Toward Autonomous SOC Operations: End-to-End LLM Framework for Threat Detection, Query Generation, and Resolution in Security Operations
Security Operations Centers (SOCs) face mounting operational challenges. These challenges come from increasing threat volumes, heterogeneous SIEM platforms, and time-consuming manual triage workflows. We present an end-to-end threat management framework that integrates ensemble-based detection, syntax-constrained query generation, and retrieval-augmented resolution support to automate critical security workflows. Our detection module evaluates both traditional machine learning classifiers and large language models (LLMs), then combines the three best-performing LLMs to create an ensemble model, achieving 82.8% accuracy while maintaining 0.120 false positive rate on SIEM logs. We introduce...
| Authors | Md Hasan Saju, Akramul Azim |
|---|---|
| Primary category | cs.CR |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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