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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Tirion AI Governance Operating Model

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.

Architecture model

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.

01Policy Layer

Which usage is allowed, restricted, review-required or blocked?

02Approval Layer

Which use cases need business, IT, security, legal or executive approval?

03Operating Layer

Which reviews, metrics, incidents and portfolio decisions keep AI controllable?

Scorecard logic

Each dimension is scored from 0 to 3. Governance is reliable only when rules, approvals and operations are connected in a repeatable rhythm.

30 to 36 points

Operating model ready for multiple AI initiatives.

23 to 29 points

Pilot governance is workable, scale needs refinement.

16 to 22 points

Policy exists, but operations and approval are weak.

0 to 15 points

Clarify governance before expanding AI.

Readiness dimensions0-3

Policy Clarity

Are allowed, restricted and blocked AI uses defined?

0-3

Risk Tiering

Are there risk classes for data, users, automation and impact?

0-3

Use Case Intake

Is there an intake path for new AI ideas and tools?

0-3

Approval Path

Is it clear who approves each class?

0-3

Ownership

Does every active use case have business and technical owners?

0-3

Data Controls

Are data classes, access and external tools governed?

0-3

Human Review

Are review gates set for sensitive outputs and actions?

0-3

Evaluation

Are test sets, quality criteria and negative tests available?

0-3

Monitoring

Are usage, errors, risk and cost observable?

0-3

Incident Process

Is there a process for wrong outputs, data issues or tool misuse?

0-3

Portfolio Review

Are start, stop, scale and wait decisions reviewed regularly?

0-3

Communication

Do teams know how to use AI safely and permissibly?

0-3

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

Operationalize AI governance policy

Translate policy into approvals, roles and repeatable decisions.

Make shadow AI governable

Create allowed paths for useful adoption instead of only issuing bans.

Run the AI portfolio

Make start, stop, scale and wait decisions comparable across use cases.

Executive FAQ

Author: TirionReviewed by: Tirion AI Governance Operating Model

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?

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