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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsAn AI safety audit starts by defining what system is being assessed, where and how it is used, and who could be harmed. Then connect each material risk to a test, preserve the evidence, and record findings, mitigations, owners, and residual risk. The result is a reviewable account of what was assessed—not a universal pass score or proof that an AI system is safe in every setting.
What should an AI safety audit cover?
Audit the deployed system in its actual context, not just an underlying model or a generic checklist. A system may include a model, application code, retrieval or other external services, data pipelines, user interfaces, human review, and operational controls. Its risks can change with the intended use, users, affected groups, configuration, and deployment environment.
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Before testing, state the decision the audit will inform: for example, whether to deploy, continue use, add a control, or accept a defined residual risk. Identify the people authorized to act on findings and the person or body authorized to accept risk. An audit can describe evidence and uncertainty; it cannot make those responsibilities disappear.
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Open an audit record before running tests. Capture enough context that a later reviewer can tell exactly what was in scope and whether the evidence still applies.
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- System identity: system and model identifiers, versions, configuration, and audit date. Include relevant dependencies, integrations, and external components.
- Purpose and setting: intended use, deployment environment, user groups, affected populations, relevant sectors and jurisdictions, and foreseeable misuse.
- People and decisions: provider and deployer roles where relevant, accountable technical and operational owners, reviewers, human decision points, and risk-acceptance authority.
- Data and boundaries: important data flows, inputs and outputs, external tools or retrieval sources, and controls around access or retention where they affect the risks being assessed.
- Scope limits: what was excluded, why it was excluded, and what that means for the conclusions.
Record the specific version and configuration tested. If the deployed system changes later, the old evidence may no longer describe the system in use.
2. Identify risks in the context of use
Build a risk register around plausible harm pathways, not just abstract properties of AI. For each risk, describe who might be affected, what conditions could trigger harm, what existing controls are supposed to prevent it, and where uncertainty remains.
NIST’s AI Risk Management Framework groups trustworthiness considerations into validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and fairness with harmful bias managed. These are lenses, not a requirement to weight every dimension equally in every audit. NIST notes that trustworthiness characteristics can involve tradeoffs and vary in relevance by setting; document why a dimension is in or out of scope for this use. See the NIST AI RMF FAQs.
A useful risk-register entry distinguishes the hazard from the evidence about it. For instance, “incorrect output could cause a harmful decision in this workflow” is a risk statement; a test result showing how often a specified test set produced that output is evidence. If no suitable evidence exists yet, record the gap rather than treating the risk as cleared.
3. Turn each material risk into a test plan
For every risk worth assessing, write a testable claim or question and decide in advance what evidence would support a conclusion. Choose methods that fit the system: document and data review, technical evaluation, security testing, observation of human workflows, or review of operating controls. NIST’s AI Resource Center provides testing, evaluation, verification, and validation (TEVV) resources; the exact test plan below is a practical audit approach, not a claim that NIST mandates this list.
| Risk area | Test question to adapt | Evidence to preserve |
|---|---|---|
| Validity and reliability | Does the system perform as intended on representative cases, including relevant edge conditions? | Test data provenance and exclusions, metric or decision rule, results, and known gaps. |
| Safety and failure behavior | What happens when inputs are ambiguous, incomplete, out of range, or otherwise difficult? Can a harmful outcome be detected or contained? | Test cases, observed failures, fallback behavior, escalation path, and control evidence. |
| Security and resilience | Can relevant threats or degraded conditions change outputs, expose data, or defeat safeguards? | Threat assumptions, test procedure, affected components, findings, and mitigation evidence. |
| Fairness in context | Is performance or impact materially different for relevant groups or deployment contexts? | Group and context selection rationale, data limitations, results, and uncertainty. Do not claim a universal fairness result from a narrow test. |
| Privacy | Do data handling and system behavior create privacy risks in this use? | Relevant data flows, test or review method, observed behavior, controls, and unresolved issues. |
| Human oversight and accountability | Can the people expected to intervene recognize problems and take effective action? | Workflow review, role definitions, escalation and override procedures, and evidence that controls operate as described. |
For generative systems, tailor tests to prompt and output behavior, tool or retrieval boundaries where present, and failure handling. Include expected operating conditions as well as relevant edge, degraded, or adversarial conditions. Select data and contexts deliberately; state their provenance, exclusions, and known gaps. Do not imply that a test set represents every user or every future input.
