Full Report
How It Works Complex threat detection queries can often become difficult to interpret and maintain—especially when layered with nested logic, conditionals, and multiple filters. Uncoder AI introduces automated decision tree summarization to solve this. Using Elastic Stack Query (EQL) as an example, Uncoder AI ingests the rule and explains it in structured English. The summarization […] The post Rule/Query’s Decision Tree Summarization with AI appeared first on SOC Prime.
Analysis Summary
# Tool/Technique: Rule/Query’s Decision Tree Summarization with AI (Uncoder AI Feature)
## Overview
This feature, part of SOC Prime's Uncoder AI suite, uses a custom Llama 3.3 model trained on detection engineering data to interpret and summarize the logic of security rules and queries (e.g., SIEM rules). Its purpose is to provide human-readable context, accelerating rule validation and improving detection engineering efficiency.
## Technical Details
- Type: Tool/Feature Set
- Platform: Multi-SIEM Environments (supports 48 languages/query formats)
- Capabilities: Logic interpretation, identification of filtering stages, explanation of complex operators, and structured summarization of decision logic.
- First Seen: April 29, 2025 (based on article date)
## MITRE ATT&CK Mapping
The described feature focuses on improving the **Defense** lifecycle (Detection Engineering and Analysis) rather than directly mapping to adversarial **Techniques**. However, improving detection capabilities indirectly relates to improving visibility against most tactics.
- **TA0001 - Initial Access** (Indirectly, by improving related detection rules)
- **TA0005 - Defensive Evasion** (Indirectly, by improving detection rule resilience)
## Functionality
### Core Capabilities
- **Logic Interpretation:** Goes beyond syntax checking to interpret the underlying logic of security rules.
- **Structured Summarization:** Provides summaries of decision logic in structured paragraphs, making it easier for analysts to review.
- **Operator Explanation:** Explains the usage of complex operators such as `eval`, regular expressions (`regex`), and logical branching.
### Advanced Features
- **Filtering Stages Identification:** Identifies the different filtering stages embedded within a query.
- **AI-Driven Context:** Utilizes a custom Llama 3.3 model specifically trained on detection engineering data to generate relevant, human-readable context.
- **Cross-Platform Support:** Operates across 48 different query languages/formats used by various SIEM platforms.
## Indicators of Compromise
- File Hashes: N/A (This is a software feature, not malware or a specific attack artifact)
- File Names: N/A
- Registry Keys: N/A
- Network Indicators: N/A
- Behavioral Indicators: N/A
## Associated Threat Actors
- N/A (This is a defensive tool/feature)
## Detection Methods
- N/A (This is a detection engineering/analysis tool)
## Mitigation Strategies
- N/A (This is a defensive tool designed to aid security teams)
## Related Tools/Techniques
- Uncoder AI (The primary tool suite hosting this feature)
- Sigma (Related to detection content standardization, which this tool helps analyze)
- Detection as Code platforms