ABSTRACT
The rapid growth of sophisticated cyberattacks has exposed the limitations of conventional security architectures in protecting next-generation enterprise networks. This study proposes an AI-driven Autonomous Cyber Threat Hunting and Adaptive Zero-Trust Defense Framework designed to provide proactive threat detection, continuous risk assessment, and dynamic access control. The proposed framework integrates network traffic monitoring, endpoint telemetry collection, behavioral analytics, and threat intelligence feeds into a unified security ecosystem. Advanced machine learning and deep learning models are employed to identify anomalous activities, detect previously unseen attack patterns, and predict potential security breaches in real time. Simultaneously, the adaptive Zero-Trust mechanism continuously evaluates user, device, and application trust levels, enforcing context-aware authentication and least-privilege access policies.The methodology consists of data acquisition, preprocessing, feature extraction, AI-based threat classification, autonomous threat hunting, adaptive policy generation, and automated response orchestration. Experimental evaluation demonstrates significant improvements in threat detection accuracy, attack containment speed, and reduction of false-positive alerts compared with conventional security monitoring approaches. Results indicate enhanced resilience against ransomware, insider threats, advanced persistent threats (APTs), and lateral movement attacks. Furthermore, the framework enables continuous security adaptation in highly dynamic enterprise environments while minimizing administrative overhead. The proposed solution provides a scalable, intelligent, and autonomous cybersecurity architecture capable of strengthening organizational defenses against evolving cyber threats and supporting secure digital transformation initiatives.
Keywords: Artificial Intelligence Cybersecurity, Autonomous Threat Hunting, Zero-Trust Security Architecture, Machine Learning-Based Intrusion Detection, Adaptive Access Control, Enterprise Network Security
