Cyber Threat Intelligence: Leveraging AI for Predictive Analytics in Hybrid Cloud Systems

Abstract

As hybrid cloud environments become the backbone of enterprise IT infrastructure, they introduce complex and evolving threat landscapes that challenge traditional cybersecurity approaches. Cyber Threat Intelligence (CTI) plays a vital role in identifying, analyzing, and mitigating these threats. The increasing volume, velocity, and variety of threat data demand more advanced, automated solutions. This paper explores the integration of Artificial Intelligence (AI) into CTI to enable predictive analytics within hybrid cloud systems. I examine how AI techniques such as machine learning, deep learning, and natural language processing can enhance threat detection, behavioral analysis, and proactive risk mitigation. The paper proposes a scalable framework for AI-driven CTI that supports real-time data ingestion, cross-domain threat correlation, and dynamic risk scoring, all tailored for hybrid environments. Real-world use cases demonstrate the efficacy of these methods in identifying sophisticated threats before they escalate. Ethical considerations, including data privacy and model bias, are also discussed. By harnessing AI for predictive CTI, organizations can shift from reactive to proactive defense strategies, significantly improving their security posture. This research offers both practical insights and a foundation for further innovation at the intersection of AI and cybersecurity in hybrid cloud ecosystems.