clab60917/RAG-LLM-SOC_analyst
📦 Open Source Projectclab60917
Automate Level 1 SOC analyst tasks using RAG-powered LLMs for efficient cyber threat detection and incident response.
The RAG-LLM-SOC_analyst project provides a framework for integrating Large Language Models into security operations workflows. At its core, it utilizes RAG to ingest organizational security documentation, playbooks, and real-time log data, allowing the LLM to provide context-aware analysis of security alerts. This approach addresses the common SOC challenge of 'alert fatigue' by automating the initial triage process typically handled by Level 1 analysts. The system is built using Python and is designed to be modular, allowing security engineers to plug in various LLM backends and vector databases. Key features include automated incident classification, contextual enrichment of security events, and the ability to query internal security knowledge bases to suggest remediation steps. By standardizing the initial investigation phase, the tool ensures consistent handling of security events and allows human analysts to focus on high-priority threats that require deep forensic investigation.
💡Highlights
- ├─Automates L1 SOC triage
- ├─RAG-based log analysis
- └─Python-based modular design
🎯For
- ├─Cybersecurity Analysts
- └─Security Engineers