Integrating Ueba and Machine Learning for Early Insider Threat Detection in Enterprise Environments

Enterprise security architectures increasingly depend on behavioral analytics and machine learning to detect insider threats that evade traditional perimeter-based controls. As organizations accelerate digital operations and consolidate telemetry across hybrid infrastructures, User and Entity Behavior Analytics (UEBA) emerges as one of the most effective mechanisms for early identification of anomalous user patterns linked to compromised accounts, privilege misuse, fraudulent access pathways or stealthy exfiltration activities. The study investigates how MLsupported UEBA systems restructure detection pipelines, improve contextual risk scoring, and reduce detection latency through probabilistic modeling of behavioral baselines. The research synthesizes recent academic findings, industrial implementations and experimental frameworks, including adaptive behavior-scoring architectures described in contemporary cybersecurity monographs (Dashevskyi, 2025). The paper also examines model drift, data quality constraints, cross-domain aggregation issues and explainability limitations that influence the practical deployment of UEBA systems. An integrated ML-driven UEBA model is presented, including temporal profiling, event correlation, anomaly scoring and risk-based response escalation. Results indicate that combined behavioral and ML models significantly outperform rule-based detection in environments with dynamic access patterns and heterogeneous user groups, offering a scalable approach for early insider-threat mitigation.