AI-based insider threat detection focuses on identifying suspicious user activities that may compromise the confidentiality or security of sensitive information within Linux systems. It involves monitoring file-access events, analyzing user activity patterns, examining audit logs, and identifying deviations from expected behavior that may indicate unauthorized access or potential data exfiltration. By combining machine learning-based classification with sequential behavior analysis and explainable AI techniques, this approach helps detect suspicious interactions with sensitive files, identify abnormal activity patterns over time, and interpret the factors contributing to threat predictions. The resulting analysis supports timely investigation, evidence-based risk assessment, and the implementation of appropriate access controls and monitoring measures to reduce insider-related security risks.