From Threat Report to Detection Logic: Uncoder AI Automates Rule Generation

How It Works

Turning threat reports into detection logic is often the most time-intensive part of the detection engineering lifecycle. Reports are written for humans, not machines — and transforming narrative threat intelligence into actionable rules can take hours of manual interpretation.

Uncoder AI solves this with AI-assisted rule generation from reports. By analyzing threat intelligence documents — such as CVE disclosures, research summaries, or actor-specific TTPs — Uncoder AI generates detection logic tailored to your environment.

In the case shown, a report on critical vulnerabilities in Kubernetes Ingress-NGINX (including CVE-2025-1097, CVE-2025-1098, and others) is transformed into a ready-to-deploy rule for Microsoft Sentinel. The output includes:

• Structured rule metadata (displayName, description, severity)
• Native KQL logic detecting vulnerable annotation usage patterns (e.g., auth-tls-match-cn, mirror-target)
• Full integration with Microsoft Sentinel schema and detection flow

This happens in seconds — directly from the source text of the threat report.

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Why It’s Innovative

This capability removes the manual bottleneck of detection content creation. Rather than reading a report, reverse-engineering behavior, and writing platform-specific code, detection engineers now receive instant AI-generated logic mapped to their platform of choice.

Uncoder AI uses:

• Llama 3.3, a large language model fine-tuned for threat detection workflows
• Hosted in SOC Prime’s private SOC 2 Type II cloud, ensuring all inputs and outputs remain confidential
• Support for 56+ production-ready platforms, including Sentinel, Splunk, Elastic, Cortex XDR, Falco, and many more

It enables the automation of rule prototyping at enterprise scale.

Operational Value

• Faster Response to Threat Reports: Move from awareness to action in minutes, not days.
• Scalable Use Case Development: Let small detection teams cover broader threat landscapes.
• Eliminate Manual Translation Overhead: Reduce the need for parsing and scripting CVE-based rules from scratch.
• Works Across 56 Languages: Whether for Microsoft Sentinel or OpenSearch, the engine adapts the logic to your stack.

From PDF to Platform Logic — Powered by AI

Uncoder AI brings LLMs to where they’re needed most: at the intersection of threat intelligence and detection engineering. When a new CVE hits or a vendor publishes a critical advisory, teams no longer have to wonder how to detect this. They just feed it into Uncoder AI — and get back production-ready detection logic, aligned to their platform, within seconds.

From raw report to real protection — that’s the power of AI-powered detection creation.

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