DriftGard - Runtime governance for production AI

I built DriftGard after seeing a gap in how we govern AI once it moves from experimentation into production. Most AI governance happens around the AI: policies, documentation, risk assessments, and pre-deployment checks. Those are important, but I kept coming back to a simpler question: What happens when the AI is actually running? Can we see what it is doing? Can we enforce policies at runtime? Can we stop or escalate risky actions? And when something goes wrong, can we clearly explain what happened and why? That became the idea behind DriftGard: a runtime governance layer for production AI. As I built it, the focus evolved from simply evaluating AI interactions to a broader runtime control layer: observing behavior, enforcing policies, routing high-risk decisions for human review, and capturing audit-ready evidence. The goal is simple: help teams move from AI governance on paper to governance that operates alongside their AI systems. DriftGard is still evolving, and I’d genuinely value feedback from builders, security teams, AI leaders, and anyone deploying AI into production. What are the hardest governance problems you’re encountering with production AI today?