AI in audit data analysis explores the benefits, risks, and limitations of GenAI, highlighting when auditors should use AI, avoid it, and apply human oversight for accurate, reliable results.
Compliance10 juni, 2026
AI in audit data analysis: Risks, limitations, and best practices
Belangrijkste inzichten
- AI in audit enhances efficiency but requires human oversight to ensure accuracy, reliability, and audit defensibility.
- Generative AI excels at analyzing unstructured data like emails and documents, helping auditors identify risks and patterns faster.
- AI has limitations in structured data analysis, making it unsuitable for precise calculations and repeatable audit procedures.
- Key audit risks of AI include hallucinations, variability, and lack of transparency, which can impact audit quality and trust.
- Best practices for AI in internal audit include using enterprise tools, protecting data security, and validating outputs with professional skepticism.
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