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Using LLMs to Automate Threat Intelligence Analysis Workflows in Security Operation Centers

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arxiv 2407.13093 v1 pith:KMQKAFYM submitted 2024-07-18 cs.CR

classification cs.CR
keywords agentanalyzingcentersintelligenceinvolvedllmsoperationrepetitive
verification ladder T0 review T1 audit T2 compute T3 formal
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SIEM systems are prevalent and play a critical role in a variety of analyst workflows in Security Operation Centers. However, modern SIEMs face a big challenge: they still cannot relieve analysts from the repetitive tasks involved in analyzing CTI (Cyber Threat Intelligence) reports written in natural languages. This project aims to develop an AI agent to replace the labor intensive repetitive tasks involved in analyzing CTI reports. The agent exploits the revolutionary capabilities of LLMs (e.g., GPT-4), but it does not require any human intervention.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques

    cs.CR 2025-07 conditional novelty 4.0 of 10

    A survey that maps LLM applications, vulnerabilities, and defenses across eight cybersecurity domains, but with significant citation and rigor problems.

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