An AI safety audit log should let an authorized reviewer reconstruct a consequential event: when it happened, which system and version acted, what triggered it, the relevant input and output references, what action followed, and whether a person reviewed or changed the result. There is no universal legal schema for every AI system. Treat the fields below as a risk-based design, and distinguish them from requirements that apply only to systems within a specific law’s scope.
What should an AI audit log capture?
Design each record to answer the questions an investigation or audit would actually ask: who or what acted, when, on which deployed system and configuration, in response to what, and with what result? A practical event schema may include:
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- Time and linkage: a timestamp with a consistent time basis, plus an event or correlation ID that connects related records.
- System identity: the application or service, deployed model or service version, and relevant configuration or policy version.
- Actor and trigger: the user, service, or other actor identity and the initiating action, request class, or event.
- Relevant context: references to input and output artifacts. Where external data or tool calls materially affect the action, record the relevant source or tool identifiers and their outcomes.
- Outcome and safeguards: the decision or action taken, errors, safety interventions, and any policy or control path invoked.
- Human involvement: review, approval, override, escalation, or interruption, with reviewer identity and time where appropriate.
- Record provenance: logging-pipeline status and enough provenance to identify missing or altered records.
This is an implementation pattern, not a prescribed field list for all AI systems. Tailor it to the system’s purpose, risk, and legal obligations. A reference, hash, or minimized representation may answer an audit question without retaining an entire prompt or output; store raw content only when it is necessary and lawful.
What does the EU AI Act require?
Article 12 of Regulation (EU) 2024/1689 requires high-risk AI systems within the Act’s scope to technically allow automatic recording of events over the system’s lifetime. The purpose is traceability and support for identifying risk situations, post-market monitoring, and deployer monitoring. This is not a logging mandate for every AI system worldwide. See the consolidated EU AI Act text and the European Commission’s Article 12 page.
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For the specified high-risk remote biometric identification systems, Article 12 names particular log details: each use’s start and end time, the reference database checked, the input data that led to a match, and the identities of people who verified the results. That list applies to that category; it should not be presented as the legal minimum for every AI deployment.
Instructions for deployers
Article 13 says instructions for use should describe mechanisms that enable deployers to collect, store, and interpret logs where relevant. The Commission summarizes this in its Article 13 guidance.
How long should AI audit logs be kept?
For automatically generated logs under Article 12, Article 19 sets a period appropriate to the intended purpose and a minimum of six months, unless applicable Union or national law provides otherwise. The provision addresses covered high-risk systems; six months is not a universal global retention rule or blanket permission to retain personal data. Check the applicable regime and data-protection requirements when setting a schedule. The Commission’s Article 19 page reproduces the retention provision.
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Logs can become sensitive records in their own right. NIST’s SP 800-92 warns that logging may capture information such as passwords or email contents and discusses policies for inadvertent disclosures, protecting evidence, restricting access, and preserving integrity. Practical controls include:
- Limit collection to what is needed to investigate relevant risks and meet obligations; avoid default retention of full prompts, outputs, or personal data when a less revealing reference will suffice.
- Restrict access by role, protect log transfers and storage according to risk, and monitor administrative access.
- Define a response for accidental capture or disclosure of sensitive data.
- Document retention, secure disposal, and procedures to preserve records needed for an investigation.
These operational measures must be adapted to context and do not replace applicable privacy law. NIST’s SP 800-92 Rev. 1 initial public draft page describes log management as a lifecycle that includes generating, transmitting, storing, accessing, and disposing of log data. Collection alone is not a complete log-management practice.
How should you assess a logging design?
Whether you are reviewing an in-house design or a logging tool, assess the records against the actual audit questions rather than the volume of data collected. Useful checks include:
- Can related events be linked across the model, application, tools, external data, and human actions?
- Do the captured details support the system’s risk and intended purpose without unnecessary sensitive content?
- Are access controls, integrity protections, and investigation procedures adequate?
- Does retention and deletion behavior match applicable obligations?
- Can the team export and interpret records, and are coverage gaps or operational costs understood?
NIST’s AI Risk Management Framework and its Playbook are voluntary risk-management resources, not mandatory logging checklists; NIST says the framework is being revised. The Playbook offers suggestions for achieving framework outcomes. NIST SP 800-92 is broader computer-security log-management guidance, not an AI-specific event schema. See the NIST AI Risk Management Framework and NIST AI RMF Playbook.
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