Conducting internal audits on IT infrastructure is an essential process for all organizations. Many organizations conduct it manually and face issues like manual errors, especially in large organisations’ dynamic and complex infrastructure. This research explores how AI could profoundly revolutionize internal audits in IT by enhancing accuracy, efficiency, and compliance verification while eliminating traditional audit induction issues like inefficacy, human error, and rigidity in adapting to changes in regulatory requirements. The automation in AI-powered auditing shapes the real-time verification of compliance along the path of improved identification of anomalies and, therefore, advances the assessment of risk and allocation of resources. Challenges like data privacy issues, lack of explainability in AI models, regulatory compliance complexities, and infrastructure limitations hold back large-scale adoptions of such measures. The study proposes an AI-driven audit Framework, where artificial intelligence techniques such as ML, NLP, and automated anomaly detection are incorporated into the IT’s internal audit lifecycle, from initial planning and scoping to continuous monitoring. The research underlined the need for future researchers to consider topics like explainable AI, empirical validation, AI blockchain integration, adaptive compliance frameworks, and the socio-technical impact of AI in auditing. Addressing these challenges will ensure that AI-driven IT audits will be more transparent regarding security and efficiency and redefine audit methodologies to suit the evolving demands of cybersecurity and compliance regulation.