AI-Powered Query Optimization in Uncoder AI

How It Works

Long and complex detection queries — especially those involving multiple joins, enrichments, and field lookups — often become performance bottlenecks. This is particularly true for queries in Microsoft Sentinel, where misaligned joins or poor field usage can significantly delay results.

To address this, SOC Prime’s Uncoder AI introduces AI-driven Query Optimization. The system analyzes detection rules and provides instant feedback — either confirming the query is efficient or suggesting targeted improvements. This reduces trial-and-error debugging and accelerates the deployment of high-performance rules.

In the example provided, a KQL query related to Microsoft Defender for Office 365 is analyzed by Uncoder AI. The platform:

• Parses the query structure and identifies potential inefficiencies,
  
• Suggests a restructured version of the query with optimized joins, more efficient field projections, and cleaner logic,
  
• Ensures faster execution while preserving functional intent.

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Why It’s Innovative

Unlike static linting or rule checkers, Uncoder AI uses a custom-trained LLM (Llama 3.3) deployed in SOC Prime’s private cloud infrastructure. This allows the system to reason over detection logic and propose optimizations at a structural level — with:

• Contextual awareness of security-specific data schemas,
  
• Support for 48 production languages, from Sentinel and Splunk to Cortex XDR, Elastic Stack, QRadar, Snowflake, and more,
  
• Secure-by-design architecture: no queries leave SOC Prime’s cloud during the analysis process.

This approach enables language-aware, SOC-specific optimization, not generic formatting advice.

Operational Value

• Query Speed Gains: Optimized rules run faster, improving detection timeframes and reducing load on SIEM environments.
  
• Engineering Efficiency: Analysts receive practical, structured recommendations — not vague syntax tips.
  
• Secure Optimization: AI runs in SOC Prime’s SOC 2-compliant cloud; no data leaves the infrastructure.
  

Platform-Agnostic Impact: Though shown here with Microsoft Sentinel, the feature is applicable across dozens of supported tools — including Splunk, Graylog, CrowdStrike Falcon LogScale, and beyond.

From Query Overhead to Instant Efficiency

Uncoder AI takes performance tuning out of the hands of syntax experts and puts it into the AI layer — where every join, filter, and projection can be evaluated for speed and impact. With near-instant recommendations from a cloud-secure LLM, detection engineers can stop worrying about optimization debt — and start delivering high-speed, high-fidelity detection content at scale.

Uncoder AI doesn’t just detect inefficiencies. It fixes them.

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