Azure AI projects can move quickly from prototype to production pressure.
That is exactly why governance needs to exist before teams connect sensitive data, production workflows, or internal tools. The goal is not to slow delivery. The goal is to make delivery safer and repeatable.
First Questions To Answer
Before rollout, confirm:
- who owns the AI workload
- what data can be used
- what data must never be used
- who can access the environment
- how secrets are stored
- where logs go
- how costs are tracked
- what approval is required before production use
Identity And Access
Review:
- Entra ID groups
- privileged roles
- managed identities
- service principals
- app registrations
- least privilege access
- break-glass process
- access review cadence
AI workloads often create new integration paths. Those paths need owners.
Data And Network Boundaries
Validate:
- allowed data sources
- prohibited data classes
- private endpoint requirements
- network access rules
- storage account configuration
- logging and retention
- export restrictions
- vendor or third-party access
Operational Controls
A production AI landing zone should include:
- monitoring
- cost alerts
- incident routing
- change approval
- prompt or workflow review
- model and tool inventory
- rollback process
- support boundaries
Product Fit
For a structured Azure AI governance workflow, use the Azure AI Landing Zone Governance Kit:
https://store.cloudpeakify.com/products/azure-ai-landing-zone-governance-kit
If your AI workflow includes agents and external tools, pair it with:
https://store.cloudpeakify.com/products/mcp-security-ai-agent-ops-starter-kit
Final Checklist
Before rollout, confirm:
- owners are assigned
- data boundaries are documented
- access is reviewed
- logging exists
- cost alerts are active
- risky actions require approval
- incident flow is clear
- production use has a repeatable governance path