AI audit checklist: what to check when auditing AI

Use this AI audit checklist to make sure an audit covers everything that matters — from the AI inventory to bias testing to the evidence trail. It follows the dimensions a credible audit examines. Part of our guide to the AI audit.

AramGRC Team·AI Audit & Assurance·September 11, 2026·8 min read

How to use this checklist

Work through each group for the system under audit, gathering evidence as you go. A “yes” needs proof — a document, a test result, a log — not an assurance. Gaps become findings with a severity rating.

The AI audit checklist

System and scope — the system is in your AI inventory, with an owner, a risk tier, and a documented purpose and intended use.
Data — training and input data quality, representativeness, and lawful basis (DPDP / GDPR), with documented lineage.
Model performance — accuracy against intended use, defined evaluation metrics, and benchmarking.
Bias and fairness — tested across protected and proxy attributes, with results documented.
Robustness and security — adversarial testing, drift monitoring, and incident handling.
Explainability and transparency — explanations appropriate to the use, and disclosure to users where required.
Human oversight — a working review, override and escalation path.
Governance and controls — an AI policy, approval gates, a monitoring cadence, and an evidence trail.
Compliance mapping — the system mapped to ISO/IEC 42001, the EU AI Act, DPDP and any sector rules.

Turn the checklist into an audit

A checklist tells you what to check; an audit gathers the evidence and rates the gaps. Run it as a structured process — see how to audit an AI system — and against a defined standard with an AI audit framework.

How AramGRC helps

AramGRC turns this checklist into a formal independent audit — evidence-gathered, scored against a standard, and delivered as an actionable report.

Frequently asked questions

What should an AI audit check?+

The AI inventory and ownership, data quality and lawful basis, model performance, bias and fairness, robustness and security, explainability, human oversight, governance controls, and compliance mapping.

What is in an AI audit checklist?+

Grouped checks covering system scope, data, performance, bias, robustness, explainability, human oversight, governance and compliance — each backed by evidence.

What data do auditors review in an AI audit?+

Training and input data quality and lawful basis, data lineage, model cards, evaluation and bias-test results, monitoring logs, and governance records.

Do you need to test for bias in an AI audit?+

Yes — bias and fairness testing across protected and proxy attributes is a core part of any credible AI audit, especially for systems that affect people.

AI AuditChecklist

The AI Audit series

AI Audit

What an AI audit is, the types, the process, and what a good report contains.

How to Audit an AI System

The step-by-step process for auditing an AI system.