Full Report
How It Works Writing detection rules often starts with a question: What am I trying to find, and under what conditions? But even the best threat intel reports don’t come prepackaged in platform-ready syntax. Uncoder AI’s Custom Prompt Generation bridges that gap. This feature allows users to input natural language descriptions of the behavior they […] The post Custom AI Prompting in Uncoder AI Enables On-Demand Detection Generation appeared first on SOC Prime.
Analysis Summary
# Tool/Technique: Uncoder AI Custom Prompting
## Overview
Uncoder AI is a tool offered by SOC Prime that utilizes custom AI prompting capabilities to enable Security Operations Centers (SOCs) to generate detection logic (such as Splunk queries) on-demand based on descriptive text inputs rather than manual coding or template modification. This shifts detection engineering from syntax mastery to describing desired outcomes.
## Technical Details
- Type: Tool / Framework Component (Detection Engineering Utility)
- Platform: Generates detection logic for SIEMs/logging platforms (specifically mentions Splunk). Security context is handled within SOC Prime's infrastructure.
- Capabilities: On-demand generation of detection queries, adaptation to context-rich prompts (infrastructure limitations, actor profiles), and low/no external API dependency for core functionality.
- First Seen: April 24, 2025 (Publication date of the article).
## MITRE ATT&CK Mapping
*Note: As this is a defensive tool designed to create detection rules, it primarily aids in the **Defense** and **Detection** phases, rather than executing offensive TTPs. However, the *output* of the tool is expected to map to offensive TTPs.*
- TA0011 - Command and Control
- T1071 - Application Layer Protocol
- T1071.001 - Web Protocols
- TA0003 - Persistence
- T1547 - Boot or Logon Autostart Execution
## Functionality
### Core Capabilities
- **On-Demand Detection Generation**: Analysts describe required detection logic (e.g., "Generate a Splunk rule for PowerShell execution encoded") and the AI builds the high-fidelity query.
- **Syntax Abstraction**: Allows analysts to focus on *what* to detect rather than mastering complex SIEM query languages (like Splunk SPL).
### Advanced Features
- **Context Adaptation**: Handles "context-rich prompts," allowing the generated logic to adapt to specific infrastructure constraints, known log limitations, or profiles of threat actors being tracked.
- **Privacy/Security**: Data and instructions remain within SOC Prime’s infrastructure, ensuring maximum IP protection and no external API calls for execution environments.
## Indicators of Compromise
- File Hashes: N/A (This is a software feature, not malware)
- File Names: N/A
- Registry Keys: N/A
- Network Indicators: N/A (The tool generates rules; it does not inherently communicate externally for core function based on description.)
- Behavioral Indicators: N/A
## Associated Threat Actors
- N/A (This tool is used by defenders/Blue Teams to create detections against various threat actors.)
## Detection Methods
- This section applies to detecting the *use* of the Uncoder AI tool itself, which is generally executed within approved infrastructure. Detecting effective defensive rules generated by the tool relies on existing SIEM monitoring capabilities for the platform it targets (e.g., Splunk queries).
## Mitigation Strategies
- **For use of the tool**: Standard organizational governance regarding the introduction and use of third-party AI/SaaS tools for security engineering.
- **For generated detections**: Continuous auditing of detection coverage gaps (via SOC Prime's SIEM Posture Audit services) and prioritizing high-fidelity, context-aware alerts generated by the tool.
## Related Tools/Techniques
- Sigma (Rule format for correlation)
- Detection as Code platforms
- SOC Prime Threat Detection Marketplace (TDM)