What is AI Audit
Reviewing an AI system for quality, risk, safety, fairness, compliance, and suitability for real-world use.
Definition
An AI audit helps you understand whether the model and the process around it can be trusted. Review data, metrics, errors, stability, documentation, access, security restrictions, user impact, and compliance with internal or external regulations. This is necessary before implementation and after launch.
Example
The company is implementing a support chatbot and checking whether it does not disclose personal data, does not give dangerous advice, and whether it consistently answers standard questions.
Why it matters
The term is important because an AI service can look convincing in a demo but fail in real-world scenarios, edge requests, and security requirements.
How it works
The audit includes test cases, manual review, log analysis, risk assessment, model documentation, and recommendations for fixes.
Where it is used
- corporate implementation of AI
- chatbot check
- risk and compliance assessment
Limitations
A one-time audit does not guarantee security forever. Models, data, users and threats change, so regular monitoring is necessary.