How easy it is for an independent reviewer — a regulator, auditor, or assessor — to check that an AI system is doing what it claims to do and following the rules.
How easy it is for an independent reviewer — a regulator, auditor, or assessor — to check that an AI system is doing what it claims to do and following the rules.
Auditability requires three enablers: documentation (comprehensive records of design decisions, data sources, training procedures, and test results), traceability (the ability to link outputs back to inputs and decisions), and access (the ability for auditors to retrieve and review relevant records). The EU AI Act embeds auditability requirements in its technical documentation, logging, and post-market monitoring provisions. ISO/IEC 42001 includes audit requirements as part of its management system structure. Auditability is increasingly assessed not just by regulators but by customers, investors, and insurers evaluating AI risk.
Engineering and governance teams should design auditability into AI systems from day one — logging decisions, preserving model artefacts, and maintaining documentation so that any future audit can be satisfied without costly reconstruction.
Like the audit trail in a financial accounting system — every transaction is recorded, timestamped, and attributable, enabling an auditor to trace any discrepancy back to its source.
How easy it is for an independent reviewer — a regulator, auditor, or assessor — to check that an AI system is doing what it claims to do and following the rules.
Auditability requires three enablers: documentation (comprehensive records of design decisions, data sources, training procedures, and test results), traceability (the ability to link outputs back to inputs and decisions), and access (the ability for auditors to retrieve and review relevant records). The EU AI Act embeds auditability requirements in its technical documentation, logging, and post-market monitoring provisions. ISO/IEC 42001 includes audit requirements as part of its management system structure. Auditability is increasingly assessed not just by regulators but by customers, investors, and insurers evaluating AI risk.
Engineering and governance teams should design auditability into AI systems from day one — logging decisions, preserving model artefacts, and maintaining documentation so that any future audit can be satisfied without costly reconstruction.