International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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

Author(s) Namboodiri Arun Mullamangalath Kesavan
Country United States
Abstract 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.
Keywords User Behavior Analytics; SASE Architecture; Insider Threat Detection; Behavioral Analysis; Machine Learning Security
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-24
Cite This User Behaviour Analytics in SASE: An Architectural Framework for Insider Threat Detection - Namboodiri Arun Mullamangalath Kesavan - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.31267
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.31267
Short DOI https://doi.org/g8r8j6
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