[Paper Review] Employing LLMs for Incident Response Planning and Review

Sam Hays, Jules White|arXiv (Cornell University)|Mar 2, 2024

Data Quality and Management4 citations

TL;DR

This paper proposes using Large Language Models (LLMs) like ChatGPT to enhance Incident Response Planning (IRP) and Standard Operating Procedure (SOP) development, enabling automated drafting, gap detection, and post-incident review. LLMs improve IRP efficiency and accuracy by generating, refining, and analyzing response plans, significantly reducing manual effort while maintaining human oversight for critical validation.

ABSTRACT

Incident Response Planning (IRP) is essential for effective cybersecurity management, requiring detailed documentation (or playbooks) to guide security personnel during incidents. Yet, creating comprehensive IRPs is often hindered by challenges such as complex systems, high turnover rates, and legacy technologies lacking documentation. This paper argues that, despite these obstacles, the development, review, and refinement of IRPs can be significantly enhanced through the utilization of Large Language Models (LLMs) like ChatGPT. By leveraging LLMs for tasks such as drafting initial plans, suggesting best practices, and identifying documentation gaps, organizations can overcome resource constraints and improve their readiness for cybersecurity incidents. We discuss the potential of LLMs to streamline IRP processes, while also considering the limitations and the need for human oversight in ensuring the accuracy and relevance of generated content. Our findings contribute to the cybersecurity field by demonstrating a novel approach to enhancing IRP with AI technologies, offering practical insights for organizations seeking to bolster their incident response capabilities.

Motivation & Objective

• Address the challenge of creating and maintaining comprehensive, up-to-date Incident Response Plans (IRPs) and SOPs in organizations with complex systems, high turnover, and legacy technologies.
• Overcome the discontinuity between IRP development (management-led) and SOP creation (engineer-led) by integrating LLMs to align both processes.
• Improve organizational resilience by leveraging LLMs to identify documentation gaps, suggest best practices, and streamline post-incident reviews.
• Demonstrate how LLMs can reduce time and resource burdens in IRP lifecycle management while ensuring accuracy through human-in-the-loop validation.
• Contribute a novel, practical framework for AI-augmented cybersecurity planning using the SMART framework and real-world incident simulation.

Proposed method

• Utilize LLMs to generate initial drafts of IRPs and SOPs based on NIST 800-61 Rev. 2 and industry best practices.
• Apply LLMs to analyze existing incident logs and provide structured post-mortem commentary, identifying procedural flaws and improvement opportunities.
• Employ the SMART framework to define constraints and guide LLMs in generating contextually relevant, actionable response procedures.
• Integrate LLMs into the post-incident phase to suggest improvements such as automated alerts for SOP updates and regular training drills.
• Use real-world incident simulations (e.g., stolen device scenario) to evaluate LLM-generated feedback on plan effectiveness and documentation accuracy.
• Maintain human oversight to validate LLM outputs, ensuring technical accuracy and alignment with organizational policies and system changes.

Experimental results

Research questions

• RQ1 How can LLMs improve the efficiency and quality of Incident Response Plan (IRP) and SOP development in organizations with limited documentation and high staff turnover?
• RQ2 In what ways can LLMs bridge the gap between management-led IRP creation and engineer-led SOP development to enhance plan composability and consistency?
• RQ3 To what extent can LLMs identify documentation gaps and procedural flaws in existing IRPs and SOPs during post-incident analysis?
• RQ4 How effective are LLMs in generating actionable, context-aware recommendations for improving incident response procedures based on real incident logs?
• RQ5 What role does human oversight play in ensuring the accuracy and reliability of LLM-generated IRP and SOP content?

Key findings

• LLMs significantly reduce the time and effort required to draft and refine IRPs and SOPs by generating comprehensive, context-aware content based on established frameworks like NIST 800-61.
• LLMs successfully identified that the 'Remote Wipe' SOP in a simulated incident was outdated due to interface changes, highlighting a critical documentation gap.
• Post-incident LLM analysis provided actionable recommendations, including implementing automated alerts for platform updates and scheduling regular training drills.
• The integration of LLMs into post-mortem reviews enabled detailed, structured feedback that enhanced organizational learning and plan improvement.
• LLMs demonstrated strong capability in correlating incident logs with procedural requirements, suggesting improvements that align with real-world system changes.
• Despite their utility, LLMs require human validation to ensure technical accuracy and relevance, especially when system interfaces or tools evolve.