Shadow AI is not always malicious — it's often a team shipping a copilot to staging because the governance process takes six weeks. Deploy gates flip the default: no AI deploy proceeds until governance intake is complete, and GRC is notified automatically.
What a deploy gate does
When engineering triggers a deploy to dev, test, or production, the gate checks governance status. If intake is missing, incomplete, or not yet approved, the deploy is blocked with a clear reason. The request routes to the AI Governance Console — no manual email chains.
Why gates beat policy documents
Policies without enforcement are suggestions. Gates embed policy in the delivery pipeline — the same place engineering already operates. Developers see governance as a step in deploy, not a separate bureaucracy discovered after an incident.
What intake should capture
- Project, environment, and owning team
- AI models and tools the agent will use
- Intended use case and explicit non-goals
- Data types exposed (PII, regulated, proprietary)
Structured intake feeds automated assessment — risk tier, controls, and draft runtime policy — instead of free-text tickets GRC must reinterpret every time.
Beyond the first block
A gate is only the first step in Gate → Assess → Prove → Enforce. After conditional approval, engineering submits compliance evidence; GRC audits and authorizes; deploy carries a policy token. The gate ensures nothing skips the line — ever.