AI-Based Behavioral Analysis for Detecting Insider Threats in Enterprise Networks

Abstract

Insider threats pose a significant risk to enterprise networks, potentially causing severe financial and reputational damage. Traditional methods of detecting insider threats, which often rely on rule-based systems and manual monitoring, are increasingly inadequate in the face of sophisticated attacks. This paper explores the use of artificial intelligence (AI) and machine learning (ML) techniques to enhance behavioral analysis for detecting insider threats. We review various AI-based approaches, assess their effectiveness, and discuss implementation challenges. Our findings highlight the potential of AI to improve threat detection accuracy while also identifying areas for future research.