AI-Powered Behavioral Analysis for Insider Threat Detection in Enterprise Networks

Abstract

With the growing complexity of enterprise networks and the increasing frequency of cyber-attacks, insider threats have emerged as a significant risk to organizational security. Artificial Intelligence (AI) has the potential to revolutionize the way enterprises detect and mitigate insider threats by analyzing vast amounts of network activity data and identifying anomalous behaviors that may indicate a threat. This paper explores the application of AI-powered behavioral analysis for insider threat detection, focusing on how machine learning and advanced data analytics can enhance the identification of malicious activities within an enterprise network. We discuss various AI techniques used for behavior profiling, anomaly detection, and real-time monitoring. The study emphasizes the benefits, challenges, and practical considerations of integrating AI-based systems into existing security infrastructures. Additionally, we explore future trends and the role of AI in evolving cybersecurity strategies to combat insider threats.