Most AI policies fail the same way. Someone writes a five-page document about responsible use, it gets circulated, everyone acknowledges it, and nothing about daily behaviour changes.
Meanwhile the marketing coordinator is pasting client information into a free tool, and the operations manager has built an automation that nobody knows exists.
Short answer: workable AI governance in a Canadian business is four things. An inventory of every AI tool and automation in use, a data rule people can remember, permission boundaries defined per system, and an approval and logging model that matches the risk of each use case. Everything else is elaboration.
Governance is an enablement tool
The framing matters. A policy written to prevent AI use will be routed around within a month, because your team has already found tools that make their work easier and they are not giving them up.
A policy written to make AI use safe and consistent gets followed, because it tells people what they are allowed to do rather than only what they are not.
Write for the second outcome.
Start with the inventory
You cannot govern what you have not counted.
Ask every department three questions.
- Which AI tools are you using, including free ones
- What information goes into them
- What automations exist, and who built them
The results are usually surprising. Not because anyone acted badly, but because capable people solve their own problems and do not think of it as a technology decision.
Record each entry with the tool, the owner, the data it touches, and whether output is reviewed. That table is your governance foundation. It is also, on its own, the most valuable hour of work in this whole exercise.
The data rule people will remember
Long classification schemes do not survive contact with a busy team. One rule does.
Before you put information into an AI tool, ask whether you would be comfortable with it appearing in a document you did not control.
Then attach a short list of hard limits.
- No client personal information without an approved tool and a documented purpose
- No employee records
- No unpublished financials
- No signed contracts or terms
- No credentials of any kind
- No third-party confidential information covered by an agreement
Six lines. People will actually retain that. A twelve-tier data classification matrix will live in a folder.
Permissions for AI agents
Chat tools and agents need different treatment. A chat tool receives what a person gives it. An agent has standing access to systems.
For every agent you deploy, write down the answers to these seven.
| Question | Why it matters |
|---|---|
| What can it read | Defines the blast radius of any data problem |
| What can it change | The line between assistant and liability |
| What can it send externally | Customer-facing output carries reputational risk |
| What requires human approval | Your primary control, especially early |
| Who owns it | Accountability when behaviour drifts |
| What is logged | Without a log there is no investigation |
| What happens when it fails | Fallback path, notification, and who gets called |
If those seven are not answered, the agent should not be running against live systems.
Approval thresholds by risk
Match oversight to consequence. Uniform rules either strangle low-risk work or under-protect high-risk work.
Low risk. Internal drafts, summaries, research, formatting. Human reads before use. No approval workflow needed.
Medium risk. Customer-facing communication, scheduling, data entry into business systems. Draft and approve. Log the action.
High risk. Anything involving money, contracts, pricing exceptions, personal information, or regulated commitments. Explicit human approval, full logging, periodic audit, and a documented rollback.
Autonomy should be earned. Start every agent at draft-and-approve regardless of category, run it for a defined period, review the log, and expand permissions based on observed behaviour rather than expected behaviour.
Canadian specifics worth knowing
Federal privacy law, PIPEDA, and the substantially similar provincial regimes in Alberta, British Columbia, and Quebec already apply to what you do with personal information in an AI system. There is no AI exception. Consent, purpose limitation, and safeguards obligations carry through.
Alberta’s PIPA governs private sector personal information handling in this province. Quebec’s Law 25 sets a higher bar including automated decision-making transparency obligations that are worth understanding if you serve customers there.
Where data resides and which jurisdiction governs it has become a live procurement question for Canadian organizations, particularly in the public sector, health, and financial services. Ask vendors where processing happens and get the answer in writing.
Sector rules stack on top. Health, financial services, legal, and public sector procurement each carry additional requirements that do not disappear because a tool is convenient.
This is general business guidance rather than legal advice. If your AI use touches personal health information, regulated financial activity, or automated decisions affecting individuals, get counsel who works in that area.
Incident response
Decide in advance what happens when the system produces something wrong, exposes information it should not have, or takes an action nobody intended.
Four elements are enough for most businesses.
- Detection. How you find out. Usually a person reporting it, which means people must know where to report.
- Containment. Who can switch the system off, and how fast.
- Assessment. What was affected, whose information, what went out.
- Notification. Legal obligations under privacy law, plus your own commitments to clients.
Write it on one page. Test it once.
Governance that stays alive
Review the inventory quarterly. Departments add tools. People change roles. Systems get renamed and an automation silently breaks.
Assign the review to a named person with time allocated. Governance without a maintenance schedule becomes a document about a company that no longer exists.
FAQ
Do we need an AI policy if we only use ChatGPT?
Yes, and it can be short. The data rule and the tool inventory cover most of the risk in that situation.
Who should own AI governance?
Someone with operational authority rather than purely technical or legal. Operations, finance, or the leader closest to the affected workflows.
How do we handle staff using unapproved tools?
Find out what problem they were solving and approve a safe path to solve it. Enforcement alone drives the behaviour underground.
Does governance slow implementation down?
Setting it up early is faster than retrofitting it after a system is live. Retrofitting means rebuilding permissions and approval flows while people are already depending on the tool.
What about AI-generated content and disclosure?
Decide your position on customer-facing disclosure and apply it consistently. Inconsistency causes more trouble than either choice.
Where to go next: Run the tool inventory this week. Three questions, every department, one table. It usually changes what you thought your first governance priority was.
