AI Agents Translate SIEM Rules Across Platforms

Researchers Crack the SIEM Interoperability Problem With Agentic AI

Academics from Singapore and China have developed a novel agentic AI technique that automatically translates detection rules between incompatible Security Information and Event Management (SIEM) platforms — a breakthrough that could dramatically reduce the workload of overwhelmed security operations centers worldwide. The system leverages large language models orchestrated through an agentic framework to parse, understand, and re-express threat detection logic across vendor-specific formats, effectively creating a universal translator for cybersecurity rules.

For years, security teams have struggled with a frustrating reality: every major SIEM vendor — from Splunk to Microsoft Sentinel to Elastic — uses its own proprietary query language and rule format. When organizations migrate between platforms or run multi-vendor environments, analysts must manually rewrite hundreds or even thousands of detection rules. This new research promises to automate that painful process with remarkable accuracy.

Key Takeaways

• Agentic AI framework translates SIEM detection rules between different vendor formats automatically
• Researchers hail from institutions in Singapore and China, targeting a universal cybersecurity pain point
• The system handles diverse query languages including Splunk SPL, Microsoft Sentinel KQL, and Elastic EQL
• Unlike simple rule-mapping tools, the technique preserves detection logic and semantic meaning across translations
• The approach could save SOC teams hundreds of hours during platform migrations
• Built on top of large language models with specialized agentic orchestration layers

Why SIEM Rule Translation Is a Massive Headache

Every modern enterprise runs a Security Operations Center (SOC) that relies on SIEM platforms to detect threats. These platforms ingest logs from firewalls, endpoints, cloud services, and applications, then apply detection rules to flag suspicious activity. The problem? Each vendor created its own detection language.

Splunk uses its Search Processing Language (SPL). Microsoft Sentinel relies on Kusto Query Language (KQL). Elastic Security employs Event Query Language (EQL) and Lucene-based queries. IBM QRadar has its own Ariel Query Language (AQL). The list goes on.

When a company decides to switch SIEM vendors — a common occurrence given the competitive $6.4 billion SIEM market — security engineers face the daunting task of manually converting every detection rule. A typical enterprise SOC maintains anywhere from 500 to 5,000 custom detection rules. Rewriting each one by hand is not only tedious but error-prone, potentially leaving dangerous gaps in threat coverage during transitions.

Even organizations running multiple SIEM platforms simultaneously — an increasingly common strategy — struggle to keep detection logic synchronized. A rule written for Splunk cannot simply be copied into Sentinel. The syntax, field names, data models, and logical operators all differ.

How the Agentic AI Translation System Works

The research team's approach goes well beyond naive prompt-based translation. Instead of simply asking a large language model to 'convert this Splunk query to KQL,' the system employs a multi-step agentic pipeline that decomposes the translation task into discrete reasoning stages.

The framework operates through several key phases:

• Parsing and abstraction: The agent first analyzes the source rule to extract its semantic meaning — what threat behavior is being detected, which data fields are referenced, and what logical conditions must be met
• Intermediate representation: The detection logic is converted into a vendor-neutral intermediate format that captures intent without platform-specific syntax
• Target generation: A specialized agent then re-expresses the logic in the target SIEM's native query language, accounting for differences in field naming conventions and operator syntax
• Validation and refinement: A verification agent checks the translated rule for syntactic correctness and logical equivalence with the original

This agentic decomposition is critical. Previous attempts to use LLMs for direct rule translation often produced syntactically valid but semantically incorrect results — rules that would compile but miss the threats they were designed to detect. By breaking the task into reasoning steps, the agentic approach dramatically improves translation fidelity.

Why Traditional Approaches Fall Short

Before this research, the cybersecurity community had attempted several approaches to SIEM interoperability, none fully satisfactory.

Sigma rules represent the most widely adopted effort. Created by security researcher Florian Roth, Sigma is an open standard for writing vendor-agnostic detection rules that can be compiled into platform-specific formats. While Sigma has gained significant traction — with thousands of community-contributed rules — it has limitations. Not all detection logic maps cleanly to Sigma's abstraction layer, and the compilation step sometimes produces suboptimal or incorrect queries for certain platforms.

