Insider Threat Behavior (Benign Vs Malicious)

Abstract

Insider threats remain one of the most complex and damaging security challenges in modern organizations. While much research has focused on identifying malicious insiders, there is limited understanding of benign insider behaviors that may unintentionally compromise organizational security. This study introduces a novel behavioral framework that differentiates between benign and malicious insider actions, incorporating psychological, behavioral, and organizational factors. Unlike previous studies that primarily rely on historical data or post-incident analysis, this research applies a mixed-methods approach combining real-time behavioral analytics, controlled simulations, and machine learning classification techniques to predict potential insider risks. The proposed methodology captures subtle indicators of risk, such as unusual access patterns, policy violations, and inadvertent procedural lapses, while distinguishing them from intentional malicious acts. The outcomes of this study provide organizations with a proactive and nuanced risk assessment tool, enabling tailored mitigation strategies that address both intentional threats and inadvertent errors. This research not only advances the theoretical understanding of insider behavior but also offers practical applications for improving organizational resilience.

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