Harnessing Large Language Models for Real-Time Cyber Threat Detection and Response: A Comprehensive Survey

Large Language Models (LLMs) have recently gained recognition as transformative tools across various domains, especially in cybersecurity. This survey explores the state-of-the-art applications of LLMs in tackling complex and rapidly evolving cyber threats. By synthesizing insights from recent literature, we analyze how LLMs enable real-time threat detection, detailed incident analysis, and actionable mitigation strategies. Compared to traditional cybersecurity approaches, LLMs demonstrate notable advancements in detection accuracy and response efficiency. We offer a comprehensive evaluation of the capabilities and limitations of LLM-based methodologies, spotlighting real-world use cases where these models have shown exceptional effectiveness. Additionally, we identify critical research challenges and provide future directions to enhance the performance, interpretability, and safety of LLM-driven cybersecurity systems.