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4. Run tests and preserve reproducible evidence
Record the conditions of each test as it is performed. For stochastic systems, explain repeat counts or sampling choices and report variability when measured; a single output may not characterize behavior across runs. Do not describe a proposed test as completed unless it was actually run.
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- Identify the run: record the date, tester, system and model version, configuration, and environment.
- Identify the test material: record the test-set or prompt-set version, data provenance, selection method, and relevant exclusions.
- Specify the procedure: preserve steps, tools, criteria, thresholds or decision rules, and any deviations from the planned method.
- Capture the result: retain outputs, failures, logs or other artifacts needed to understand the result, subject to appropriate access controls for sensitive information.
- Index the evidence: give every artifact a stable reference and connect it to the risk and test-plan entry it supports.
For covered providers of general-purpose AI models with systemic risk, European Commission guidance specifically describes documented adversarial evaluation. That model-provider duty should not be generalized into a blanket legal checklist for every downstream AI system audit; see the Commission guidance on general-purpose AI provider obligations.
5. Write findings that lead to decisions
Each finding should make clear what was observed, where it applies, and what should happen next. Separate a measured failure from a plausible but untested risk, and separate both from missing evidence. Those are different conclusions and call for different responses.
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- Finding: concise title and description, linked evidence, affected users or contexts, and the tested system version.
- Assessment: severity rationale and, when assessed, likelihood or uncertainty. Explain the criteria used; do not present an unsupported score as objective fact.
- Response: recommended mitigation, accountable owner, due date, and how completion will be verified.
- Residual risk: remaining exposure after the proposed or completed mitigation, plus the name or role of the person authorized to accept it.
Set acceptance criteria before testing where practical. If a finding is accepted without mitigation, document the reason, limits of that decision, and approval rather than leaving the exception implicit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Assemble a reviewable audit package
A useful audit file gives an independent reviewer a path from system and purpose to risk, method, result, and action. It should include an executive summary, system description and scope, roles and assessment criteria, risk register, methods and data description, results and limitations, findings and remediation plan, residual-risk decisions, approvals, and a versioned evidence index.
Use this compact record structure for each risk or test:
- System/context: identifier, version, configuration, intended use, users and affected groups, deployment context, jurisdiction, and exclusions.
- Risk: hazard, harm pathway, affected population, trigger conditions, existing controls, and uncertainty.
- Test: objective, method, data or prompt-set provenance, environment, metric or decision rule, and limitations.
- Result: outcome, failures or deviations, evidence references, and enough procedural detail to reproduce critical tests.
- Action: severity rationale, mitigation, owner, due date, residual risk, and approval or risk-acceptance record.
- Maintenance: monitoring or incident signals, material-change triggers, next review date, and evidence access controls.
7. Keep the audit current and understand legal boundaries
Revisit the audit when a material system or context change, incident, newly observed failure mode, or monitoring signal could affect its conclusions. Set a review cadence appropriate to the system’s risk and applicable requirements; there is no single interval established here for every system.
Framework guidance and legal duties are not interchangeable. NIST describes the AI RMF as voluntary, and its resource hub says AI RMF 1.0 is being revised. The framework can help structure risk management, but using it does not by itself establish legal compliance. Consult the NIST AI RMF page and AIRC for current resources.
The EU AI Act creates obligations according to the system’s category and the organization’s role. For covered high-risk AI systems, the Act requires technical documentation and conformity assessment; applicable routes and whether a notified body is involved depend on the system and circumstances. The Act’s Annex IV specifies technical-documentation elements, and high-risk technical documentation must be prepared before placing a system on the market or putting it into service and kept up to date. These are specific statutory requirements, not universal duties for every AI system or a substitute for all broader safety evidence. Check the applicable provisions and current standards for the actual deployment in the EU AI Act text.
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