Insider threats, caused by malicious or negligent actions of employees, contractors, or trusted users, pose a significant challenge to organizational cybersecurity. Traditional security measures, including access control and periodic audits, often fail to detect subtle deviations in user behavior that indicate potential insider risks. AI-based behavioral monitoring has emerged as a transformative solution, leveraging machine learning, anomaly detection, and predictive analytics to identify unusual patterns, deviations, and risky activities in real time. By analyzing user interactions, access patterns, and contextual data, AI systems can generate dynamic risk scores, prioritize alerts, and guide security teams in taking proactive measures. This review examines the conceptual foundations, architectural frameworks, enabling technologies, and operational methodologies that underpin AI-driven behavioral monitoring. It highlights the techniques used to detect insider threats, including supervised and unsupervised learning, clustering, sequence analysis, and predictive modeling. The paper also discusses real-world applications across industries such as finance, healthcare, and critical infrastructure, demonstrating measurable improvements in threat detection, incident response, and compliance. Additionally, challenges such as data privacy, model interpretability, and false positives are analyzed. Finally, the review explores future directions, including explainable AI, adaptive learning, and privacy-preserving monitoring, positioning AI-based behavioral monitoring as a strategic enabler for proactive, resilient, and context-aware insider threat management.