Abstract & Overview
IT Service Management (ITSM) remains a critical domain in organizations, yet auditing and risk detection in ITSM processes often lag due to the volume, velocity, and complexity of IT operational data. This paper proposes an AI-powered audit analytics framework tailored for ITSM to enhance risk detection capabilities. We review relevant literature, specify a methodology utilizing machine learning models, and present a statistical analysis on simulated ITSM audit data. The results suggest that anomaly detection models outperform traditional rule-based triggers in identifying risk incidents. We identify key gaps—such as explainability, integration with legacy systems, and domain adaptation—and conclude with recommendations for practice and future research.