An AI auditor should request evidence that makes each system reconstructable, testable and accountable—not just an AI policy. The core package covers the system’s purpose and scope, model and data lineage, risk decisions, test results, production logs, human-oversight records, incidents, changes, suppliers and any regulatory conformity file. Evidence must tie to a specific model version, data or prompt configuration, test run, reviewer and decision so another qualified person can reproduce what happened.
The exact legal set depends on jurisdiction, sector, system risk class and whether the organization is a provider, deployer or other participant. NIST’s AI Risk Management Framework (AI RMF), released January 26, 2023, is voluntary guidance; the EU AI Act creates mandatory duties for specified systems and roles. Use the checklist below to build an evidence request that tests both documented intent and controls operating in production.
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Start with an evidence map, not a policy review
Ask the company to identify every AI system in scope and map each control to an artifact, an owner and a period of operation. A policy explains what the organization says it will do. Audit evidence shows what it approved, tested, deployed, observed and corrected.
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| Evidence question | What a useful answer contains |
|---|---|
| What system is being audited? | A unique inventory entry, model and release identifiers, owner, provider/deployer role, purpose, users, locations and interfaces. |
| What could go wrong? | Risk and impact assessments, affected-group analysis, mitigation decisions and accepted residual risk. |
| Was it fit for use? | Test plans, data and metrics, thresholds, evaluator qualifications, tools, results and limitations under deployment-like conditions. |
| Does it work in operation? | Monitoring dashboards, alerts, event logs, human review records, incidents, root-cause analyses and corrective actions. |
| Who controls change? | Release tickets, approvals, post-change validation, dependency inventories, supplier reviews and access records. |
| What must be retained or disclosed? | Applicable conformity records, technical documentation, notices, authority communications and retention schedules. |
NIST organizes implementation around Govern, Map, Measure and Manage. The European Data Protection Board (EDPB) describes an end-to-end, socio-technical audit approach that examines people, processes, technology and impacts together. Use those structures to organize requests, while tailoring the scope to the applicable law.
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1. System identity, scope and intended use
Request enough detail to distinguish the system under audit from a product family, a vendor platform or an informal experiment. For each deployment, obtain:
- AI inventory record and unique system name;
- model name, exact version or commit, release date and deployment environment;
- provider, deployer, operator and accountable owner;
- intended purpose, user groups, affected people and geographic locations;
- interfaces, upstream and downstream dependencies, connected tools and data stores;
- approved uses, prohibited uses, known capabilities, limitations and failure conditions;
- risk classification and the rationale for including or excluding the system from a regulated category.
Ask for architecture and data-flow diagrams that show where inputs originate, where prompts or features are transformed, where outputs go and which human or automated process acts on them. Request the approval record for the stated purpose and evidence that actual use has not drifted beyond it.
What high-risk EU documentation should show
For an EU high-risk system, the technical documentation must describe the system’s general characteristics, capabilities and limitations as part of the risk-management framework. A generic product brochure is not a substitute for a version-specific technical file.
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2. Governance, accountability and competence
Request the control framework and evidence that named people exercised their responsibilities. The package should include:
- AI policy, standards and procedures, with approval dates and revision history;
- an accountability chart naming executives, system owners, risk approvers, operators and independent reviewers;
- role descriptions, segregation-of-duties rules and conflict-of-interest controls;
- training and competence records for developers, data stewards, reviewers, operators and incident staff;
- approval gates for procurement, design, release, material change and retirement;
- internal-audit plans, management reviews, prior findings and proof that findings were tracked to closure;
- resource records showing that staffing, computing, monitoring and review capacity were funded;
- supplier-governance procedures and escalation paths.
Sample a few approvals and trace them to meeting minutes, tickets or signed decisions. A policy with no accountable owner, due date or closure evidence is a statement of intent rather than an operating control.
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Use NIST without treating it as a certification
NIST says the AI RMF is intended for voluntary use to improve the incorporation of trustworthiness considerations into AI design, development, use and evaluation. Its Playbook offers suggested actions across Govern, Map, Measure and Manage, but explicitly is not a checklist or a mandatory sequence. Record which practices the company adopted and why; do not imply that using the framework itself proves compliance.
