Leveraging Artificial Intelligence in Next-Generation Cyber Threat Intelligence Platforms

Abstract

As cyberattacks become more targeted and destructive, artificial intelligence (AI) has emerged as a cornerstone of next-generation threat intelligence platforms. AI technologies such as deep learning, reinforcement learning, and graph neural networks are capable of detecting subtle anomalies that escape traditional signature-based systems. This paper examines the role of AI in enhancing cyber situational awareness, threat attribution, and attack prediction. The research investigates the integration of AI with security information and event management (SIEM) systems, intrusion detection systems (IDS), and endpoint detection and response (EDR) solutions. Furthermore, the study evaluates the limitations of current AI-driven approaches, particularly regarding adversarial machine learning attacks where threat actors attempt to deceive AI classifiers. A comprehensive case study is provided on AI-based detection of polymorphic ransomware campaigns, highlighting how AI-driven pattern recognition can outperform manual rule-based systems. The discussion emphasizes ethical considerations, explainability challenges, and the necessity for hybrid human-AI decision making models in cyber defense.

KEYWORDS: Artificial Intelligence, Cyber Threat Intelligence, Deep Learning, Anomaly Detection, Adversarial Attacks