AI-DRIVEN THREAT INTELLIGENCE FOR PREDICTIVE CYBER DEFENSE

Abstract

The increasing volume, velocity, and sophistication of cyber attacks have outpaced the capabilities of traditional, reactive security operations. Threat intelligence (TI) has emerged as a key enabler for proactive defense, yet manual analysis and rule-based systems struggle to scale and adapt to evolving threats. This paper examines how artificial intelligence (AI) can transform threat intelligence into a predictive capability for cyber defense. It explores AI-driven methods for ingesting heterogeneous security data, detecting patterns, predicting emerging threats, and automating response recommendations. The paper discusses core techniques—such as machine learning, deep learning, natural language processing, and graph analytics—and outlines a reference architecture for AI-driven threat intelligence platforms. Benefits, limitations, and ethical considerations are analyzed, along with use cases such as early ransomware detection, phishing campaign prediction, and attack-path analysis. The study concludes that AI-driven threat intelligence, when combined with human expertise and strong governance, can significantly enhance the accuracy, speed, and proactivity of cyber defense.