UC Davis

This research introduces an innovative framework that integrates large language models (LLMs) with retrieval-augmented generation (RAG) systems and continuous threat intelligence feeds for real-time cybersecurity. Our approach overcomes the limitations of static threat analysis by providing dynamic, adaptive threat detection and response. Through comparative studies, we demonstrate that our system outperforms traditional methods, excelling in identifying both known and zero-day vulnerabilities. Detailed case studies, including device-specific vulnerabilities and actively-exploited CVEs in ransomware campaigns, highlight the framework's precision and reliability. This work bridges significant gaps in current cybersecurity solutions and sets the stage for future advancements in automated threat management.