EU AI Act Article 12: Record-Keeping & Structured Logging
How EU AI Act Article 12 record-keeping requirements translate to structured logging and audit trails in your AI systems.
Article 12 of the EU AI Act requires high-risk AI systems to have automatic logging capabilities that record events relevant for identifying risks and ensuring traceability.
For engineering teams, this means structured logging is no longer just a best practice — it is a legal requirement.
What Must Be Logged
System Operation Events
Record the period of each use (start/end times), the reference database against which input data was checked, and the input data for which the search led to a match.
Decision & Output Logs
Log AI system outputs, including confidence scores, classification results, and recommendations.
Human Oversight Actions
Record human review decisions, overrides, approvals, and rejections with timestamps and operator identity.
Anomalies & Incidents
Log anomalous inputs, unexpected outputs, system errors, and performance degradation.
Implementation with Structured Logging
Structured JSON Logging
Use structured logging (JSON format) with consistent schemas. Include timestamp, event type, system ID, trace ID, input/output summary, and confidence scores.
Immutable Audit Trail
Store logs in append-only, tamper-resistant storage. WORM storage like S3 Object Lock ensures logs cannot be altered after the fact.
Correlation & Traceability
Use correlation IDs (trace IDs) that link logs across the entire AI pipeline for end-to-end traceability.
Log Retention Requirements
The EU AI Act specifies minimum retention periods for logs generated by high-risk AI systems.
Providers must retain logs for at least 6 months, unless longer periods are required by applicable law.
Deployers must keep logs for a period appropriate to the intended purpose, at least 6 months.
Frequently Asked Questions
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