AI-Powered Audit Analytics: Improving Risk Detection in IT Service Management
DOI:
https://doi.org/10.63345/sjaibt.v2.i4.203Keywords:
AI audit analytics; IT Service Management; risk detection; anomaly detection; audit automationAbstract
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.
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References
• ISACA. Audit Practitioner’s Guide to Machine Learning — Part 2: Compliance & Risk. Practical guidance for IT auditors assessing ML-related compliance and risk across the ML lifecycle. ISACA
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