a conformity assessment for high-risk AI systems before placing them on the EU market or putting them into service. It describes deployer duties that include using the system according to instructions, monitoring it, responding to identified risks or serious incidents, and assigning human oversight to people with the necessary competence and authority.
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The Commission 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, the FAQ says this can be carried out alongside a required data-protection impact assessment.
The Commission’s high-risk guidance page reports updated application dates of December 2, 2027, for specified high-risk areas and August 2, 2028, for AI integrated into certain products. The exact category matters, and implementation dates or guidance may change; verify the Commission’s current material for the system in question.
The Commission states that Article 50 transparency obligations apply from August 2, 2026, for certain interactive AI systems and AI-generated content. Whether a particular provider or deployer is covered depends on the obligation’s scope and exceptions in current Commission guidance.
United Kingdom: assess whether a DPIA is required
The UK Information Commissioner’s Office (ICO) says Article 35 of the UK GDPR requires a data protection impact assessment (DPIA) when personal-data processing—particularly using new technologies—is likely to result in high risk to individuals, and advises carrying it out before processing. This is a trigger based on the processing and risk, not a rule that every AI deployment automatically requires a DPIA.
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What should the launch decision say?
A useful approval is specific to the evaluated deployment, not a blanket statement that an AI model is “safe.” Record the scope, criteria, evidence, limitations, residual risks, and conditions under which the approval remains valid. A practical decision record should answer:
- What purpose and workflow were evaluated, and which people may be affected?
- Which tests were run, what did they show, and where is the evidence incomplete?
- Which risks remain, who owns them, and what mitigations or controls are required?
- Who approved the deployment, and who has authority to restrict or stop it?
- What events will trigger monitoring escalation, rollback, or reassessment?
These answers let operators act on the evaluation after launch and make clear when a change in purpose, system, data, or operating conditions invalidates the original decision.
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