3. Risk, impact and legal classification
Ask for the method used to identify and rate risks, not merely the final rating. Evidence should include:
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- risk methodology, scoring criteria, assumptions and review frequency;
- risk register entries linked to the specific system and version;
- impact assessments covering individuals, groups and society where relevant;
- analysis of discrimination, privacy, safety, security, accessibility, labor and environmental effects as applicable to the use;
- affected-group consultation or testing records, including who was not represented;
- mitigation options considered, selected controls and reasons for rejecting alternatives;
- residual-risk acceptance signed by an authorized decision-maker;
- legal classification, jurisdictional analysis and the reasoning behind the classification.
Check whether a new purpose, data source, model update or incident triggered a reassessment. For EU systems, connect the risk-management process to the technical file and the duties that apply to the provider or deployer; do not generalize high-risk obligations to every AI use.
4. Data and training lineage
An auditor should be able to follow material data from collection to the deployed model or decision rule. Request:
- data inventories, schemas, provenance and custodians;
- collection authority, licensing terms, consent or other legal basis where relevant;
- data-quality, freshness, completeness and representativeness checks;
- labeling instructions, annotator qualifications, disagreement handling and quality sampling;
- preprocessing, filtering, feature engineering, deduplication and exclusion rules;
- privacy, confidentiality, access-control, retention and deletion safeguards;
- separate descriptions of training, validation and test sets, including known gaps and overlap checks;
- approvals for data changes and records showing which data version produced each model.
General-purpose model documentation
EU guidance for general-purpose AI providers calls for documentation of the model architecture; training process; training, testing and validation data; computational resources; and energy consumption. Request those records for the relevant release, together with the date and provider role. If a company relies on an upstream model, obtain the upstream documentation and the contract terms that define what the downstream deployer must monitor or disclose.
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5. Testing, validation and assurance
Pass/fail labels are not enough. Request the complete test plan and enough detail to reproduce or independently challenge the results:
- test objectives, hypotheses, acceptance criteria, thresholds and release gates;
- test-set versions, sampling method, representative populations and excluded cases;
- metrics, definitions, confidence intervals or error analysis where used;
- robustness, security, privacy, misuse, accessibility and stress tests appropriate to the system;
- tools, code or configuration identifiers, random seeds and reproducibility instructions;
- evaluator names, qualifications, independence and instructions;
- human-subject protections and approvals where people participated in testing;
- results for conditions that resemble real deployment, including language, geography, workload and adversarial inputs;
- limitations, unresolved failures, compensating controls and the decision to release.
Trace every headline metric to a dataset, run, model version and evaluator. Ask whether performance was measured separately for materially affected groups and whether the threshold reflects the harm of false positives and false negatives. A single aggregate accuracy number can conceal unacceptable failures.
Verify, validate and monitor separately
Verification asks whether the system was built as specified; validation asks whether it is suitable for the intended context; monitoring asks whether that remains true after release. Request evidence for all three rather than accepting a pre-release test report as proof of operational performance.
6. Human oversight and user information
Request operating procedures that identify who may review, override, interrupt or escalate an output, and under what conditions. Evidence should include:
- role-specific review instructions and decision criteria;
- training, competency checks and workload or fatigue safeguards for reviewers;
- override, rejection, escalation and shutdown logs;
- sampled decisions showing the reviewer’s reasoning and final outcome;
- user instructions, limitations, transparency notices and appeal routes;
- access controls preventing unauthorized staff from changing or bypassing oversight.
For a high-risk system, connect each oversight control to a named role, a decision record and a sampled event log. An “always human in the loop” statement is weak evidence if reviewers cannot see relevant inputs, have no time to investigate or routinely accept outputs without meaningful review.
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7. Production monitoring, logs and incidents
Ask how the company knows that controls continue to work after deployment. Request the monitoring plan, dashboard exports and alert history, including:
- quality, drift, bias, latency, availability, abuse and security indicators;
- thresholds, alert routing, response-time targets and escalation rules;
- model, prompt, policy, feature and configuration identifiers recorded with each event;
- timestamped input, output, action and reviewer records, subject to privacy minimization;
- incident reports, severity criteria, notifications and communications;
- root-cause analyses, corrective and preventive actions, owners and due dates;
- rollback, model-replacement and shutdown exercises with observed results;
- post-market monitoring reports and periodic management review.
