Insider threats pose significant risks to organizational cybersecurity, often arising from complex human behaviors that traditional detection systems struggle to identify (Smith & Johnson, 2023). This study proposes a novel, cross-disciplinary approach integrating artificial intelligence (AI) and behavioral analytics to enhance insider threat detection. By combining machine learning techniques with cognitive science principles, the framework captures nuanced behavioral patterns and psychological indicators that precede malicious insider activities (Lee et al., 2022). This work contributes to advancing proactive risk mitigation strategies by bridging technical cybersecurity defenses with human behavioral insights, for both researchers and practitioners.