Integration of SIEM Data Analytics and AI for Proactive Cyber Threat Hunting

Abstract: The ever-shifting landscape of cyber threats is always on the move, with APTs, insider attacks, and zero- day attacks at the forefront. Conventional, rule-based SIEMs—until now, the workhorse of many security operations— demonstrate their limitations in the face of such threats. They can wade through massive amounts of security data, but they tend to vomit out lots of false positives and lack the ability to predict what’s around the corner. This research investigates how SIEMs might do more than simply respond to threats: it examines the use of AI and other forms of advanced analytics to predict future intrusions. To improve the signal-to-noise ratio, add context, and accelerate response times, the proposed solution relies on behavioral analytics, anomaly detection, machine learning, and automated threat intelligence enrichment. The research describes an analytics workflow, an AI-based SIEM solution, and a methodology for comparing these AI-infused systems to traditional systems. The findings indicate that as AI continues to evolve, AI-based SIEM solutions enable organizations to concentrate on threats that matter, minimize the need for continuous human interaction, and identify complex or unexpected attacks earlier.

Keywords: Intrusion Detection Systems (IDS), anomaly detection, alert prioritization, cyber threat intelligence, security information and event management (SIEM), predictive threat hunting, behavioral analytics, data analytics, and machine learning.