Employing LLMs for Incident Response Planning and Review
arxiv(2024)
摘要
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.
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