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Tirion AI Governance Operating Model
AI Governance Operating Model
An operating model for organizations that need to approve, run and stop AI use cases rather than only write policy.
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Framework overview
AI governance becomes effective only when policy, use-case approval, risk tiers, roles, data controls, human review, monitoring, incident process and portfolio decisions operate together.
The Governance Operating Model
The framework makes governance manageable. Not every AI topic needs the same process, but every topic needs the right risk class, role, approval and operating response.
Which usage is allowed, restricted, review-required or blocked?
Which use cases need business, IT, security, legal or executive approval?
Which reviews, metrics, incidents and portfolio decisions keep AI controllable?
Each dimension is scored from 0 to 3. Governance is reliable only when rules, approvals and operations are connected in a repeatable rhythm.
Operating model ready for multiple AI initiatives.
Pilot governance is workable, scale needs refinement.
Policy exists, but operations and approval are weak.
Clarify governance before expanding AI.
Readiness dimensions0-3
Policy Clarity
0-3Are allowed, restricted and blocked AI uses defined?
Risk Tiering
0-3Are there risk classes for data, users, automation and impact?
Use Case Intake
0-3Is there an intake path for new AI ideas and tools?
Approval Path
0-3Is it clear who approves each class?
Responsibility
0-3Does every active use case have business and technical responsibilities?
Data Controls
0-3Are data classes, access and external tools governed?
Human Review
0-3Are review gates set for sensitive outputs and actions?
Evaluation
0-3Are test sets, quality criteria and negative tests available?
Monitoring
0-3Are usage, errors, risk and cost observable?
Incident Process
0-3Is there a process for wrong outputs, data issues or tool misuse?
Portfolio Review
0-3Are start, stop, scale and wait decisions reviewed regularly?
Communication
0-3Do teams know how to use AI safely and permissibly?
Hard stop criteria
- Policy exists, but nobody owns approvals or operations.
- Use cases start without risk class and owner.
- Shadow AI is only banned instead of being routed into governed paths.
- Automated actions have no human review.
- There is no incident process for AI errors or data risk.
Short checklist
- Allowed, restricted and blocked usage defined.
- Risk tiers and approval paths documented.
- Use-case intake with owners established.
- Human review and data controls defined.
- Monitoring, incident and portfolio review planned.
- Communication and enablement prepared for teams.
Where to use this framework
Translate policy into approvals, roles and repeatable decisions.
Create allowed paths for useful adoption instead of only issuing bans.
Make start, stop, scale and wait decisions comparable across use cases.
Executive FAQ
What is the difference between policy and operating model?
Policy describes the rules. The operating model describes who decides, how approvals work and how AI remains controlled in operations.
Does every company need an AI board?
Not necessarily. A clear approval path matched to risk class and company size matters more.
When is governance too heavy?
When simple internal low-risk use cases go through the same process as external, sensitive or automated decisions.
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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.