Insider Threat Detection using Agentic AI

Abstract: Organizations today rely heavily on digital infrastructure to store and manage sensitive information such as financial records, intellectual property, and confidential documents. While traditional cybersecurity solutions effectively protect against external attacks, threats originating from authorized internal users remain difficult to detect. Insider threats occur when employees, contractors, or partners misuse their legitimate access privileges either intentionally or unintentionally. Conventional security systems mainly rely on rule-based monitoring and signature detection, which often fail to identify subtle behavioral anomalies.

This research proposes an intelligent insider threat detection framework using Agentic Artificial Intelligence. The system utilizes autonomous AI agents that continuously monitor user behavior across multiple data sources including system logs, network traffic, and file access records. Machine learning techniques are used to build behavioral profiles and detect deviations from normal patterns. The agentic architecture enables reasoning over anomalies and supports automated threat assessment.

Experimental evaluation using publicly available cybersecurity datasets demonstrates that the proposed approach improves threat detection accuracy and reduces false positives compared to traditional systems. The system provides real-time alerts and contextual explanations, enabling security teams to respond quickly to potential risks. This research highlights the potential of Agentic AI to enhance modern cybersecurity systems by enabling proactive detection and intelligent response to insider threats.

Keywords: Insider Threat Detection, Agentic AI, Cybersecurity, Behavioral Analysis, Machine Learning, Anomaly Detection, User Activity Monitoring.