As cyber threats evolve in complexity and frequency, the need for timely, accurate, and actionable Cyber Threat Intelligence (CTI) has never been greater. However, traditional CTI processes often struggle with the overwhelming volume of unstructured data and the demands placed on human analysts. Large Language Models (LLMs) offer a promising solution by automating tasks like data extraction, summarization, and threat analysis. This presentation will explore the intersection of CTI and LLMs, examining how these models can enhance threat detection, reporting, and operational efficiency. It also addresses the limitations and risks of relying on LLMs in security-sensitive environments, including concerns around accuracy, data privacy, and model transparency. By evaluating both the opportunities and challenges, this presentation aims to provide a balanced perspective on the role of LLMs in the future of CTI.
