AI audit: what it is and how to start

What an AI audit is

An AI audit is a structured check of an AI system. The question is simple: does it work as intended, fairly, and within the rules that apply to it?

Most people searching for an AI audit mean one of two things. Some want to audit an AI system. Others want to use AI to audit something else.

This guide covers both. They share one idea: AI output must be traceable, testable and reviewed by a person.

The AI audit meaning is practical rather than technical. It is evidence that a system does what the business says it does.

• Auditing an AI system: risk, bias, accuracy, data and controls
• Auditing with AI: document review, monitoring and evidence gathering

What an AI audit checks

What is an AI audit in practice? A reviewer looks at how the system was built, what data it uses, what it decides and who can override it.

An AI bias audit tests whether results differ unfairly between groups of people. It compares outcomes across groups and looks for patterns that the business cannot justify.

An AI audit trail is the record that makes all this checkable. It shows what went in, what the system produced and who approved it.

Without that record, advanced work in AI audit has nothing to examine. Logging is the first thing to build, not the last.

• Purpose: what the system is for and where it must not be used
• Data: where it comes from and who can see it
• Fairness: a bias check on outcomes
• Accuracy: tested results against a known answer
• Records: logs, versions and approvals

How AI helps audit and compliance teams

• 40%

  of audit chiefs use generative AI, up from 15%

  Institute of Internal Auditors

• 83%

  expect internal audit to use more AI next year

  IIA and AuditBoard

The other half of the topic is AI as a tool for auditors. Automating audit work starts with the repetitive parts: reading documents, matching records and flagging exceptions.

Internal audit AI use is rising. The Institute of Internal Auditors reported that chief audit executives using generative AI for internal audit activities rose from 15% to 40% in a year.

The 2025 North American Pulse of Internal Audit survey ran from October 3 to November 14, 2024.

Looking ahead, the IIA and AuditBoard found that 83% of respondents expect their internal audit function to increase AI usage over the next year.

Generative AI in audit means drafting summaries, comparing policies to controls and turning notes into workpapers. A reviewer still checks each output.

• Reading contracts, invoices and policies for key terms
• Matching transactions to records and flagging exceptions
• Drafting first versions of memos and workpapers
• Keeping a log of what was checked and when

Generative AI for compliance

Generative AI for compliance helps with the reading-heavy work: summarizing a new regulation, mapping it to your policies and drafting updates for review.

Generative AI for regulatory compliance suits tasks where the source text is known and the reviewer can compare output to it. It suits less well where a wrong answer reaches a regulator unchecked.

Three questions help decide: Is the source text fixed? Can a person verify the output quickly? What happens if it is wrong?

The IIA also reports that more than 40% of internal audit leaders were researching future AI use. Most teams are still early, so a small first step is normal.

Automation of the audit process

The automation of audit process steps is wider than AI. Rules-based tools and robotic process automation handle fixed steps, such as pulling reports or reconciling lists.

An RPA audit checks those bots like any other control: who built them, what they touch and how failures are caught. Bots that move financial data belong in scope.

IT audit automation applies the same idea to systems work: access reviews, configuration checks and change logs collected on a schedule.

Internal audit automation works best as a mix. Rules handle the fixed steps, AI reads messy documents, and people judge the results. Our guide to intelligent automation explains how those layers fit together.

US rules and frameworks

The US has no single AI audit law. Several frameworks and enforcers apply instead.

The NIST AI Risk Management Framework is intended for voluntary use. It aims to improve how trustworthiness considerations enter the design, development, use and evaluation of AI products, services and systems.

NIST also published a cross-sectoral companion profile for generative AI. It applies the framework to the risks of generative systems.

The Federal Trade Commission has stated that using AI tools to trick, mislead or defraud people is illegal. That sets a floor for any AI claim you make to customers.

Sector rules, such as those for finance and health, add their own duties. Check what applies to your business. This guide is not legal advice.

Why compliance with AI is getting harder

People ask online why AI compliance is becoming more complex. The core AI audit challenge is that more tools, more vendors and more rules arrive at the same time.

Each new tool is a new place where data flows and decisions get made. Compliance teams need an inventory before they can assess anything.

Start by listing every AI tool in use, including ones staff adopted on their own. Then rank them by the harm a mistake could cause.

How to start

Begin small and keep a record. A first AI audit does not need a large team.

If you want help choosing where AI pays off first, our AI strategy consulting work ranks processes by payback. For finance-heavy review work, see how AI in financial services handles the same questions.

• List the AI tools and automations already in use
• Name an owner for each one
• Turn on logging so every output can be traced
• Pick one document-heavy process to test first
• Add a person who approves outputs before they leave the team
• Review results on a fixed schedule and write down what changed