Human behavior remains one of the most persistent contributors to cybersecurity incidents, as unintentional errors, social-engineering susceptibility, and insider misuse continue to bypass purely technical safeguards. This conceptual, literature-based study examines how artificial intelligence (AI) and machine learning (ML) techniques can be applied to analyze human behavioral patterns in order to reduce cybersecurity risk and human error within organizational information systems. Drawing on a structured review of insider-threat detection, phishing and social-engineering research, security-awareness training studies, and explainable-AI literature, the study synthesizes recurring behavioral indicators, modeling approaches, and evaluation practices reported across the field. A conceptual AI-based human-behavior-analysis framework is proposed, comprising data acquisition, behavioral feature engineering, AI/ML modeling, explainable risk scoring, and adaptive human-in-the-loop response. The review indicates that sequence-aware deep-learning models, such as long short-term memory networks and autoencoders, combined with explainable AI techniques, achieve strong reported detection performance while preserving analyst interpretability, and that adaptive, behavior-based training interventions are associated with measurably greater reductions in human-related risk than static, one-time awareness programs. The study concludes that integrating continuous behavioral analytics with human-centered training and feedback mechanisms offers a more resilient approach to cybersecurity risk management than technology-only or training-only strategies. Implications for organizational security governance, model interpretability, and future empirical validation are discussed.