Development of an AI-Driven Behaviour Analytics Model for Predicting Insider Threat in Organizational Networks 04 November 2026

Insider threats represent one of the most complex challenges in contemporary enterprise cybersecurity due to the legitimate credentials and elevated access privileges held by internal actors. Traditional perimeter-focused security mechanisms, including firewall policies and signature-based Intrusion Detection Systems (IDS), fail to detect malicious or inadvertent activities initiated from authorized endpoints. This paper presents a hybrid AI-driven User and Entity Behavior Analytics (UEBA) framework designed to predict and isolate insider threats across complex organizational networks. The proposed architecture integrates continuous multi-source data ingestion—comprising NetFlow records, system authentication logs, file interaction frequencies, and temporal activity matrices—into a dual-stage modeling pipeline. A recurrent sequence autoencoder captures baseline behavioral drift across temporal windows, while an Isolation Forest classifier identifies fine-grained anomalous deviations. Evaluated against enterprise benchmark telemetry, the model achieves a detection accuracy of 94.7% with a false positive rate of 2.1%, significantly outperforming static threshold models while mitigating Security Operations Center (SOC) alert fatigue.