The Moderating Impact of Cybersecurity Risks on the Relationship Between Artificial Intelligence (AI) and Internal Audit Quality (IAQ): Evidence from Jordan

This research pinpoints the relationship between artificial intelligence and the internal auditing quality in Jordanian commercial banks, and whether this relationship is impacted by cybersecurity risks as a moderating variable. A questionnaire is designed and distributed to the research population consisting of 13 ASE-listed banks between (2016) and (2022) to achieve the research objectives. Quite a few statistical methods have been used that are commensurate with the research objectives. The findings indicate that the elements of artificial intelligence represented by expert systems (ES), knowledge-based systems (KBS), inference (1), and machine learning (ML) positively affect the internal auditing quality. The results also show that cybersecurity risks affect the internal auditing quality. However, the findings demonstrate that cybersecurity risks have no significant effect on the relationship between artificial intelligence and the internal auditing quality in commercial banks. Given these findings, the research recommends that banks should pay more attention to artificial intelligence tools thanks to their significance in improving and developing internal auditing quality and work hard to find mechanisms that limit cybersecurity risks.