Detecting insider threats in banking using AI-driven anomaly detection with a data science approach to cybersecurity

The banking sector faces an increasingly critical challenge in detecting and preventing insider threats, which account for significant financial losses and data breaches annually. This comprehensive review explores how artificial intelligence-driven anomaly detection, integrated with advanced data science approaches and cybersecurity frameworks, is transforming insider threat detection in banking institutions. By synthesizing current research in behavioral analytics, machine learning methodologies, and employee activity monitoring, the study examines how AI-driven technologies are revolutionizing traditional approaches to insider threat detection and risk management. The review critically analyzes emerging AI-driven methodologies, particularly focusing on unsupervised learning techniques, behavioral pattern analysis, and real-time employee activity monitoring systems. Through an extensive examination of behavioral analytics frameworks, privileged access monitoring, and user entity behavior analytics (UEBA), the research illuminates both the potential and challenges of AI-powered insider threat detection. The investigation reveals significant advancements in behavioral anomaly detection, predictive modeling of employee activities and network behavior analysis while simultaneously addressing critical privacy considerations and regulatory complexities specific to employee monitoring.