AI Governance Needs a Control Plane, Not Another Dashboard

Most enterprise AI governance conversations focus on the wrong layer.

The hard part is not showing a dashboard with model usage. The hard part is building a control plane that still makes sense when someone joins, leaves, changes teams, or works in a different workspace. If the system cannot handle first boot safely, cannot revoke access cleanly, and cannot keep provenance inside your own infrastructure, then it is not really governing anything.

That is why the current LineageLens direction feels more like infrastructure than analytics. The backend now has a setup guard so the product stays locked until the first admin exists. It supports workspace-scoped invites, registration can be disabled, and token rotation means old sessions can be invalidated instead of lingering forever. On the capture side, even the free local extension preserves confidence and source, so evidence is not flattened into a raw diff.

I think that is the right shape for enterprise AI provenance. The important question is not “what model wrote the code?” It is “who had access, what workspace was it in, and can we prove that the evidence still means something after access changes?”

There is a live demo at https://lineage-website.vercel.app/ if you want to see the direction. I’d genuinely like to know: when your team talks about AI governance, are you treating model logs as enough, or do you need a full identity and revocation layer too?