Large Language Models for Cybersecurity Intelligence, Threat Hunting, and Decision Support

Large language models (LLMs) have emerged as transformative technologies in cybersecurity, offering unprecedented capabilities in threat detection, vulnerability analysis, and intelligent decision-making. This review examines the application of LLMs across critical cybersecurity domains, including cyber threat intelligence (CTI), threat hunting, vulnerability detection, malware analysis, and decision support systems. The integration of LLMs such as Generative Pre-trained Transformer 4 (GPT-4), Bidirectional Encoder Representations from Transformers (BERT), Large Language Model Meta AI (LLaMA), and domain-specific models like SecureFalcon has demonstrated remarkable potential in automating complex security tasks, enhancing analyst productivity, and enabling proactive defense mechanisms. However, the deployment of LLMs in cybersecurity contexts introduces unique challenges, including prompt injection vulnerabilities, data poisoning risks, hallucination concerns, and ethical considerations regarding adversarial use. This paper synthesizes recent research advances, evaluates current LLM architectures and their security applications, examines real-world implementation challenges, and identifies critical gaps requiring further investigation. Through comprehensive analysis of over sixty recent studies, we highlight how LLMs are reshaping cybersecurity practices while emphasizing the necessity for robust security frameworks, continuous model validation, and responsible deployment strategies to mitigate emerging risks associated with these powerful artificial intelligence (AI) systems.