AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
An audit trail is a complete record of what an AI system did and why, so any decision can be reconstructed and reviewed later. In regulated work, an answer you cannot trace is an answer you cannot defend. Traceability is what makes AI decisions accountable to auditors, regulators, and customers.
An audit trail for AI is the same as a financial ledger: every decision is dated, attributed, and preserved so that any question about what happened can be answered with evidence, not memory.
A regulated lender logs every AI decision: the input, the model version, the output, and the timestamp. When a customer disputes a decline, the compliance team retrieves the exact record and provides a documented response.
In any regulated industry or any AI system that makes decisions affecting people or money. Build traceability into the design from the start, not as a retrofit after a compliance review.
Retaining every AI interaction is expensive and may itself create data compliance obligations. Audit logs should retain what regulation and business need require, not everything by default.
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