Governance

A governance model AI teams can actually operate.

Most AI governance frameworks live in slide decks. DataVibe enforces them at runtime. Every outbound payload passes through three layers — policy, approval, audit — with graduated trust to keep operators out of the loop when it is safe to do so.

1 — Policy versions

Each workspace has named policies (e.g. "outbound-default", "support-replies"). A policy has versions. Only one version is published at a time. Operators can preview a new version against historical payloads before promoting it.

2 — Approval queue

Payloads that fail policy or land in a WARN tier route to a human approval queue scoped to a workspace role (Reviewer, Admin, Owner). Approvers see severity, a payload preview, the rule that fired, and the recommended action.

3 — Trust graduation

Each action type carries an autonomy trust state — manual, semi-autonomous, or autonomous. Operators graduate an action type out of the queue only after a configurable window of clean reviews. Any policy violation collapses trust back to manual.

4 — Immutable audit

Every gate decision lands in an append-only audit row that captures the payload, the policy version that ran, the operator (if any) and the dispatch result. Exports to CSV/JSON for compliance handoff.

5 — Roles

• Owner — Workspace billing, settings, members, and content.
• Admin — Members, policies, and content; no billing.
• Reviewer — Approval queue + audit; read-only policy access.
• Developer — API keys + integrations + docs.
• Billing — Invoices and plan changes; no operational surface.