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LLM-powered agents automate security alert investigation with higher accuracy

Researchers have developed an agentic workflow that uses large language models (LLMs) to automate the initial stages of security alert investigations. This system integrates predefined queries and tool access, such as SQL over logs and text search, to correlate information across multiple sources. The LLM component plans investigations and extracts evidence, demonstrating higher accuracy than LLMs used without this structured workflow. AI

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IMPACT Automates initial security alert investigations, potentially reducing manual workload for security analysts.

RANK_REASON This is a research paper detailing a novel approach to security alert investigation using LLMs.

Read on arXiv cs.AI →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 · Gudmund Grov ·

    Towards Agentic Investigation of Security Alerts

    Security analysts are overwhelmed by the volume of alerts and the low context provided by many detection systems. Early-stage investigations typically require manual correlation across multiple log sources, a task that is usually time-consuming. In this paper, we present an exper…