Behavioral Analytics for Detecting Insider Threats in Governmental Organizations: A Human-Centric Approach

Abstract

Behavioral analytics for detecting insider threats in governmental organizations represents a critical area of research focused on identifying malicious or negligent activities from within an organization, often by individuals with authorized access. Traditional security measures, such as firewalls and network monitoring, are insufficient on their own, as they do not capture the complex patterns of human behavior that are often indicative of insider threats. This abstract explores a human-centric approach to behavioral analytics, emphasizing the importance of analyzing user activities, interactions, and behavioral anomalies to detect potential threats. By incorporating machine learning algorithms and advanced data analytics techniques, organizations can establish baseline behavior patterns for individuals, allowing for real-time detection of deviations that could indicate malicious intent or accidental violations. This approach focuses not only on system access logs but also on psychological and behavioral indicators, such as unusual working hours, changes in communication patterns, and access to sensitive data beyond normal job requirements. The goal is to develop a more nuanced understanding of risk by integrating both technological and human-centric factors, recognizing that insider threats often emerge from a blend of personal, social, and professional dynamics. Moreover, such analytics can be used proactively to identify potential vulnerabilities and address them before a serious security breach occurs. This human-centric model, while primarily data-driven, incorporates feedback loops from security teams and human resource departments, allowing for a more comprehensive, multidisciplinary perspective on potential risks. It also seeks to balance security with privacy concerns, ensuring that behavioral analytics are implemented in a way that respects individual rights while protecting sensitive governmental data. Ultimately, the proposed approach offers a more adaptive, dynamic, and effective strategy for mitigating insider threats within governmental organizations, creating a security framework that evolves in tandem with the changing landscape of digital and organizational behaviors.