User Behaviour Analytics in SASE: An Architectural Framework for Insider Threat Detection

This article presents a comprehensive framework for integrating User Behavior Analytics (UBA) with Secure Access Service Edge (SASE) architectures to enhance insider threat detection capabilities in modern enterprises. The article explores the evolution of security approaches from traditional methods to advanced behavioral analytics, emphasizing the crucial role of machine learning in identifying and mitigating security risks. Through detailed analysis of implementation strategies, behavioral components, and architectural considerations, this article demonstrates how UBA-SASE integration provides organizations with robust security measures while maintaining operational efficiency. The article examines various aspects of behavioral analysis, including data collection methods, baseline development processes, and contextual factor analysis, while highlighting the importance of continuous learning and model optimization in maintaining effective security postures. This article contributes to the field by providing a structured approach to implementing and managing UBA within SASE frameworks, offering insights into best practices, emerging capabilities, and future directions for enterprise security architectures.