A Dynamic AI Maturity Model for Agile Audit: A Roadmap for Enhanced Effectiveness and Innovation

Abstract

The intersection of Artificial Intelligence (AI) and agile methodologies is transforming information systems audit by enabling real-time risk assessment, anomaly detection, and automated control testing. These capabilities enhance the security, efficiency, and reliability of IT environments. This article introduces a dynamic AI maturity model for agile audit, structured into five levels of AI integration. Each level reflects increasing AI capabilities and outlines key transition points. The model supports strategic AI adoption across various audit domains, including data analysis, cybersecurity, compliance monitoring and fraud detection. We validate this model using interviews and a case study in a public-sector audit institution. Ethical concerns such as transparency, fairness, and accountability are integrated, recognizing the potential impact of AI on privacy, compliance, and governance. By applying this maturity model, organizations can systematically strengthen their agile audit practices while maintaining control over their information systems.