AI System Logging Requirements
Multiple regulatory frameworks — including the EU AI Act, NIST AI RMF, and ISO 42001 — require AI systems to produce verifiable audit logs. This page covers the technical and compliance requirements for AI system logging, with a focus on training data provenance.
Why AI Logging Is Not Optional
The EU AI Act, effective from August 2025 for general-purpose AI systems and August 2026 for high-risk AI, creates binding logging obligations. Article 12 requires automatic logging of operational events; Article 19 requires providers to retain records of training datasets, test procedures, and performance evaluations for ten years.
Beyond the EU, NIST AI RMF Govern 1.7 and ISO 42001 Section 8.4 both require organizations to document the provenance of AI training data and retain evidence of conformity assessments. Logging is therefore a cross-jurisdictional requirement, not a European-only concern.
Required Log Properties
Log entries must be cryptographically bound so that any modification is detectable. Hash-chaining or Merkle tree structures satisfy this requirement.
Logs must capture every relevant event — not just errors. For training data, this includes the generation event, dataset fingerprint, and certification issuance.
Each log entry must carry a verifiable timestamp. Ed25519-signed certificates with ISO-8601 timestamps are accepted by regulatory frameworks as authoritative records.
The issuing authority must be identified and their signing key publicly accessible for independent verification.
EU AI Act Article 19 requires a ten-year retention period for high-risk AI system documentation.
Auditors and regulators must be able to verify log integrity without relying on the AI provider's cooperation.
Logging Synthetic Training Data
Synthetic training datasets require specific logging provisions. Because synthetic data is generated programmatically — not collected from real-world sources — the log must capture the generation algorithm, the configuration parameters, the synthetic sample size, and a cryptographic fingerprint of the resulting dataset.
CertifiedData issues certification artifacts that serve as logging records for synthetic datasets. Each artifact contains the SHA-256 hash of the dataset, the generation algorithm identifier (e.g. CTGAN), the timestamp, and an Ed25519 signature from the CertifiedData certificate authority. This artifact is the evidence that Article 12 and Article 19 require.
Logging Architecture Comparison
CertifiedData public transparency log is append-only and hash-chained
CertifiedData uses Ed25519 for all certificate issuances
CertifiedData verification requires no authentication
Every certified dataset is SHA-256 fingerprinted at generation time
Signing key history preserved at /.well-known/signing-keys.json
Certificates stored indefinitely on CertifiedData platform