Explainable AI for Insider Threat Detection: Bridging the Trust Gap in Security Operations Centers

Insider threats represent a critical security challenge for organizations, accounting for substantial financial losses and reputational damage.While machine learning and deep learning approaches have demonstrated high detection accuracy in identifying malicious insider activities, they suffer from inherent opacity, functioning as "black boxes" that provide limited interpretability to security analysts.This research presents a comprehensive framework integrating Explainable Artificial Intelligence (XAI) techniques with behavioral analytics for insider threat detection in Security Operations Centers (SOCs).The framework combines multi-modal data fusion (network traffic, user behavior logs, and access control events) with state-of-the-art machine learning models, enhanced by SHAP and LIME-based explanation mechanisms.A human-in-the-loop validation system enables security analysts to provide feedback, refine model explanations, and improve overall threat detection performance.Our investigation addresses three critical research questions: (1) How can model complexity be balanced with explainability in real-time threat detection?(2) What explanation formats prove most effective for SOC analysts?(3) Can explainability reduce false positives and accelerate incident response times?Through systematic literature review and framework design, we identify key challenges, including class imbalance, heterogeneous data integration, and the absence of standardized explainability metrics in cybersecurity.This paper contributes to the advancement of transparent, trustworthy AI-driven security systems, fostering improved collaboration between human analysts and intelligent machines.