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Keep an audit trail that lets you reconstruct which AI system and version produced a decision or change, when it happened, what information informed it, and what review followed. Start by defining the events to record, then protect the records, test retrieval, and set retention periods based on the system’s legal and operational context.
What an AI audit log should let you reconstruct
A useful audit trail connects an event to the system that produced it and the evidence and oversight around it. For each material decision or model change, record enough context to answer: what happened, when, which system version was involved, why the outcome or change occurred, and who reviewed or authorized it.
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The EU AI Act requires high-risk AI systems to technically allow automatic event logging over their lifetime. Article 12 identifies traceability, risk identification, post-market monitoring, and operational monitoring among the purposes. Applicability depends on the system’s classification, the organization’s role, and the relevant legal context; this is not a universal logging rule for every AI system. Regulation (EU) 2024/1689, consolidated text.
What to record for decisions and model changes
The following is a practical record design to support reconstruction and oversight, not a field-by-field statutory checklist for every AI system.
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| Record element | What to capture |
|---|---|
| System identity | Stable system, model, and deployment identifiers; version; and effective dates. |
| Event context | Timestamp with a consistent time zone, event type, and a request or case identifier where appropriate. |
| Decision outcome | The outcome and the relevant decision pathway or policy. Link to inputs and outputs when retention is lawful and necessary; do not copy sensitive data into logs without a defined need. |
| Change record | What changed, why, who authorized it, and when it was deployed; link to testing, validation, and risk-review evidence. |
| Human oversight | Applicable review, override, or appeal and its outcome, where relevant. |
| Record controls | Access permissions, protected storage, monitoring for logging failures, and a tested retrieval process for the defined audit period. |
For remote biometric identification, the Act specifies particular minimum log information: the period of each use, the reference database against which input data were checked, the input data that led to a match, and the identities of people who verified the results. These specialized requirements should not be treated as a universal schema for other AI systems. Regulation (EU) 2024/1689, consolidated text.
How to make the trail usable in an audit
- Define material events. Decide which decisions, deployments, model changes, overrides, and failures must be recorded. Keep event definitions consistent so teams can find and compare records.
- Connect records to evidence. Use stable identifiers to link an event with its system version, relevant case or request, review, and supporting tests or risk assessments. Store references rather than duplicating sensitive material when that meets the need.
- Restrict and protect access. Give access according to role and purpose, protect stored records against unauthorized changes, and monitor whether required logging is operating.
- Test retrieval and export. Run periodic retrieval checks against the audit period you have defined. Confirm that authorized staff can locate, interpret, and export related records rather than finding isolated log fragments.
- Document retention and disposal. Set periods by record category and applicable law or policy, then ensure disposal follows the same documented rules.
NIST’s AI Risk Management Framework and Playbook provide voluntary guidance for organizing AI risk-management work, including governance and evidence practices. They can help structure a process, but do not decide which laws apply to a particular system. NIST says AI RMF 1.0, released January 26, 2023, is under revision, so check its current status when using it. NIST AI Risk Management Framework and NIST AI RMF Playbook.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How long to keep AI logs and related documentation
Do not treat logs and technical documentation as one record category. In the EU AI Act consolidated text dated July 27, 2026, Article 19 says providers must retain automatically generated logs under their control for a period appropriate to the intended purpose and for at least six months, unless applicable Union or national law provides otherwise. Article 18 separately requires specified provider records—including technical and quality-management-system documentation, applicable change approvals and notified-body records, and the EU declaration of conformity—to be kept for ten years after the high-risk system is placed on the market or put into service. These periods concern different records and duties; applicability depends on the system, role, and legal context. Check the current text and applicable rules before setting a real schedule. Regulation (EU) 2024/1689, consolidated text.
Other guidance should not be mistaken for a general AI retention period. NIST SP 800-171 Rev. 3 says audit records should be retained in line with the records retention policy in its context of protecting controlled unclassified information in nonfederal systems. It does not establish a universal retention period for AI logs. NIST SP 800-171 Rev. 3.
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Common logging failures to avoid
- Recording an outcome without its version: A decision record that cannot be tied to the deployed model and system configuration is difficult to interpret later.
- Logging sensitive data by default: Capture or link only information needed for a defined purpose, consistent with applicable privacy and retention obligations.
- Keeping logs but not testing access: Records that cannot be retrieved, correlated, or exported when needed do not provide a dependable audit trail.
- Confusing logs with documentation: Event logs and lifecycle or conformity documentation can have distinct retention rules.
- Assuming voluntary guidance sets legal duties: Use NIST resources as implementation guidance, while separately determining applicable law and organizational policy.
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