Artificial Intelligence in Cybersecurity: A Comprehensive Survey on AI-Driven Insider Threat Detection

Abstract: Insider threats are one of the most difficult cybersecurity problems organizations face today. Unlike attacks that come from outside, insider threats involve people who already have authorized access — employees, contractors, or trusted partners — who either deliberately misuse that access or unknowingly create security risks. Because these individuals operate within normal system boundaries, traditional security tools like firewalls tend to miss them entirely. This paper looks at how artificial intelligence is being used to tackle this problem, drawing on published research from IEEE Xplore, ACM Digital Library, Springer, and ScienceDirect. We looked at a range of approaches — deep learning, graph-based analysis, User Behavior Analytics (UBA), Support Vector Machines, rule-based methods, and even psychosocial behavioral modeling. To make sense of this variety, we put together a four-tier framework that organizes these systems from the simplest rule-based tools all the way up to fully adaptive AI platforms. We also measured how these systems perform in terms of detection accuracy, false alarm rates, scalability, and speed. One finding kept coming up: no existing system brings together real-time monitoring, automated risk scoring, explainable outputs, and adaptive learning in a single working platform. We explore why this gap exists and what it would take to close it.

Keywords: Artificial Intelligence; Cybersecurity; Insider Threat Detection; Machine Learning; Deep Learning; User Behavior Analytics; Anomaly Detection; Graph-Based Detection; Support Vector Machine; LSTM; Risk Scoring; Behavioral Analysis; Explainable AI; Real-Time Monitoring; Intrusion Detection Systems.