Introduction
Most organizations deploying AI in a regulated decision - credit scoring, employment screening, insurance underwriting, fraud flagging - already log something. A timestamp, an input, an output, maybe a model version number. When a regulator or an affected individual challenges that decision months later, that log is rarely enough. The question that actually gets asked is not "what did the model output" but "can you prove the model that produced this decision was operating on the data it was supposed to be operating on, unaltered, at the time it made this call." Most existing logging infrastructure was never built to answer that.
What a Regulator-Grade Audit Trail Actually Requires
The decision, the model version, and the data must be linked - permanently
A defensible audit trail connects three things at the moment of decision: the specific input and output, the exact model version that produced it (not a rolling label like "production" that silently points to different weights over time), and the specific state of any reference or training data the model relied on. If any one of these three can drift independently of the others in the historical record, the chain breaks, and a challenged decision cannot be reconstructed with confidence.
The record has to be tamper-evident, not just append-only
Append-only logging is a good baseline but does not, by itself, prove nothing was altered - it only makes alteration harder to do casually. A regulator-grade trail requires that any modification to the historical record, however it occurred, is cryptographically detectable. This is the difference between a log an engineer trusts because they built it, and a log an external auditor can independently verify without trusting the organization's word for it.
