Abstract

In an era of escalating cyber threats and evolving digital ecosystems, organizations must adopt robust and intelligent approaches to secure access to critical systems and data. Continuous Access Governance (CAG) has emerged as a vital framework that transcends traditional identity and access management (IAM) models by enabling real-time visibility, control, and auditability of user privileges across hybrid environments. This review explores the integration of Artificial Intelligence (AI) in enhancing CAG strategies, with a focus on real-time security monitoring and adaptive privilege management. The paper examines how AI-powered analytics, machine learning algorithms, and behavior-based models can dynamically assess access risks, detect anomalies, and enforce context-aware access decisions. It further evaluates the effectiveness, challenges, and limitations of current AI-driven CAG solutions while identifying emerging trends and future directions. Through a synthesis of scholarly literature and industry best practices, this paper provides a comprehensive overview of how AI transforms continuous access governance from a static, periodic process into a proactive, intelligent security function.