AI-Driven Threat Hunting System

The escalating sophistication and volume of cyber threats have rendered conventional security measures increasingly inadequate, necessitating a paradigm shift toward proactive, intelligent defense mechanisms. This paper provides a comprehensive analysis of Artificial Intelligence (AI)-driven threat hunting systems that leverage machine learning (ML), deep learning (DL), and autonomous response technologies to preemptively identify, analyze, and mitigate advanced cyber threats. Through an examination of contemporary literature and industry implementations, we explore the architectural components, operational methodologies, and practical applications of these systems across diverse cybersecurity environments. Our findings indicate that AI-enhanced threat hunting significantly reduces mean time to detection (MTTD), improves accuracy in identifying novel and polymorphic threats, and enhances operational efficiency through automation. However, significant challenges persist, including false positives, adversarial attacks on AI models, and integration complexities. This paper concludes with an assessment of future directions, including explainable AI (XAI) and quantum computing, and their implications for organizational security postures in an increasingly hostile digital landscape.