Autonomous Cyber Defense: LLM-Powered Incident Response with LangChain and SOAR Integration

Abstract

The increasing sophistication of cyber threats necessitates the adoption of advanced, autonomous defense mechanisms. Large Language Models (LLMs) have emerged as a powerful tool for automating cybersecurity workflows, enabling intelligent incident response. This paper explores integrating LLM-powered incident response using LangChain, a framework that enhances natural language processing capabilities, and Security Orchestration, Automation, and Response (SOAR) platforms like Tines for automated containment workflows. The proposed system leverages MITRE ATT&CK playbooks to train LLMs, ensuring contextual decision-making and threat mitigation. Furthermore, probabilistic graphical models (PGMs) validate LLM-driven decisions, enhancing reliability and reducing false positives. This approach minimizes response time and enhances cybersecurity resilience by automating threat detection, triage, and containment. The findings underscore the transformative potential of AI-driven cyber defense, offering a scalable and efficient solution for mitigating modern cyber threats.