Toward Autonomous SOC Operations: End-to-End LLM Framework for Threat Detection, Query Generation, and Resolution in Security Operations
As the digital landscape evolves, Security Operations Centers (SOCs) are increasingly faced with operational challenges that threaten their efficiency and effectiveness. With the growing volume of cyber threats, diverse security information and event management (SIEM) platforms, and labor-intensive manual triage workflows, the need for innovative solutions has never been more urgent. A recent paper published on arXiv (2604.27321v1) introduces a comprehensive framework that aims to automate critical security workflows by leveraging advanced machine learning techniques.
Overview of the Proposed Framework
The proposed end-to-end threat management framework stands out by integrating three core components: ensemble-based detection, syntax-constrained query generation, and retrieval-augmented resolution support. Each of these components plays a vital role in addressing the challenges faced by SOCs today.
Ensemble-Based Threat Detection
The detection module within the framework employs both traditional machine learning classifiers and large language models (LLMs). By evaluating various models, the researchers identified the three best-performing LLMs, which were then combined into an ensemble model. This innovative approach resulted in an impressive accuracy rate of 82.8%, while maintaining a low false positive rate of 0.120 on SIEM logs.
Automated Evidence Collection with SQM
Central to the framework is the Syntax Query Metadata (SQM) architecture, designed to facilitate automated evidence collection. The SQM leverages platform-specific syntax constraints and metadata-based retrieval to generate executable queries tailored for systems like IBM QRadar and Google SecOps. The effectiveness of SQM is measured through its BLEU and ROUGE-L scores, achieving 0.384 and 0.731, respectively. These results are more than double the baseline performance of existing LLMs, demonstrating significant advancements in query generation.
Incident Resolution and Recommendation Generation
In addition to threat detection, the framework enhances incident resolution and recommendation generation. By integrating evidence derived from SQM, the accuracy of resolution code predictions improved from 78.3% to an outstanding 90.0%. Furthermore, the overall quality of recommendations received an impressive score of 8.70, reflecting the framework’s potential to streamline incident response processes.
Impact on SOC Operations
One of the most notable benefits of implementing this framework in production SOC environments is the drastic reduction in average incident triage time. The framework is capable of decreasing this time from several hours to under 10 minutes, thereby enabling SOC teams to respond more swiftly and effectively to security incidents.
Conclusion
This work highlights the potential of domain-constrained LLM architectures combined with retrieval augmentation to meet the demanding reliability and efficiency requirements of operational security environments at scale. As SOCs continue to grapple with escalating threats, the adoption of such innovative frameworks may very well redefine the future of security operations.
- Integration of ensemble-based detection for enhanced accuracy.
- Automation of query generation through SQM architecture.
- Significant improvement in incident resolution and recommendation quality.
- Reduction of triage time, enhancing overall SOC efficiency.
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