As AI moves into regulated territory, audits are becoming routine — by regulators, customers, and your own risk teams. An AI audit asks a simple question with complex answers: does this system do what you say it does, safely and lawfully? Preparation is what turns that question from a threat into a formality.
What an audit looks at
Auditors typically examine four things: the data a system was trained and runs on, how it makes decisions, the controls around it, and the evidence that those controls work. Gaps in any one area undermine confidence in the rest.
Build the evidence trail early
The organisations that pass audits comfortably are the ones that documented as they built. Keep records of data sources and consent, model purpose and limitations, testing for accuracy and bias, and the human oversight in place. Reconstructing this after the fact is painful and rarely convincing.
A short readiness checklist
- Maintain an up-to-date inventory of AI systems and their owners.
- Store technical documentation where it can be produced on request.
- Log incidents, changes, and the decisions made in response.
- Be able to explain, in plain language, how each system reaches its outputs.
Treat your first audit as a template for the rest. The effort you invest in evidence and explainability pays off every time someone — internal or external — asks you to prove your AI is under control.
