In short: An AI audit is an independent check of an AI system. Does it work as intended, treat people fairly and keep data safe? Does it meet rules such as the EU AI Act or ISO 42001? Auditors look at three things: how you govern the AI, how it actually behaves, and the evidence you kept. Without test records over time, there’s little to show.
You brought in AI to answer customers faster or to save your team hours of work. Sooner or later someone asks how you know it does that well. It might be a large customer, your board or an auditor. An AI audit is how that question gets answered, and it goes a lot smoother when you’ve been collecting the evidence all along.
What is an AI audit?
An AI audit is a structured review of an AI system by someone who didn’t build it. It checks if the system does what it should, without unfair, unsafe or unlawful side effects. Your own audit team can do it. So can an outside firm, or a body that certifies against ISO/IEC 42001, the main standard for managing AI. Some audits look at the organisation: policies, roles and risk assessments. Others look at one system: its data, its answers and its controls. Most look at both. An AI audit differs from a normal IT audit in one big way. Normal software does the same thing every time, so one test per release tells you a lot. An AI model can give two answers to the same question. Its behaviour can also shift after launch. So an auditor wants to see how it behaved over time, not just on one day.
The EU AI Act adds its own version for high-risk systems. Before such a system goes on the market, the provider must pass a conformity assessment under Article 43. For most high-risk uses, that’s a documented internal check by the provider. For some biometric systems, an outside body is involved.
What does an AI audit check?
An AI audit checks three layers: how the AI is governed, how it behaves and what evidence backs that up. Auditors usually follow a framework, such as the NIST AI Risk Management Framework, ISO 42001 or the EU AI Act. For chatbots and other language model apps, many also use the OWASP Top 10 for LLM applications. It lists the most common security risks. The questions overlap a lot:
- Governance: who owns the system, what it’s for, which risks you found and who can switch it off.
- Data: where the data comes from, whether you may use it and whether it fits the people the system serves.
- Behaviour: accuracy on real questions, made-up answers, bias, privacy leaks and resistance to misuse such as prompt injection, where someone hides instructions in the input.
- Monitoring: how you notice problems after launch, and what you did about them.
- Transparency: whether you can explain what the system does, and whether users know they’re dealing with AI.
Why is behaviour the hardest part to prove?
Behaviour is the hardest part because a policy is written once, while behaviour has to be measured again and again. An auditor can read your AI policy in an hour. That policy won’t tell them what your chatbot said to a customer last Tuesday. For that they need test results: which questions were asked, what the model answered, how it scored and what changed between runs. Many organisations have the policy but not the records. They tested before launch, maybe in a spreadsheet, and then stopped. So they can say they have a process, but they can’t show it works. In our view, that gap is where most AI audits get uncomfortable.
It’s also where AI differs from what software testers are used to. Even well-designed AI systems drift and misbehave over time. A clean result from launch day says little about today.
How do you prepare for an AI audit?
Start with a list of your AI, then build a record of how each system behaves. You don’t need a big project for this. You need a routine that keeps running. Our advice is to take these steps in order.
- List every AI system you build or use, including bought tools, with an owner and a purpose.
- For each one, write down what good looks like: the questions it must get right and what it must never do.
- Turn that into a fixed set of test questions and run it now. That first run is your baseline.
- Repeat the tests on a schedule and after every change. Keep the scores and the exact answers.
- Log every problem you find and what you did to fix it.
- Map your tests to the framework your auditor uses, so the evidence lines up with their questions.
There’s a trade-off. Not every AI tool needs this much. A writing helper that staff use for internal drafts may only need a line in your register and a spot check now and then. Put the effort where a wrong answer reaches a customer or decides something about a person.
What this means for your team
You’re responsible for your AI, even when you didn’t build it. An audit simply asks you to show that you’re in control. The hard part isn’t writing the policy. It’s having months of test results ready when someone asks for them.
For over thirteen years, Nekst IT, the company behind VeritasAI, has helped organisations deliver software they can trust through testing, validation and quality assurance. VeritasAI brings that to AI. It runs frequent automated risk and compliance tests on your model. It scores them against the EU AI Act, ISO, OWASP and other key standards. You can review the exact responses your model gave, so every result can be checked. Need a full review? Our experts assess connected systems, governance setup and compliance posture. You get a complete AI assurance report. None of this guarantees that you’ll pass an audit. It does mean you walk in with evidence instead of good intentions.
Walk into your next AI audit with the evidence ready.
Frequently asked questions
Is an AI audit required by law?
Not for every AI system. Under the EU AI Act, high-risk systems need a conformity assessment before they go on the market. ISO 42001 certification is voluntary. Large customers may still ask for audit evidence when they buy from you.
What is the difference between an AI audit and an AI risk assessment?
A risk assessment is your own analysis of what could go wrong with an AI system and how likely it is. An audit is a check, usually by someone else, of whether your controls for those risks actually work.
Who can carry out an AI audit?
Your internal audit team, an outside audit or assurance firm, or an accredited certification body if you want ISO 42001 certification. Under the EU AI Act, some biometric systems need a notified body, an organisation appointed by an EU country.
How long does an AI audit take?
It depends on how many systems are in scope and how good your records are. A review of one chatbot with clear test records goes much faster than an organisation-wide audit. Missing evidence is what makes audits drag on.
