AI Audit Trail
How to build an AI audit trail that satisfies EU AI Act requirements: provenance, decision logs, tamper-evidence, retention, and regulatory access.
An AI audit trail is the complete, tamper-evident record of all significant events in an AI system's lifecycle — from data sourcing and model training through deployment and individual decisions.
Building a regulatory-grade AI audit trail requires combining training data provenance, model version lineage, deployment records, and runtime decision logs into a coherent, verifiable record.
Audit Trail vs. Decision Log
A decision log captures runtime AI decisions (who, what, when, with what inputs and outputs). An audit trail is broader — it captures the full chain of provenance: where did the training data come from? Which model version was deployed? What configuration was active at the time of the decision? Who reviewed and approved the deployment?
CertifiedData.io provides cryptographic certification infrastructure for synthetic datasets and AI artifacts, producing tamper-evident records for audit and EU AI Act compliance.