Manual conversion by experienced analysts remains the gold standard for accuracy, but it does not scale. A skilled security engineer might spend 30 to 60 minutes translating a single complex detection rule, accounting for field mappings, data model differences, and edge cases. At enterprise scale, this translates to weeks or months of dedicated effort.

Simple regex-based or template-based converters exist as well, but they handle only straightforward cases and break down when confronting complex nested logic, custom field names, or platform-specific functions with no direct equivalent.

The agentic AI approach addresses these shortcomings by combining the contextual understanding of LLMs with structured reasoning workflows. Unlike Sigma's static compilation, the AI system can reason about ambiguous mappings and make intelligent decisions about how to preserve detection intent.

Industry Context: AI Meets Cybersecurity Operations

This research arrives at a pivotal moment for the cybersecurity industry. SOC teams globally face a well-documented talent shortage — the (ISC)² Cybersecurity Workforce Study estimates the gap at 3.4 million unfilled positions worldwide. Anything that reduces manual toil for existing analysts has immediate practical value.

Major SIEM vendors are already embedding AI into their platforms. Microsoft has invested heavily in Copilot for Security, which assists analysts with incident investigation and response. Splunk, now owned by Cisco following its $28 billion acquisition, has integrated AI assistants into its platform. Google Chronicle leverages Gemini models for security analytics.

However, these vendor-specific AI features tend to deepen platform lock-in rather than solve interoperability. A Copilot for Security feature works exclusively within the Microsoft ecosystem. The Singapore-China research takes a fundamentally different approach by treating cross-platform compatibility as the primary objective.

The broader trend of agentic AI — systems where LLMs orchestrate multi-step workflows autonomously — has exploded in 2024 and 2025. Frameworks like LangChain, CrewAI, and AutoGen have made it easier to build these pipelines. This SIEM translation work represents one of the more practical applications of agentic architecture, solving a concrete operational problem rather than pursuing open-ended autonomy.

What This Means for Security Teams and Vendors

The practical implications of this research extend across multiple dimensions of cybersecurity operations:

• Platform migration becomes less risky: Organizations can switch SIEM vendors without fearing months-long gaps in detection coverage during rule conversion
• Multi-SIEM strategies become viable: Enterprises running hybrid security architectures can maintain synchronized detection logic across platforms
• Threat intelligence sharing improves: Detection rules published in one vendor's format can be quickly adapted for use in any SIEM
• Smaller teams gain leverage: Resource-constrained SOCs that cannot afford dedicated detection engineering staff can more easily adopt community rules written for other platforms
• Vendor lock-in weakens: When switching costs drop, SIEM vendors must compete more aggressively on platform quality rather than relying on migration friction

For MSSPs (Managed Security Service Providers) that operate multiple SIEM instances across their client base, the technology could be transformational. These providers often maintain parallel rule sets for different platforms — a massive duplication of effort that agentic translation could eliminate.

Looking Ahead: From Research to Production

Several challenges remain before this technique reaches production-ready maturity. The research team will need to demonstrate robust performance across a wider range of SIEM platforms, including legacy systems like ArcSight and newer cloud-native options like Sumo Logic and Devo.

Accuracy validation at scale presents another hurdle. In cybersecurity, a mistranslated rule is not just an inconvenience — it can mean missing an active intrusion. Any production deployment would require extensive testing against real-world attack scenarios to ensure translated rules maintain their detection efficacy.

The open-source community could accelerate adoption. If the researchers release their framework publicly, it could be integrated into existing security orchestration tools or offered as a standalone utility. Integration with the Sigma ecosystem would be particularly powerful, potentially creating a bridge between Sigma's vendor-neutral rules and platform-specific optimizations that pure compilation cannot achieve.

The SIEM market itself continues to evolve rapidly. Gartner has rebranded the category as part of a broader 'Security Operations Platform' vision, and consolidation continues. But as long as multiple vendors exist — and they will for the foreseeable future — the interoperability problem this research addresses will remain relevant.

For SOC teams drowning in manual rule conversion work, the promise of an AI agent that handles the translation automatically is not just a technical curiosity. It is a lifeline.