AI decision integrity is the ability to prove, with cryptographic certainty, what an AI system decided, when it decided, which model version ran, and what policy was in force. Learn why logging is not enough.
The Problem Decision Integrity Solves
AI logs can be modified after the fact
Model versions change but decisions reference stale metadata
Regulators ask which model made a decision and organizations cannot answer
Observability platforms monitor performance but cannot prove what happened
Compliance teams spend weeks reconstructing evidence for audits
The 7 Fields in a Decision Integrity Record
Model Version ID: cryptographic hash of the exact model binary
Confidence Score: model-reported confidence at inference time
Policy Version Hash: hash of the active organizational policy
Input Feature Hash: proves what the model saw without storing raw data
Timestamp: cryptographically bound at the moment of inference
Decision Action: APPROVE, ESCALATE, DENY, or FLAG
Sequence ID: monotonic counter where gaps reveal tampering