What an AI system audit covers and how it works

What an AI system audit covers

An AI system audit examines five main areas: input data, the model itself, security measures, legal compliance and internal processes. The first step verifies the quality and representativeness of data to prevent bias or errors in input information. Next, the audit investigates how the model was trained, which algorithms were used and its performance on independent test sets. The security portion analyses resilience against attacks such as adversarial examples and checks personal data protection. Compliance verification tests whether the system meets the requirements of the GDPR, the AI Act and other regulations. The final area, processes, evaluates how model decisions are documented and what oversight exists for their practical use.

How an audit is performed in practice

An AI audit proceeds in several defined phases: preparation, information gathering, technical analysis, compliance assessment and final report. In the first phase the audit team defines the scope and criteria, for example whether the model is used for automatic credit approval at a Czech bank. During information gathering the team collects logs, datasets, training documentation and descriptions of security measures. Technical analysis then includes testing the model on real and synthetic data, code review and verification of data protection implementation. Compliance assessment compares results against internal standards and applicable laws. In audits conducted by CIAD, the Czech Institute for AI and Data, a structured approach has been shown to increase transparency significantly and reduce the risk of regulatory sanctions. The audit team concludes with a report containing concrete recommendations, such as adjusting the data pipeline or introducing monitoring for model drift.

What outputs an audit delivers

The output is a detailed report summarising identified deficiencies, proposing corrective steps and defining priority areas for improvement. Typical outputs include a list of data anomalies, recommendations for model retraining, a design for security controls and an implementation plan for monitoring mechanisms. The report also contains an assessment of compliance with the GDPR and other relevant regulations, which facilitates both internal and external audits. A practical example comes from an audit of an AI system at a Czech insurance company, where a model for detecting fraudulent insurance claims was found to have a high rate of false positives. The audit revealed weak data labels and recommended adding further variables, which reduced the error by 15 % and increased the insurer’s confidence in the results. The audit outcome therefore provides a clear plan for improving the quality, security and legal compliance of an AI solution, supporting long-term sustainability and user trust.

Why this matters for organisations outside Czechia

The examples in this article reference Czech financial institutions, but the audit framework applies directly to any organisation deploying AI in the European Union. The GDPR and the AI Act are EU-wide regulations; compliance requirements are identical across member states. A structured audit methodology that demonstrates conformity with these regulations reduces legal exposure and is increasingly expected by supervisors and business partners throughout the EU. The 15 % error reduction achieved in the insurance case illustrates the measurable operational benefit that accompanies regulatory alignment.