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Tirion Azure OpenAI Governance Decision Map
Azure OpenAI Governance Decision Map
A decision model for Microsoft-first teams that want Azure OpenAI to be secure, traceable and operable.
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Framework overview
Azure OpenAI governance is not one architecture choice. It connects use case, data classes, region, model access, identities, network, logging, evaluation, content safety, cost control and operating ownership.
The Governance Map
The framework moves Azure OpenAI decisions out of tool debate and into a controllable architecture sequence: purpose, data, identities, model access, guardrails, evaluation and operations.
Which data classes, regions and system boundaries are allowed for the use case?
Which identities, network paths, logs, policies and content-safety rules limit risk?
Who operates, monitors, evaluates and decides on scale or stop?
Each dimension is scored from 0 to 3. An Azure OpenAI setup is ready only when architecture, security, evaluation and operations are decided together.
Governance-ready for controlled implementation.
Pilot-ready with open operations or evaluation topics.
Architecture decision is not yet reliable.
Stop build and clarify governance foundations.
Readiness dimensions0-3
Use Case Boundary
0-3Is the purpose narrow enough and approved by the business?
Data Classes
0-3Are confidential, personal and regulated data clarified?
Region And Residency
0-3Was the region deliberately chosen for data, logging and operations?
Identity
0-3Are user, service and application identities separated?
Network
0-3Are access paths and private/public boundaries decided?
Model Access
0-3Are models, deployments and approvals documented?
Content Safety
0-3Are filters, refusals and escalations defined?
Evaluation
0-3Are test sets, negative tests and acceptance criteria available?
Logging And Audit
0-3Are prompt, tool and cost indicators observable?
Cost Control
0-3Are limits, owners and consumption reviews set?
Hard stop criteria
- Unclear data classes or personal data without a purpose boundary.
- No owner for model access, cost or operations.
- No evaluation before production use.
- No logging and incident decision.
- Architecture is driven by tool selection rather than risk and use case.
Short checklist
- Use case, data classes and non-goals documented.
- Region, identities and network paths decided.
- Model access and deployment owner named.
- Evaluation, content safety and refusals defined.
- Logging, cost control and incident process available.
- Start, stop or scale decision documented.
Where to use this framework
Connect use case, data and guardrails before build or procurement.
Show which decisions remain open before scale.
Put Azure, M365, security and operating ownership into one decision.
Executive FAQ
Is Azure OpenAI automatically safer?
No. Azure provides building blocks. Governance comes from data classification, identities, logging, evaluation and operating ownership.
When is a pilot enough?
When use case, data classes, region, identities, evaluation and cost control are clear for a narrow scope.
What is the most important stop criterion?
Unclear data classes plus missing owner for access, logging or operations.
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Need this translated into a real decision?
Use the AI check to identify the strongest AI, cloud or approval lever before choosing a next step.