AI Audit Trails That Produce Verifiable Evidence
CertifiedData connects artifact certification, Decision Ledger records, public logs, registries, and verification tools into a tamper-evident audit trail for AI systems.
Instead of relying on mutable application logs, CertifiedData records lifecycle events as signed, hash-linked evidence: what artifact was certified, what decision was made, which policy or context applied, and whether the record was altered later.
From audit trail concept to execution stack
An audit trail is only useful if teams can inspect and verify the records behind it.
CertifiedData exposes the operational surfaces behind the audit trail. Each step below is a live route — click through to certify, record, inspect, verify, or connect.
Fingerprint and sign datasets, model artifacts, or AI outputs.
or notarize an artifact →Generate signed, hash-chained AI decision records that survive review.
Review the append-only feed of signed decision records and public proof surfaces.
Validate hashes, signatures, and record integrity independently — no platform access required.
Link audit events back to the certified artifacts and datasets they reference.
Verifiable evidence shape
What this gives an auditor
A reviewable record of an AI lifecycle event includes:
- the certified artifact or dataset involved
- the decision record or lifecycle event
- timestamp and issuer context
- policy, model, or system context where available
- SHA-256 fingerprint
- Ed25519 signature
- public verification path
- registry or transparency-log reference
All cryptographic checks run against the published Ed25519 public key — verification does not depend on access to the application that produced the record.
Logs vs. audit trails
Application logs are not audit trails.
Most teams hand a regulator their application logs and discover, slowly, that the records do not survive review. Logs answer engineering questions. Audit trails answer evidence questions. The shape, integrity, and verification path are different.
Application log
- Written by the application that produced the event, at the discretion of the application.
- Editable by anyone with database or admin access; no external signal of modification.
- Captures what the system thinks happened, not what an outside reviewer can verify.
- Treated as supporting evidence at best; rarely accepted as primary evidence in regulated review.
- Lost or rewritten if the application, schema, or storage layer changes.
Verifiable audit trail
- Canonicalized, hashed, and signed at the point of creation; record bytes are fixed once written.
- Tampering is detectable by anyone with the public key — no platform access required.
- Captures actor, entity, decision, rationale, timestamp, model context, and verification metadata.
- Designed to support EU AI Act Article 12 record-keeping and equivalent traceability obligations.
- Independent of the application: a record can outlive the system that produced it and still be verifiable.
The distinction is structural: an audit trail is not a tone of voice or a retention policy applied to logs. It is a different kind of record — one whose contents are provable, not just stored.
Audit-readiness questions
What an auditor will actually ask
The cleanest test of an AI audit trail is to read the questions a reviewer will ask and check whether the evidence layer can answer them without screen-shares, vendor calls, or tribal knowledge.
Cryptographic primitives
What makes the audit trail tamper-evident
CertifiedData uses a small, well-understood set of primitives. None of them are exotic; the value is in how they compose, not in any single piece.
RFC 8785 JCS canonicalization
Eliminates serialization ambiguity. Two reviewers will compute the same canonical bytes from the same record content.
SHA-256 record hash
A 256-bit fingerprint of the canonical record bytes. Any byte-level change produces a different hash, making tampering detectable.
Ed25519 signature
Compact, fast, and well-supported elliptic-curve signatures. The public key verifies the record without giving access to the signing key.
Public verification path
Each record carries a verification URL. Reviewers fetch the canonical record, recompute the hash, and check the signature against the published key.
Article 12 evidence path
For high-risk AI systems, traceability requires more than logs.
EU AI Act Article 12 record-keeping pressure is not just about storing logs. Teams need traceable records that can support review after the fact. Decision Ledger and CertifiedData artifact certification help connect runtime decisions to verifiable evidence — supporting Article 12 readiness without overclaiming compliance.
Read the Article 12 record-keeping guide →Build the audit trail you can prove
Move from internal logs to independently verifiable AI lifecycle records.
Sign a decision, inspect the public ledger, verify the proof, and connect each event back to the certified artifacts it references.