A Practical AI Governance Framework for Canadian Organizations
AI governance works best when it is built into everyday operations. The goal is not a binder of policies; it is a clear operating system for deciding what AI may access, what it may do, who is accountable, and how the organization proves that controls are working.
Create an inventory with accountable owners
List every AI use case, the business owner, the technical owner, the data involved, and the people affected. You cannot govern systems the organization does not know it is using.
Classify data and control access
Define what information may enter each system, where it may be processed, how long it is retained, and who can retrieve it. Least-privilege access and clear residency choices should be default design decisions.
Set decision boundaries and human checkpoints
Separate assistance from authority. Drafting, summarizing, and prioritizing may be automated, while consequential decisions should retain an authorized human reviewer with enough context to make a real judgment.
Keep evidence and monitor change
Maintain logs, evaluation results, approvals, incidents, and model or configuration changes. Governance must continue after launch because data, vendors, models, and operational risks evolve.
Key takeaways
- Inventory every AI system and assign accountable owners.
- Define data, access, retention, and residency controls.
- Keep humans responsible for consequential decisions.
- Preserve evidence and reassess controls continuously.
