Autonomous Cyber Defense: LLM-Powered Incident Response with LangChain and SOAR Integration
Year: 2025
Type: article
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.
Author: Sandhya Guduru
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410059180