AI-Powered Anomaly Detection Systems for Insider Threat Prevention

Abstract

As company networks become more complicated and cyber-attacks more frequent, insider threats have surfaced as a considerable concern to organizational security. Artificial Intelligence (AI) have the capacity to transform how organizations discover and address insider threats by scrutinizing extensive network activity data and recognizing aberrant activities that may signify a threat. This study examines the utilization of AI-driven behavioral analysis for the detection of insider threats, emphasizing how machine learning and sophisticated data analytics may improve the discovery of nefarious behaviors within an organizational network. We examine diverse AI methodologies employed for behavior profiling, anomaly detection, and real-time surveillance. The research highlights the advantages, obstacles, and pragmatic factors of incorporating AI-driven systems into current security frameworks. Furthermore, we examine forthcoming trends and the influence of AI on the development of cybersecurity solutions to address internal threats.