Knowledge Grid Debuts Cognitive Data Layer for AI-Driven Security

Modern security teams often struggle with AI models that generate plausible but incorrect inferences when analyzing standard telemetry. While legacy SIEMs and log platforms excel at indexing data for human analysts, they fail to transform telemetry into a format suitable for the specific reasoning requirements of AI agents. This mismatch causes silent missed detections and recurring false positives, as models attempt to interpret data structures never intended for machine cognition.

"The AI was ready, but the data wasn't," said Peter Bybee, founder and CEO of Knowledge Grid. Bybee noted that his team repeatedly encountered these limitations while building managed detection and response services, concluding that the industry needed a fundamental shift in data architecture rather than mere prompt engineering. The platform utilizes a proprietary Temporal Data Grid to refine data at the point of ingestion.

Early adoption is already underway, with Dragonfly Cyber integrating the technology to monitor for rogue AI usage and unknown threats within enterprise environments. Knowledge Grid plans to demonstrate the platform’s capabilities to industry leaders during the Black Hat USA 2026 conference in Las Vegas this August.