What is AI Auditing
The process of regularly reviewing an AI system, its data, behavior, documentation, and consequences of use.
Definition
An audit of AI systems is broader than a one-time model check. It includes technical quality, security, access rights, data handling, transparency, impact on users, regulatory risks and team performance. In mature companies, auditing becomes part of the AI product life cycle.
Example
After updating the model, the company compares the responses of the old and new versions, checking for error growth, security, user complaints, and policy compliance.
Why it matters
The term is important for projects that launch AI into production: without regular testing, quality can quietly deteriorate and risks accumulate.
How it works
They create an audit plan, define metrics, collect logs and test scripts, check documentation, analyze incidents and record an improvement plan.
Where it is used
- model monitoring
- regular quality checks
- corporate governance AI
Limitations
A weak audit turns into a formality if there is no data, independent verification and responsibility for corrections.