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
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