First Recon AI Launches 'AI Security Runtime' to Block Sensitive Data Before It Reaches AI Models

First Recon AI has announced general availability of its AI Security Runtime, a platform aimed at governing how enterprises actually use AI rather than just cataloging risk. The pitch: inspect every AI interaction — human-to-model, agent-to-tool, and agent-to-agent — and apply policy inline before data reaches a model.

The DLP hook here is concrete, not aspirational. A Windows and macOS endpoint agent enforces device-level controls across both sanctioned and shadow AI tools, blocking sensitive data from leaving managed endpoints. That puts this squarely in GenAI/endpoint DLP territory rather than the broader, fuzzier ‘AI governance’ bucket. The company also ships a browser and desktop ‘secure AI workspace’ as a managed alternative to public chat apps — a control pattern that will look familiar to anyone who has deployed CASB prompt inspection or browser isolation for LLM traffic.

For practitioners, this is another data point in the ongoing convergence of endpoint DLP with LLM prompt and response inspection. The buyer questions are the usual ones: how does the inline policy engine handle latency and false positives on real prompts, what does the audit-evidence trail look like for compliance reviews, and how does coverage compare against incumbents like Microsoft Purview’s runtime DLP and the AI controls in Zscaler and Netskope, plus newer autonomous-DLP entrants.

Worth noting: this is a launch/GA announcement, not a funding event. No round or valuation was disclosed, so treat it as a signal of product direction rather than a market-sizing datapoint. Teams already scoping controls for AI usage should slot it into a bake-off rather than a procurement decision.