Turning raw logs into feature vectors without manual labeling

Dealing with 40PB of logs means you can't afford to have humans in the loop for every new data source.

We’ve built a pipeline that treats log ingestion as a dynamic feature engineering problem. By using schema inference and automated feature extraction, we feed raw telemetry directly into Energy-Based Models (EBMs).

The interesting part isn't just the ingestion—it's the feedback loop. When the system sees a new risk, it creates and optimizes a SOAR playbook via simulation. This post covers the "Step 1 to Step 6" of moving from raw text to an optimized, autonomous response.