Full Report
How It Works This feature enables detection engineers to seamlessly convert Sigma rules into Google SecOps Query Language (UDM). In the screenshot, the original Sigma rule is designed to detect DNS queries to known Katz Stealer domains — a malware family associated with data exfiltration and command-and-control activity. Left Panel – Sigma Rule: The Sigma […] The post Detect DNS Threats in Google SecOps: Katz Stealer Rule Conversion with Uncoder AI appeared first on SOC Prime.
Analysis Summary
# Tool/Technique: Katz Stealer DNS Detection Conversion
## Overview
This summary focuses on the process of converting existing detection rules, specifically those targeting the **Katz Stealer** malware via DNS-based indicators, into actionable queries compatible with Google Security Operations (SecOps) environments using the **Uncoder AI** tool. The core objective is maintaining detection logic integrity while enabling platform extensibility across cloud environments.
## Technical Details
- Type: Technique/Detection Engineering Process
- Platform: Google Security Operations (Google SecOps), leveraging UDM (Unified Data Model) queries.
- Capabilities: Conversion of detection rules (e.g., Sigma format) into platform-specific queries (e.g., UDM), automation, and ensuring detection logic consistency.
- First Seen: Not explicitly stated in the provided text, but linked to a June 12, 2025 article.
## MITRE ATT&CK Mapping
The focus here is on the execution/command and control phase related to DNS communications often used by stealer malware.
- **TA0011 - Command and Control**
- **T1071 - Application Layer Protocol**
- **T1071.004 - DNS Protocol** (Inferred, as the detection focuses on DNS threats/indicators)
## Functionality
### Core Capabilities
- Creating DNS-based detections for Katz Stealer within Google SecOps.
- Translating detection logic from source formats (like Sigma rules) to target platform queries (UDM in Google SecOps).
- Ensuring precision and consistency during rule translation.
### Advanced Features
- **Platform Extensibility:** The capability to build a detection once using generalized formats (like Sigma or "Detection as Code") and operationalize it universally, specifically targeting Google SecOps.
- Automated conversion using Uncoder AI to accelerate deployment.
## Indicators of Compromise
No specific IoCs (hashes, domains, etc.) for Katz Stealer are provided as the article focuses on the **detection engineering process** around known DNS IoCs for this malware.
## Associated Threat Actors
- **Katz Stealer** (The malware being detected).
## Detection Methods
- **Cross-Platform Rule Conversion:** Utilizing Uncoder AI to convert detection content (e.g., Sigma rules) into Google SecOps UDM queries.
- **High-Value Alerts:** The goal is to deploy low-noise, high-value alerts based on these converted detections.
## Mitigation Strategies
- Adopting a **Detection as Code (DaC)** methodology to standardize and accelerate threat detection deployment across environments.
- Utilizing tools like Uncoder AI for efficient translation of security content.
## Related Tools/Techniques
- **Katz Stealer:** The specific malware targeted by the detection logic.
- **Sigma:** A generic, open-source notation for describing detection signatures (implied source format).
- **Uncoder AI:** The AI-powered tool used for rule conversion.
- **Google SecOps / UDM:** The target platform and data model.