Abstract

Enterprise cybersecurity has entered a phase in which many successful attacks no longer rely on easily identifiable malware artifacts. Instead, adversaries increasingly employ advanced persistent threats, insider misuse, credential compromise, and fileless techniques that exploit legitimate system functionality and trusted access pathways. These methods allow malicious activity to blend into normal operational behavior and remain undetected for extended periods. Large-scale breach investigations consistently demonstrate that prolonged attacker dwell time is strongly associated with increased financial loss, operational disruption, and regulatory exposure. In response, organizations are shifting toward proactive detection paradigms centered on threat hunting and behavioral anomaly detection, particularly through User and Entity Behavior Analytics. This paper presents a comprehensive analytical and empirical review of these approaches. It examines their theoretical foundations, operational workflows, machine learning techniques, and integration within modern security operations. An empirical evaluation framework is proposed to assess detection latency, alert precision, and analyst workload in environments that deploy integrated hunting and behavioral analytics. Key operational challenges, including data scalability, behavioral drift, false positives, adversarial machine learning risks, skill shortages, and regulatory constraints, are analyzed with reference to peer-reviewed literature and industry studies. Emerging developments such as explainable artificial intelligence, AI-assisted hunting, Zero Trust integration, and cloud-native security architectures are also discussed. The analysis indicates that, when implemented within mature governance structures and supported by high-quality telemetry and skilled analysts, the integration of threat hunting and behavioral anomaly detection substantially enhances enterprise detection capabilities and cyber resilience.