About EnforceGrid

The barrier to faster AI adoption is unquantified risk.

Enterprise AI adoption conversations are collapsing strategy, technology, ROI, and business risk into a single discussion — without a framework to separate them. The result is decision paralysis. Organizations want to shift workloads from human capacity to AI capacity, but can't commit because the risk is unquantified, the enforcement boundaries are undefined, and the monitoring infrastructure doesn't exist. Rapidly evolving regulatory requirements and threat models make it worse: by the time an assessment is complete, the landscape has shifted.

What was missing was a working framework: quantify the risk, make high-quality and high-velocity business decisions about where AI can operate, enforce the boundaries those decisions require, monitor behavior within them, and incrementally unlock further capabilities as confidence accumulates. That's the transition we're enabling — shifting workloads that are human-capacity-bound with unclear risk to AI-capacity with governed risk.

Our vision is a future where AI risk is governed, predictable, underwritable, and eventually insurable — the same progression that made other categories of operational risk manageable enough to scale. OWASP Agentic AI controls, NIST AI RMF requirements, and EU AI Act obligations are converging on the same gap: runtime enforcement infrastructure that produces verifiable evidence. Steer makes that layer concrete before incidents force the issue.

Apache 2.0 Core enforcement engine · Open-source and self-hostable