Scaling Security Incident Response with Generative AI

Varadharaj Varadhan Krishnan

Abstract: The increasing complexity and advanced nature of cyber threats required a shift from the traditional methods of responding to incidents to sophisticated AI - driven approaches. This paper delves into incorporating Generative AI into security operations, highlighting its potential to improve security incident detection, response, and recovery significantly. Unlike AI models that depend on rules and past data, Generative AI offers text summarizing and text generation capabilities that can be used to develop capabilities to aid security analysts and simulate attack scenarios with great accuracy to train them. The paper focuses on applying Generative AI in real - time monitoring for threats, automating incident prioritization, and using Generative AI for investigation and resolution processes. The study also covers the creation of a Security Operations Workbench powered by Generative AI that serves as a hub for integrating data sources and utilizing large language models (LLMs) to enhance efficiency and effectiveness in security operations. Furthermore, it explores how Generative AI can be utilized in simulation exercises to create realistic scenarios for testing and enhancing incident response strategies. The paper also addresses the challenges of implementing Generative AI and future work areas. By addressing the possible use cases of security operations that can benefit from Generative AI and a high - level design to build a workbench, this paper aims to guide organizations looking to improve security incident response efficiency and effectiveness.

Keywords: Security Incident Response, Generative AI, Large Language Model, Security Operations, Cyber Defense