Your code assistant just wrote a database migration script at 2 a.m. It even ran it. The demo app still works, but your compliance officer is sweating bullets. That’s the modern AI dilemma. Agents and copilots supercharge developers, but they also sidestep every approval workflow your security team spent years building.
AI model governance AI control attestation exists to prove one simple thing: your AI systems follow the same rules as your humans. It’s the evidence trail behind every automated action and the audit that keeps regulators calm. But creating that proof manually is painful. You need visibility into who issued commands, whether data stayed within policy, and how each prompt translated into real-world effects. Without that context, “AI compliance” becomes guesswork with better font choices.
HoopAI fixes that problem at the source. It sits between AI workloads and your infrastructure, enforcing guardrails in real time. Every AI-issued command flows through its proxy. Policies determine what’s allowed, what gets masked, and what simply never reaches production. Sensitive parameters are sanitized before leaving memory. Commands that might alter state or read confidential data get paused or rewritten on the spot. And since every interaction is logged and replayable, you can prove exactly what happened and why.
Under the hood, permissions in HoopAI are scoped and ephemeral. Nothing lingers longer than it should. Each AI agent or copilot receives a temporary, least-privilege identity when it acts, complete with Zero Trust boundaries. If an LLM tries to pull a secret it shouldn’t, the attempt dies quietly in the proxy while your audit trail notes the blocked request. The effect is instant policy enforcement without slowing development.
Teams see results fast: