Integration of Generative Large Language Models (LLM-GAI) for Augmenting Machine Reasoning in Critical Cyber Threat Intelligence (CTI)

Conventional Network Security Intelligence (NSI) faces an epistemological constraint in contextualizing the exponential volume of data. This paper proposes and conceptually validates a Cognitive Security Augmentation (CSA) framework that integrates Generative AI (GAI) based on Large Language Models (LLM), such as the model underpinning ChatGPT, to enhance Machine Reasoning and incident narrative synthesis. The core hypothesis is that GAI can bridge the gap between data-to-information (structured SIEM output) and information-to-intelligence (contextual reasoning and attack attribution). We model the GAI integration to automate the correlation of polymorphic anomalies with high-level MITRE ATT&CK techniques and generate personalized response playbooks, thereby significantly reducing the Mean Time To Understand (MTTU) for incidents. This research highlights a paradigm shift from passive anomaly detection towards proactive hypothesis generation within the CTI domain. Keywords: Generative AI (GAI), Large Language Model (LLM), Machine Reasoning, Cyber Threat Intelligence (CTI), Security Epistemology, MITRE ATT&CK