A Modular Framework for Insider Threat Detection and Analysis

Abstract

Insider threats remain a critical and frequently underestimated challenge in cybersecurity, particularly due to their origin from authorized users and the limitations of traditional detection approaches. Addressing this problem requires not only detection mechanisms, but also integrated approaches for security event analysis and decision support. This study presents the design and implementation of a modular framework for the detection and analysis of insider threats in organizational environments. The proposed system integrates a configurable event simulator, a data ingestion module, an analytical processing API, and an interactive dashboard, forming an end-to-end pipeline for security event analysis. This architecture enables the systematic processing and interpretation of security events, supporting monitoring and decisionmaking processes. The main contribution of this work lies in the development of an integrated and interpretable analytical framework, combining event classification, weighted risk scoring, and normalization to provide transparent and actionable insights for insider threat management. The system was evaluated in controlled environments using synthetic data derived from the CERT dataset, enabling systematic experimentation in the absence of real-world data. The results demonstrate the system’s capability to detect anomalous behaviors, identify risk patterns, and operate efficiently under high-volume scenarios. These findings highlight the feasibility of the proposed approach as a practical foundation for modular insider threat analysis systems in organizational contexts.