Multi-Agent Collaboration in Incident Response with Large Language Models
This addresses the need for rapid and coordinated decision-making in cybersecurity incident response, offering a novel approach that could improve adaptability and streamline processes, though it appears incremental as it builds on existing frameworks and simulations.
The paper tackled the problem of enhancing collaboration and efficiency in cybersecurity incident response by applying large language models (LLMs) as intelligent agents in multi-agent systems, using the Backdoors & Breaches framework to simulate real-world dynamics and provide insights for optimization.
Incident response (IR) is a critical aspect of cybersecurity, requiring rapid decision-making and coordinated efforts to address cyberattacks effectively. Leveraging large language models (LLMs) as intelligent agents offers a novel approach to enhancing collaboration and efficiency in IR scenarios. This paper explores the application of LLM-based multi-agent collaboration using the Backdoors & Breaches framework, a tabletop game designed for cybersecurity training. We simulate real-world IR dynamics through various team structures, including centralized, decentralized, and hybrid configurations. By analyzing agent interactions and performance across these setups, we provide insights into optimizing multi-agent collaboration for incident response. Our findings highlight the potential of LLMs to enhance decision-making, improve adaptability, and streamline IR processes, paving the way for more effective and coordinated responses to cyber threats.