AI Audit: Definition and Examples

An AI Audit (or artificial intelligence audit) refers to the methodical and thorough examination of an AI system to evaluate its performance, potential biases, regulatory compliance, and alignment with defined objectives. This process is inspired by traditional auditing practices applied to finance or IT security, but adapted to the specificities of algorithms and machine learning models.\n\nThe audit can cover several dimensions: quality of training data, robustness of the model, fairness of the results produced (absence of discrimination), transparency of decisions (explainability), as well as compliance with applicable regulations such as the European AI Act. It can be carried out internally by the organization deploying the system, or by an independent third party to ensure evaluation objectivity.\n\nIn the context of prompt engineering, AI Audit takes on a particular dimension: it also involves evaluating how prompts influence model responses, detecting cases where the system produces biased or inaccurate results, and documenting observed limitations. A rigorous prompt audit helps identify formulations that generate hallucinations or discriminatory responses.\n\nWith the rise of AI regulation globally, AI Audits are becoming an essential practice for any organization deploying AI systems in production, particularly in sensitive areas such as healthcare, finance, recruitment, or justice.