AI Augmented Threat Intelligence Systems

Abstract

ABSTRACT The increasing complexity and volume of cyber threats have rendered traditional threat intelligence systems inadequate. AI augmented threat intelligence systems leverage machine learning, natural language processing, and big data analytics to provide automated, predictive, and context-aware threat detection and mitigation. This paper provides a comprehensive review of AI-powered threat intelligence frameworks, their architectures, analytical techniques, and practical applications. We examine key methodologies such as supervised and unsupervised machine learning, anomaly detection, and predictive modeling within the threat intelligence ecosystem. Additionally, the paper addresses emerging trends, challenges, and future directions, emphasizing the need for adaptive, scalable, and ethically guided AI systems. Illustrative tables and figures summarize comparative approaches, threat classifications, and system architectures.

KEYWORDS: AI Threat Intelligence, Cybersecurity, Machine Learning, Anomaly Detection, Predictive Analytics, Automated Threat Mitigation