Auditability

Auditability in AI means an organization can reconstruct, explain, and provide evidence of how a specific AI decision was made — what data was used, which model version was running, what the output was, and what human oversight was in place. It requires logging decisions with their context, versioning models and data, documenting review steps, and retaining records long enough to respond to inquiries. Auditability can't be added retroactively; it has to be designed into the system before deployment.

Without auditability, an organization can't investigate its own AI incidents, respond to regulatory inquiries, or defend its decisions when challenged. Regulations in financial services, healthcare, employment, and general AI governance increasingly require that organizations explain AI-influenced decisions to affected individuals and regulators — and demonstrate that appropriate oversight was in place. Audit logging is also the foundation for incident investigation: when an AI system produces a harmful output, the only way to understand what happened is if a record exists of what the system did.