Enterprises migrating between SIEM platforms often have to manually rewrite detection rules because vendors such as Splunk, Microsoft Sentinel, IBM QRadar, and Google Chronicle use different query languages and data models.

Researchers now say AI may be able to automate much of that work, though security experts remain divided over whether the problem really requires AI at all.

Researchers from the National University of Singapore and collaborators say their system, called ARuleCon, can translate SIEM rules across platforms while preserving detection logic. In tests involving nearly 1,500 rule conversions, the framework improved translation accuracy by roughly 10% to 15% over baseline large language model approaches, according to a research paper.

“SIEM rules encode not only syntax, but also detection intent,” Ming Xu, lead author of the paper, told CSO. Different SIEM platforms implement distinct field schemas, query operators, aggregation behavior, and correlation logic, meaning rules rarely translate cleanly between vendors, he said.

Practitioners say the issue is becoming more common as enterprises adopt hybrid cloud environments and multi-vendor security stacks.

Why is SIEM rule translation difficult

“In large enterprises, the need to port or reuse detection rules across platforms is becoming increasingly common,” said Prashant Chaudhary, area vice president at Splunk India. Hybrid cloud adoption, mergers, compliance requirements, and multi-vendor environments are forcing SOC teams to work across disparate telemetry formats and detection frameworks, he said.

The researchers described manual rule conversion as “slow and imposes a heavy workload.”

“In most enterprise SOCs, rule portability isn’t a daily requirement. But for MSSPs and service providers managing multiple customer environments, translating and adapting SIEM rules across platforms is a routine challenge,” said Gaurav Bisht, SIEM specialist and principal solution consultant at cybersecurity distributor RAH Infotech.

According to Chaudhary, the bigger challenge is preserving detection fidelity and operational context when rules are moved between systems. “Organizations risk breaking detection logic, misaligning field mappings, and weakening behavioral correlations,” he said, adding that such failures can increase false positives and create blind spots.

Not everyone agrees that the problem requires AI

Some practitioners argue that much of the challenge can still be solved through deterministic engineering approaches rather than AI.

“With a good understanding of both schemas, it’s just a body of work,” said Rahul Yadav, founder of cybersecurity firm CyberEvolve.

Xu disagreed that rule translation can be reduced to simple compiler-style mappings. “A compiler-style system can handle predefined mappings, but it struggles when the conversion requires semantic interpretation, restructuring, or platform-specific adaptation,” he said.

The paper similarly notes that “SIEM rule conversion is significantly more challenging” than SQL translation because SIEM vendors “lack a unified specification.”

The researchers warned that seemingly valid translations can introduce “subtle semantic drift” that changes how detections behave in practice.

“The challenge isn’t just syntax — it’s the differences in field mappings, data models, and detection logic across platforms,” Bisht said. “Those variations make simple one-to-one rule translation unreliable in practice.”

The researchers said ARuleCon is not intended to replace deterministic approaches entirely, but to combine “their reliability with the flexibility of AI-driven reasoning.” Xu said the system uses AI to infer detection intent and iteratively refine translated rules while constraining outputs through syntax validation and semantic checks.

Human oversight remains critical

Security practitioners interviewed by CSO said enterprises are unlikely to trust fully autonomous rule translation systems without extensive validation and analyst oversight.

“Customers are unlikely to adopt fully autonomous rule translation in production SOC environments without strong validation, explainability, and human oversight mechanisms in place,” Chaudhary said. Organizations will expect testing against historical telemetry and real-world attack scenarios before deploying AI-assisted rule translation at scale, he added.

The paper itself acknowledges that large language models can produce incomplete or incorrect translations when dealing with vendor-specific nuances. Xu said ARuleCon is intended as an analyst-assistance system rather than a fully autonomous conversion engine. “A human user should manually verify” rules before deployment in production environments, he said.

“AI is non-deterministic by definition, so post-migration testing is essential,” Yadav said.

Bisht said the risks become more serious as SIEM detections increasingly feed automated response systems. “A bad translation doesn’t just create noise; it can trigger the wrong action,” he said.

Yadav warned that the bigger danger may be silent failures.

“Either you miss a real threat, or you get a spike in false positives and a lot of noise,” he said. “The first is dangerous because it’s silent.”