AI governance framework gives leaders a practical way to direct artificial intelligence, assign ownership, manage risk, and decide when a use should advance, change, pause, or stop.
This guide is written for Canadian organizations. It separates widely useful governance practice from rules that apply only to specific governments, sectors, provinces, contracts, or activities.
Table of contents
- 1. Define scope
- 2. Set practical principles
- 3. Build the inventory
- 4. Create risk tiers
- 5. Assign decision rights
- 6. Embed lifecycle controls
- 7. Establish evidence and reporting
- 8. Test and improve the framework
- Practical checklist
- Frequently asked questions
- References
“A governance framework should tell a team what to do next. Principles matter, but operational clarity about ownership, evidence and escalation is what changes behaviour.”
Mehrzad Verdizadegan,
CEO, Praevion Consulting Inc.
1. Define scope
State what counts as AI, which business units and workers are covered, and whether the framework includes built, bought, embedded, and public tools.
Clarify geographical, legal, contractual, and sector boundaries. Scope gaps are where shadow systems grow.
2. Set practical principles
Use a short set of principles such as lawful purpose, proportionality, privacy, fairness, security, transparency, human accountability, and measurable value.
Translate each principle into decisions and evidence. A principle that never changes action is decoration.
3. Build the inventory
Capture purpose, owner, users, affected people, data, model, vendor, risk tier, lifecycle stage, approvals, and review dates.
Keep intake simple enough that teams will use it.
4. Create risk tiers
Set triggers based on decision consequence, personal or sensitive data, affected groups, scale, autonomy, reversibility, and potential harm.
Tie each tier to required assessment, testing, approval, monitoring, and independent review.
5. Assign decision rights
Define board, executive, central governance, business, product, technology, data, privacy, security, legal, HR, procurement, and audit roles.
Accountability must follow authority. A named owner needs power over the workflow, vendor, and funding.
6. Embed lifecycle controls
Integrate governance into intake, procurement, design, data access, testing, deployment, monitoring, change, incidents, and retirement.
NIST explicitly treats governance as cross-cutting. ISO/IEC 42001 links policies and processes to continual improvement.
7. Establish evidence and reporting
Require records of intended use, data, evaluation, limits, oversight, approvals, exceptions, incidents, and residual risk.
Executives need a short portfolio view, while delivery teams need operational detail.
8. Test and improve the framework
Review cycle time, exceptions, incidents, audit findings, user feedback, and whether low-value systems stop.
Update the framework after material incidents, legal change, new technology, and operating experience.
Questions for the next governance review
Ask whether the purpose is still valid, the owner still has authority, the evidence reflects current operation, and the controls work in practice. Review model, data, vendor, workflow, user, and legal changes. Then record the decision: continue, improve, limit, pause, or retire. This short discipline prevents yesterday’s approval from becoming permanent permission.
AI governance framework checklist
- Define the purpose, affected people, business outcome, and accountable owner.
- Record the use in an inventory and classify risk using clear evidence.
- Apply privacy, security, data, testing, human-oversight, and vendor controls.
- Document approval, limits, exceptions, residual risk, and stop conditions.
- Monitor value, performance, adoption, incidents, complaints, and major changes.
- Reassess after changes and retire systems that no longer justify cost or risk.
Related Praevion guidance
- Read the related Praevion governance guide
- Explore the next related article
- Explore Praevion Consulting Inc. digital transformation services
Frequently asked questions
How long does framework design take?
A focused first version may take six to twelve weeks, depending on size, risk, and existing controls.
Should organizations copy NIST or ISO?
Use them as credible reference structures, then adapt requirements to your organization and applicable duties.
What should be implemented first?
Start with scope, inventory, risk tiers, named owners, approved-use rules, and review paths for priority uses.
Executive takeaway
How Do You Create an AI Governance Framework? The practical answer is to place the right decision with a named owner, require evidence that matches the possible impact, and keep governance active after launch. Strong governance protects people and the organization while giving delivery teams a clear route to responsible use.
To discuss your needs, contact Praevion Consulting Inc..
References
- NIST, Artificial Intelligence Risk Management Framework
- NIST, Generative AI Profile, 2024
- ISO/IEC 42001:2023, AI management systems
- Office of the Privacy Commissioner of Canada, Principles for responsible, trustworthy and privacy-protective generative AI
- Government of Canada, Guide on Departmental AI Responsibilities
