AI And Behavioral Analytics for Insider Threat Detection: A Comprehensive Review of Techniques, Datasets, and Emerging Challenges

Abstract

Insider threats remain one of the most challenging and damaging risks in organizational cybersecurity, often bypassing traditional security controls due to their legitimate access and familiarity with internal systems. Recent advancements in artificial intelligence (AI) and behavioral analytics provide promising solutions for proactive detection of such threats by modeling user behavior, identifying anomalies, and predicting potential malicious actions. This article presents a comprehensive review of AI-driven approaches for insider threat detection, encompassing machine learning, deep learning, and hybrid models, with a focus on behavioral profiling, feature engineering, and real-time analytics. We systematically analyze publicly available and proprietary datasets commonly used in the research community, highlighting their characteristics, limitations, and suitability for various detection approaches. Furthermore, the review identifies emerging challenges, including data scarcity, privacy concerns, model interpretability, and scalability in dynamic enterprise environments. By synthesizing existing methodologies and outlining key research gaps, this study aims to guide future work towards more robust, explainable, and adaptive insider threat detection frameworks.