Rule/Query’s Decision Tree Summarization with AI – Source: socprime.com

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 shows:

• Initial Filtering:
  Time window, operating system, event type and action—e.g., filtering for event.action == “exec” on Linux hosts.
  
• Specific Process Detection:
  Matches process names and arguments related to base64 decoding across languages like Python, Perl, Ruby, and OpenSSL.
  

The AI output highlights logic branches and explains embedded conditions, including decoding flags ( -d , -base64 ) and command-line patterns.

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Why It’s Innovative

Unlike traditional rule validators, this feature doesn’t just check syntax—it interprets logic. With a custom Llama 3.3 model trained on detection engineering data, Uncoder AI delivers human-readable context:

• Identifies filtering stages and embedded logic
  
• Explains usage of complex operators like eval, regex, and logical branching
  
• Summarizes decision logic in structured paragraphs for easier review
  

This is particularly useful for SOC teams that need clarity without manually parsing dense query structures.

Operational Value

• Accelerates Rule Validation:
  Cuts the time to understand and debug rules—especially those authored by others.
  
• Boosts Detection Accuracy:
  Highlights redundant clauses or overly broad filters that may impact precision.
  
• Onboards Analysts Faster:
  Less experienced engineers can quickly understand detection logic and improve it with confidence.
  
• Improves Cross-Functional Collaboration:
  Summarized logic helps threat hunters, engineers, and managers stay aligned without decoding raw syntax.
  
• Supports Multi-SIEM Environments:
  With 48 languages supported, teams can apply this feature across a wide variety of query formats.
  

From Complex Code to Clear Intent
Uncoder AI transforms dense detection queries into understandable summaries. This bridges the gap between rule logic and analyst comprehension—bringing faster validation, more consistent tuning, and enhanced collaboration across the SOC.

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