The insider threat: a socio-technical analysis of preventing data breaches and espionage within governmental agencies
Year: 2025
Type: article
Abstract: This article presents a socio-technical analysis of the insider threat phenomenon within governmental and public sector institutions. It argues that effective mitigation requires a dynamic, integrated strategy that moves beyond siloed technical controls to holistically address the interplay between individual psychology, organizational culture, technical architecture, and policy enforcement. The analysis defines the governmental insider threat, distinguishing between malicious, unintentional, and compromised insiders, and demonstrates how this typology maps to distinct root causes within the socio-technical system. Through a detailed examination of the Edward Snowden and Chelsea Manning cases, the article deconstructs the convergence of psychological, cultural, and technical vulnerabilities that precipitate catastrophic breaches. It systematically analyzes contributory factors at the individual level, using the Critical Pathway to Insider Risk (CPIR) model; the organizational level, focusing on culture, leadership, and trust; and the technical level, highlighting architectural weaknesses. The article then evaluates a multi-layered defense-in-depth framework integrating human-centric strategies (e.g., positive deterrence, robust training), technical countermeasures (e.g., Zero Trust Architecture, User and Entity Behavior Analytics), and comprehensive policy frameworks (e.g., Executive Order 13587, NITTF Maturity Framework). The inherent tension between security surveillance and employee privacy is explored, reframing privacy protection as a positive driver of organizational trust and security. The article culminates in a novel, coordinated intervention model and provides actionable policy recommendations for governmental agencies to build a more resilient and secure posture against the threat from within..
Source: Politics & Security
Author: Mykhailo Lishchynsky
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414692680