Behavioral Analytics and AI in Zero Trust Security: A Framework for Adaptive Identity and Access Management

Abstract

The advent of cloud computing, remote work, and increasingly sophisticated cyberattacks has rendered perimeter-based security models insufficient, prompting a global transition toward Zero Trust Security (ZTS). Central to ZTS is the principle of "never trust, always verify”, which underscores continuous authentication and dynamic access control. However, traditional Identity and Access Management (IAM) systems often lack the flexibility to address evolving behavioural anomalies and insider threats. This study proposes a comprehensive framework that integrates behavioural analytics and Artificial Intelligence (AI) to enhance adaptive IAM in Zero Trust environments. By leveraging user and entity behaviour analytics (UEBA) and machine learning models, the framework continuously monitors contextual signals, such as login patterns, device usage, and network activity, enabling proactive risk scoring and real-time access decisions. This study synthesises the existing literature, identifies the current limitations of Zero Trust IAM, and develops a layered architecture that combines behavioural monitoring with AI-driven decision-making to achieve continuous verification. The findings highlight the potential of AI-enhanced behavioural analytics to improve detection accuracy, reduce false positives, and automate the enforcement of adaptive policies. This research contributes to advancing secure, scalable, and context-aware zero-trust IAM strategies, offering a roadmap for implementation across enterprises, government systems, and multi-cloud infrastructures.