COMPEL Glossary / audit-trail
Audit Trail
An audit trail is a chronological record of all activities, decisions, and changes related to an AI system, maintained to support accountability, compliance verification, and regulatory examination.
What this means in practice
Effective audit trails capture who made each decision, when it was made, what information was available, what alternatives were considered, and what rationale supported the chosen course. In AI governance, audit trails must cover the entire model lifecycle: training data selection, model design decisions, validation results, deployment approvals, production changes, incident responses, and retirement decisions. Audit trails must be immutable (protected against retrospective modification) and accessible (retrievable within a reasonable timeframe). The COMPEL artifact system creates audit trails through its evidence chain architecture spanning vertical, horizontal, and temporal dimensions.
Why it matters
Without immutable, comprehensive audit trails, organizations cannot reconstruct how AI decisions were made, who approved them, or what information was available at the time. This gap makes it impossible to investigate incidents, satisfy regulatory inquiries, or learn from failures. Audit trails are the documentary backbone of AI accountability, providing the chronological evidence that supports governance, compliance, and continuous improvement.
How COMPEL uses it
The COMPEL artifact system creates audit trails through its evidence chain architecture spanning vertical, horizontal, and temporal dimensions. During Model, audit trail requirements are designed covering the entire AI lifecycle from training data selection through retirement. The Produce stage implements immutable logging and decision documentation. The Evaluate stage reviews audit trail completeness and accessibility, and regulators can examine trails as evidence of governance rigor.
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Related Terms
Other glossary terms mentioned in this entry's definition and context.