The increasing complexity and scale of cyber threats have pushed Cyber Threat Intelligence (CTI) beyond the capabilities of traditional rule-based systems. This article explores how Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and graph-based analytics, is reshaping the CTI landscape. By automating threat data processing, enhancing attribution, and enabling predictive capabilities, AI is transforming CTI into a proactive and scalable discipline. By analysing CTI architectures, real-world use cases, platform comparisons, and current limitations, this study highlights the emerging opportunities and challenges at the intersection of cybersecurity and AI. This analysis concludes that the future of CTI lies in hybrid systems that seamlessly combine human expertise with intelligent automation.
Barrios-González et al. (Fri,) studied this question.