Full Report
Convert Sigma DNS Rules to Cortex XSIAM with Uncoder AI How It Works Uncoder AI reads a Sigma rule designed to detect DNS queries to malicious infrastructure used by Katz Stealer malware, and instantly translates it into native Palo Alto Cortex XSIAM syntax. Left Panel – Sigma Detection: Targets DNS queries to specific Katz Stealer […] The post Convert Sigma DNS Rules to Cortex XSIAM with Uncoder AI appeared first on SOC Prime.
Analysis Summary
# Tool/Technique: Sigma Rule Conversion to Cortex XSIAM via Uncoder AI
## Overview
This describes the capability, powered by Uncoder AI, to automatically translate detection rules written in the open-source Sigma format (specifically DNS rules) into the Cortex XSIAM Query Language (XQL) format. This process aims to accelerate multi-platform coverage and operationalize threat intelligence by eliminating manual query language translation challenges.
## Technical Details
- Type: Tool/Framework Feature (Automation Service)
- Platform: Sigma (Input format), Cortex XSIAM (Output platform)
- Capabilities: Automated field mapping, query intent preservation, inline documentation addition, metadata inclusion.
- First Seen: Information not explicitly provided, but associated with a June 13, 2025 article describing its features.
## MITRE ATT&CK Mapping
The core function described is the **translation/operationalization** of detection logic, which supports various underlying TTPs defined in Sigma rules. Since the article focuses on the conversion utility itself rather than a specific threat, mapping is applied to the general category of detection engineering (T8023) or the initial focus area (DNS monitoring).
- [T8023 - Detection Techniques]
- [T8023.001 - Use of Command and Scripting Interpreters] (If the Sigma rule targeted scripting)
- *Note: Direct TTP mapping is impossible without the source Sigma rule content. The tool facilitates detection engineering.*
## Functionality
### Core Capabilities
- Automating field translation from Sigma notation to XSIAM XQL.
- Maintaining the original query intent and Indicator of Compromise (IOC) coverage during translation.
- Operationalizing threat intelligence quickly, citing examples like emerging malware such as Katz Stealer.
### Advanced Features
- Automatically adding inline documentation to the generated XQL queries.
- Including licensing metadata automatically during the conversion process.
## Indicators of Compromise
- File Hashes: N/A (Tool/Service)
- File Names: N/A (Tool/Service)
- Registry Keys: N/A (Tool/Service)
- Network Indicators: N/A (Tool/Service)
- Behavioral Indicators: N/A (Tool/Service)
## Associated Threat Actors
While the tool itself is neutral, it is explicitly mentioned in the context of operationalizing detections for emerging malware, specifically listing **Katz Stealer**.
## Detection Methods
This summary focuses on a *detection engineering utility*, not a malware sample. Detection methods would apply to the *output* (the resultant XQL queries) or the *input* (Sigma rules):
- Signature-based detection: Not applicable to the translation tool itself.
- Behavioral detection: Not applicable to the translation tool itself.
- YARA rules if available: Not applicable to the translation tool itself.
## Mitigation Strategies
Mitigation strategies relate to improving detection engineering efficiency:
- Adopting Detection as Code principles using standardized formats like Sigma.
- Utilizing automated conversion tools (like Uncoder AI) to accelerate deployment across heterogeneous security platforms (e.g., moving from Sigma to XQL).
## Related Tools/Techniques
- **Sigma:** The standardized, platform-agnostic detection language used as input.
- **Cortex XQL (XSIAM Query Language):** The specific target language for the output queries.
- **Detection as Code (DaC):** The overarching methodology this tool supports.