For EU high-risk systems, the legal framework says systems should technically allow automatic recording of events over their lifetime. Test whether logs are complete, tamper-evident, searchable and retained for the required period. Sample ordinary events as well as incidents; a clean incident register may simply indicate that detection or reporting is ineffective.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Change, supplier and configuration control
Request a full change trail for the audited release and a sample of later changes:
- change tickets describing code, model, prompt, data, threshold, infrastructure or dependency changes;
- risk assessment, approval and segregation of duties for each material change;
- regression and post-change validation results;
- release, rollback and configuration records that identify what ran in production;
- software, model and data component inventories, including versions and vulnerabilities;
- vendor contracts, due-diligence reviews, service-level commitments and incident clauses;
- supplier access reviews and evidence that downstream deployers received required information;
- retirement, archival and deletion decisions.
EU Article 18 specifically identifies change approvals and quality-management records among documents to retain. Ask who can authorize an emergency change and require a retrospective review when the normal gate was bypassed.
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Where an EU high-risk system is in scope, request the conformity-assessment file, EU declaration of conformity, CE-marking evidence where applicable, notified-body decisions, quality-management records, technical documentation and communications with authorities. Confirm whether the organization is the provider, deployer, importer or distributor, because duties differ by role.
Article 18 of the EU AI Act states that specified records must be retained for 10 years after the system is placed on the market or put into service. The consolidated text cited here is shown as at July 27, 2026; verify the current text and enforcement guidance for the relevant deployment. Retention should cover the records needed to reconstruct decisions, not just a certificate or summary report.
Match evidence to the governing rule
NIST guidance is voluntary, while EU requirements are legal obligations for defined systems and actors. Sector rules, privacy law, employment law, medical-device requirements, procurement terms or contractual commitments may add controls. Document the jurisdiction, effective date, classification and source for every mandatory request.
How to test whether evidence is trustworthy
- Check identity: confirm that every artifact names the system, environment and model or configuration version.
- Check time: compare timestamps across approval, test, release, event and incident records.
- Check linkage: trace a sample output from input and model version through reviewer action, decision and any downstream effect.
- Check independence: establish who performed testing or approval and whether they could overrule the builder.
- Check completeness: look for missing periods, overwritten logs, excluded populations and undocumented emergency changes.
- Check operation: sample normal and exceptional events to see whether the documented control was actually used.
- Check closure: verify that findings have owners, deadlines, retest results and a recorded decision on residual risk.
Preserve only the minimum personal or confidential information needed for the audit, and record lawful access, masking and retention controls for the audit evidence itself.
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When an auditor must compare vendors, models or deployments, score the evidence package on these five axes:
| Axis | Questions to ask |
|---|---|
| Traceability | Can each output be connected to model/version, data, prompt or configuration, reviewer and decision? |
| Validity | Were tests representative, independently reviewed, reproducible and run under deployment-like conditions? |
| Operational effectiveness | Do monitoring, oversight, incident response, rollback and corrective-action records show controls working? |
| Governance and retention | Are owners, approvals, supplier controls, change records and retention periods defined and evidenced? |
| Regulatory fit | Does the package address the applicable jurisdiction, risk class and provider or deployer obligations? |
A certification badge may be useful context, but it cannot replace version-specific tests, production records and accountable decisions.
A practical request list to send the company
For a first request, ask for an index with document owner, version, date, confidentiality level and retention period, followed by the artifacts below:
- system inventory entry, architecture and data-flow diagrams;
- intended-use, prohibited-use and limitation statements;
- governance framework, roles, training and approval records;
- risk register, impact assessments and residual-risk decisions;
- data provenance, licenses, quality checks and dataset versions;
- model cards or equivalent technical documentation and dependency inventory;
- test plans, datasets, metrics, tools, evaluator records and results;
- human-oversight procedures, training and sampled override logs;
- monitoring plans, dashboards, alerts, event logs and post-market reports;
- incident files, root-cause analyses, corrective actions and rollback exercises;
- change tickets, release approvals, post-change validation and access reviews;
- supplier contracts, evaluations and downstream information notices;
- applicable conformity, declaration, marking, authority and retention records.
Require a cross-reference from each control in the audit criteria to one or more artifacts and to the person who can explain it. That turns a document dump into an auditable evidence trail.
What the auditor should conclude
A company is prepared when it can reconstruct why an AI system was approved, what data and version produced an output, which tests supported release, who exercised oversight, how production behavior was monitored and what happened when controls failed. Missing links—especially absent version identifiers, unrepresentative tests, unverifiable logs or unresolved corrective actions—are findings even when the policy language is excellent.
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