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Insider Threats Remain A Critical And Growing Risk To Organizations As Attackers Exploit Valid Credentials, Careless Behaviour, And Increasingly, Generative AI To Scale And Obfuscate Malicious Activity. Recent Surveys Report That A Large Majority Of Organizations Experienced Insider Incidents Within The Last Year, And Financial Impacts Continue To Rise. Contemporary Detection Approaches Exhibit Three Converging Trends. First, Behavior-centric Analytics—notably User And Entity Behavior Analytics (UEBA)—are Gaining Traction As Essential Complements To Traditional Controls (IAM, DLP, EDR) Because They Detect Subtle Contextual Deviations Rather Than Solely Rule-triggered Events. Second, Machine Learning And Deep-learning Techniques (including CNNs, Transformer Architectures And Hybrid Models) Are Increasingly Used To Model Complex Temporal And Contextual Patterns In User Activity, Improving Detection Sensitivity But Raising Challenges In Explainability, Dataset Bias, And Privacy. Third, There Is Growing Emphasis On Trustworthy, Privacy-preserving And Explainable Detection Pipelines—federated Learning, Model Interpretability Tools, And Human-in-the-loop Workflows—to Balance Detection Performance With Legal/ethical Constraints And Operational Acceptability. We Conclude That Effective Insider Risk Programs Will Be Pragmatic And Multidisciplinary: Combining Advanced Analytics, Clear GenAI Governance, Data-minimizing Architectures, Workforce Education, And Incident-response Maturity. Future Research Should Prioritize Realistic, Diverse Datasets, Interpretable Models, And Measurable ROI To Aid Adoption In Resource-constrained Environments.