AI Audit Trail: Tamper-Evident Records for Every AI Decision
An AI audit trail captures what your AI decided, when, under which model version, and at what confidence level. InferTrust™ creates cryptographic audit trails that regulators and courts accept as evidence.
What an AI Audit Trail Must Capture
- Model Version Identifier: A cryptographic fingerprint of the exact model binary, not a human-readable label that can be reused or changed.
- Policy Version at Decision Time: The specific business rules and guardrails that governed the model at the moment of inference.
- Confidence Score: The numeric certainty measure that determines whether the decision was autonomous, escalated, or deferred.
- Input Feature Hash: A cryptographic hash of the input data, proving what the model saw without storing raw data.
- Timestamp with Cryptographic Binding: Bound to the decision record through a cryptographic signature, making backdating impossible.
- Decision Outcome with Action Taken: The prediction and its operational consequence sealed together: approve, deny, escalate, flag, or defer.
Logs vs. Audit Trails
- Integrity: Logs have no integrity guarantee and can be edited. Audit trails use cryptographic signatures that make tampering detectable.
- Model Tracking: Logs use human-readable strings like "v2.3". Audit trails use a cryptographic hash of the model binary.
- Timestamps: Log timestamps can be set to any value. Audit trail timestamps are cryptographically bound to the decision.
- Completeness: Logging is best-effort with gaps under load. Audit trails generate a signed record for every inference with detectable missing records.
- Regulatory Acceptance: Auditors routinely challenge log files. Cryptographic evidence meets the standards regulators and courts require.
Industries Requiring AI Audit Trails
Related Resources