Insider Threat Monitoring Frameworks: Leveraging Behavioral Analytics

Insider threats provide a significant risk to organizational security due to their access to essential systems and sensitive information. This article examines how behavioral analytics might improve insider threat monitoring systems, providing firms with preemptive methods to identify and mitigate potential risks. Utilizing machine learning and artificial intelligence (AI), behavioral analytics facilitates real-time monitoring and anomaly detection, hence enhancing organizational resilience. This study explores the issue statement, proposes a solution through behavioral analytics, and assesses its applications, effects, and extent. This study also addresses the problems and future prospects of behavioral analytics for insider threat detection, enabling firms to adapt to changing security environments. Emphasis is placed on incorporating behavioral models, ethical considerations, and organizational preparedness for implementing these solutions.