Singapore boffins get diverse SIEMs singing in harmony with agentic rule translation

2026-05-05 · Source: The Register: Enterprise Technology News and Analysis · Field: Technology & Digital — Cybersecurity & Data Privacy, Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

Researchers from the National University of Singapore and China's Fudan University have developed ARuleCon, a technique designed to translate security rules across diverse Security Information and Event Management (SIEM) systems. Many organizations operate multiple SIEMs, each with proprietary rule schemas, leading to significant complexity and manual workload for Security Operations Centers (SOCs). Existing translation tools, like Microsoft's for Splunk to Sentinel, and frameworks like Sigma, struggle with complex or interlinked rules and lack broad vendor support. ARuleCon addresses these limitations by employing an agentic Retrieval Augmented Generation (RAG) pipeline that consults official vendor documentation to resolve schema mismatches, combined with Python-based consistency checks in controlled test environments to prevent semantic drift. This approach enables more accurate conversion of rules between SIEMs such as Splunk, Microsoft Sentinel, IBM QRadar, Google Chronicle, and RSA NetWitness, outperforming generic LLMs.

Key takeaway

For security architects and SOC managers planning SIEM consolidation or migration, ARuleCon offers a viable path to automate rule translation. This technology can significantly reduce the manual effort and complexity associated with converting proprietary SIEM rules, enabling more efficient threat detection and streamlined operations. Consider evaluating ARuleCon for its potential to facilitate smoother transitions and enhance cross-platform security visibility.

Key insights

ARuleCon translates SIEM rules across diverse platforms using agentic RAG and consistency checks, improving SOC efficiency.

Principles

• SIEM rule schemas are vendor-specific.
• Manual rule conversion is slow and error-prone.
• Generic LLMs lack SIEM schema training data.

Method

ARuleCon uses an agentic RAG pipeline to retrieve vendor documentation for schema matching and Python-based consistency checks in test environments to mitigate semantic drift during rule conversion.

In practice

• Translate Splunk rules to Sentinel.
• Convert IBM QRadar rules for Google Chronicle.
• Export rules for SIEM consolidation.

Topics

• SIEM Rule Translation
• ARuleCon
• Security Operations Center
• Retrieval-Augmented Generation
• Cybersecurity

Best for: CTO, VP of Engineering/Data, Research Scientist, AI Security Engineer, AI Scientist, Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Register: Enterprise Technology News and Analysis.