This systematic literature review examines the evolving relationship between Artificial Intelligence (AI) and internal audit, with a particular focus on the role of auditing frameworks in guiding effective adoption. The review synthesizes findings from academic studies, professional standards, and industry reports to explore how AI-driven tools enhance risk assessment, fraud detection, control testing, and assurance activities. It highlights that while AI offers opportunities for increased efficiency, accuracy, and continuous auditing, its integration presents challenges related to data quality, model transparency, governance, and ethical considerations. Established auditing frameworks such as COSO, COBIT, and ISO-based standards play a critical role in supporting auditors as they navigate these complexities by offering structure for risk management, oversight, and implementation controls. The review concludes that a robust alignment between AI capabilities and auditing frameworks is essential to ensure accountability, reliability, and trust in AI-enabled internal audit functions.
Published Date: 2025-01-16; Received Date: 2024-09-18
