Current network intrusion detection systems (NIDS) struggle to keep pace with the sophistication and evolving nature of cyberattacks. Traditional signature-based and rule-based systems are often brittle and easily bypassed, while anomaly-based systems suffer from high false positives. This leaves an alarming gap in network security, exposing organizations to data breaches, financial losses, and reputational damage.

We propose the development of a full-stack Large Language Model (LLM) product designed to revolutionize threat analysis through the comprehensive examination of system and network logs as natural language. This solution aims to provide systems that can understand complex attack protocols, detect novel attacks, and adapt to evolving threats. Our system enhances security with proactive and comprehensive defense against a wider range of threats, improves efficiency through accurate threat detection and reduced false positives, and offers a future-proofed defense with continuous adaptation to evolving threats, ensuring long-term effectiveness and protection against emerging attack vectors