A useful AI safety audit starts by defining what the system does, where and by whom it is used, and who could be harmed—not by applying a universal checklist or pass score. Then connect each material risk to a test, preserve the evidence and limitations, and record who owns remediation and any residual-risk decision. The workflow below is practical guidance; it does not by itself establish that a system is safe or satisfy every legal requirement.
1. Define the system, its use and the audit boundary
Before testing, write down the decision the audit is meant to support: for example, whether to approve a deployment, investigate an incident, or reassess a changed system. The same model can present different risks in different settings, so identify the actual use rather than auditing an abstract product label.
- System and configuration: record the system identifier, model and software versions, configuration, relevant dependencies, data flows, and date of assessment.
- Purpose and context: describe the intended use, deployment setting, users, affected groups, relevant jurisdictions and sectors, and foreseeable misuse.
- People and controls: identify provider and deployer roles, human decision points, operational controls, accountable owners, reviewers, and who has authority to accept residual risk.
- Boundary: state what components, use cases, locations, or risks are excluded, and why. An exclusion is a scope decision, not evidence that the excluded area is safe.
Use this scope to make the audit reproducible: a later reviewer should be able to tell which system and deployment the conclusions apply to, and which they do not.
2. Build a context-specific risk register
For each plausible hazard, describe the pathway from system behavior to harm: what could happen, under what conditions, and to whom. Note existing controls and uncertainty. Do not treat every risk dimension as equally relevant in every deployment. NIST’s AI RMF FAQ explains that trustworthiness characteristics can involve tradeoffs and that their importance varies by setting.
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Consider the dimensions NIST identifies, selecting those that fit the system and explaining meaningful exclusions:
- Validity and reliability: whether outputs or decisions are fit for the stated purpose and remain dependable in relevant conditions.
- Safety: whether operation can create or contribute to harm, including when the system fails or is used outside its intended conditions.
- Security and resilience: whether the system can withstand relevant threats and recover or fail safely when disrupted.
- Accountability and transparency: whether responsibilities, system behavior, and important decisions can be understood and reviewed.
- Explainability and interpretability: whether the people who need to act on or challenge outputs can understand them sufficiently for the use case.
- Privacy enhancement: whether personal information is appropriately protected in the system’s data handling and operation.
- Fairness and harmful bias: whether outcomes create unjustified disparities or harms for affected groups in the relevant context.
Keep the register tied to actual use. A risk entry should identify the affected population and conditions, not merely name a broad category such as “bias” or “security.”
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3. Turn each material risk into a testable question
For every risk selected for examination, specify what claim or question the audit will assess, what evidence could answer it, and what result would count as concerning. Set acceptance criteria or decision rules before testing when feasible; otherwise, explain how results will be interpreted.
- Choose a method appropriate to the risk: document and data review, technical evaluation, operational-control review, or a combination.
- Cover expected operation as well as relevant edge, degraded, or adversarial conditions. Select test data and contexts that represent the deployment, and record their provenance, exclusions, and known gaps.
- For generative systems, consider prompt and output behavior, tool or retrieval boundaries where present, and how failures are handled.
- Assess subgroup or context-specific performance, security threats, and human oversight where those issues are material to the system and use.
- State what the method cannot establish. A test result on a limited dataset or scenario does not automatically generalize to every user, environment, or future version.
NIST’s AI Resource Center offers testing, evaluation, verification, and validation (TEVV) resources that can inform test planning. The particular methods above are audit practices to select according to risk; they are not a universal test list prescribed by one framework page.
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4. Execute tests and preserve the evidence
For each test, create a record while it is being run. Capture enough information for a reviewer to understand the procedure and reproduce important results where practical.
- Record the test date, tester, system and model version, configuration, and environment.
- Identify the procedure, test data or prompt-set version and provenance, metrics or decision rules, thresholds used, and any deviations.
- Preserve outputs, failures, logs, relevant artifacts, and the evidence location or stable reference. Apply access controls appropriate to sensitive data.
- For stochastic systems, describe repeat counts or sampling choices and report variability if it was measured. Do not imply a single run establishes stable behavior.
For providers of general-purpose AI models with systemic risk, European Commission guidance expressly describes documented adversarial testing as part of the relevant provider obligations. That is not a blanket requirement for every downstream AI system audit; see the Commission’s GPAI guidance FAQ.
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5. Convert results into findings and decisions
Separate what the evidence shows from what remains uncertain. In each finding, distinguish a measured failure from a plausible risk that has not been demonstrated, and from an evidence gap that prevents a sound conclusion.
A finding should give reviewers enough context to judge its significance and track its resolution:
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- A concise finding title and the evidence artifacts, test method, criteria, and system version it concerns.
- The affected users, groups, and operating contexts, plus a severity rationale and likelihood or uncertainty if assessed.
- Existing controls, the recommended mitigation, an accountable owner, and a due date.
- The residual risk after mitigation, along with the person or body that approved any exception or risk acceptance.
Do not present a severity label without explaining its basis. If a risk is accepted, record the rationale and approval rather than treating acceptance as remediation.
6. Assemble the audit record and keep it current
Organize the final package so an independent reviewer can follow the line from scope to risk, test, result, finding, and decision. A practical audit file includes:
- Executive summary, scope, system description, roles, assessment criteria, and exclusions.
- Risk register, test methods and data description, results, limitations, and evidence index.
- Findings, remediation plan, residual-risk decision, approvals, and version history.
- Monitoring and incident triggers, material-change triggers, and the next review date.
Maintain stable evidence references and an audit trail. Reassess when a material system or context change, incident, newly observed failure mode, or monitoring signal could alter the risk picture; set any additional review cadence according to risk and applicable requirements. For high-risk AI systems covered by the EU AI Act, the Act requires technical documentation to be kept up to date.
How NIST guidance, the EU AI Act and GPAI duties differ
These sources serve different purposes and apply to different actors. They are not interchangeable certifications or proof that an audit is complete.
| Source or route | What it means for an audit | Who and when it applies |
|---|---|---|
| NIST AI RMF | Voluntary framework for considering trustworthiness through AI design, development, use, and evaluation. NIST’s resource hub says AI RMF 1.0 is being revised; related materials are available through the AIRC. | Organizations may use it to structure risk management; it is not, by itself, a legal compliance determination. |
| EU AI Act | Binding obligations depend on system category and the organization’s role. Covered high-risk systems have technical-documentation and conformity-assessment requirements; Annex IV specifies technical-documentation elements. | Only where the Act’s scope and relevant category and role apply. Do not describe high-risk requirements as duties for every AI system. |
| European Commission GPAI guidance | Describes model-level documentation obligations and additional duties for providers of models with systemic risk, including documented adversarial evaluation, risk assessment, incident reporting, and cybersecurity safeguards. | Relevant provider duties are not a universal checklist for organizations auditing downstream AI systems. |
Under the EU AI Act, conformity-assessment routes vary with the system and circumstances: some use internal control, while some involve a quality-management-system and technical-documentation assessment by a notified body. Check the applicable provisions and current harmonised standards or common specifications for the particular system before advising that a route, audit, or document set is sufficient. The NIST framework remains voluntary regardless of whether an organization also has legal obligations.
