LLM Agent Automates Security Alert Investigation

An innovative experimental process leverages large language models (LLMs) to streamline the initial examination of security alerts. This methodology, detailed in arXiv paper 2604.25846, integrates LLMs with specific queries and limited tool access, such as structured SQL for Suricata logs and grep-based text searches. Initially, the system executes queries to summarize the available data. Following this, the LLM component determines which queries to apply based on the summary, extracts relevant evidence from the findings, and ultimately provides a conclusive judgment. This strategy seeks to minimize the time analysts dedicate to manually correlating alerts from various log sources. Findings indicate that the LLM-driven workflow achieves significantly improved accuracy in investigating log sources and rendering final decisions.