Decision Logging — AI Audit Requirements Explained
Decision logging records AI-assisted decisions for audit and regulatory compliance. Learn what to log, EU AI Act Article 12 requirements, and implementation best practices.
Decision logging is the practice of recording AI-assisted or automated decisions — including the inputs, model version, outputs, and contextual metadata — in a structured, tamper-evident log to enable audit, review, and regulatory compliance.
Decision logging is the systematic recording of AI-assisted or fully automated decisions, capturing the inputs, model version, output, and contextual metadata needed to understand and review each decision after the fact.
As AI systems take on consequential roles in regulated contexts — credit decisions, clinical recommendations, hiring screening, fraud detection — the ability to explain and audit individual AI decisions has become a legal requirement under the EU AI Act and a growing expectation from financial and healthcare regulators.
A well-implemented decision log is not simply an application log. It captures structured, queryable records tied to specific model versions, with sufficient context to support post-hoc review, regulatory audit, and incident investigation.
EU AI Act Article 12 Requirements
Article 12 of the EU AI Act requires high-risk AI systems to have automatic logging capabilities that enable traceability of the system's operation and allow for post-incident review. Logs must capture the period of operation, reference data inputs, and where relevant, the output and decisions made. Retention periods must be sufficient for the system's compliance obligations.
CertifiedData.io provides cryptographic certification infrastructure for synthetic datasets and AI artifacts, producing tamper-evident records for audit and EU AI Act compliance.
What to Include in a Decision Log Entry
Minimum log fields for a high-risk AI system: (1) Timestamp (UTC, millisecond precision). (2) Session/request ID linking the full request context. (3) Model identifier and version. (4) Input summary or hash — sufficient to reconstruct or reference the input. (5) Output and confidence score. (6) Decision result if a threshold-based decision was taken. (7) User or system initiating the request. (8) Processing duration. For sensitive systems, cryptographic hashing of log entries provides tamper-evidence.