AI Governance Checklist for Canadian Businesses in 2026

AI governance is the set of decisions, controls and evidence that lets a company use AI responsibly. It answers basic operational questions: who owns this system, what data may it use, how do we know it works, when must a person intervene and what happens after a failure?

A policy that nobody can apply during a release review is not enough. The controls must appear in procurement, product design, access management, testing, monitoring and incident response.

List every model, assistant, agent and embedded AI feature used by employees or customers. Record the business owner, vendor, purpose, users, data categories, connected systems, deployment date and next review date. Include free tools and features that arrived through a software update.

Unknown use is unmanaged risk. A short inventory usually reveals duplicated subscriptions, unreviewed data flows and systems that no longer have an active owner.

A meeting-summary tool and a system that recommends credit decisions should not pass the same review. Classify each use case by the sensitivity of its data, the effect on people, the size of a possible loss, its reversibility and how much autonomy the system receives.

The Office of the Privacy Commissioner of Canada advises organizations to establish legal authority for collecting and using personal information, limit disclosure of personal or confidential information, explain AI use and build privacy into the design. PIPEDA continues to set rules for covered private-sector organizations, alongside provincial requirements that may also apply.

Document which data classes each tool may receive. Confirm whether prompts and outputs are retained, where data is processed, whether the vendor trains on customer data and how deletion works. Use de-identified or synthetic data when personal information is not required.

Vendor review should cover data use, security controls, subprocessors, retention, incident notice, intellectual-property terms, service changes and exit procedures. Ask what happens when the underlying model changes. A workflow that passed testing in March may behave differently after a model or retrieval update.

Build an evaluation set from normal cases, difficult edge cases and known failures. Define the expected result before running the test. Measure factual support, task completion, harmful output, privacy leakage and the quality of handoffs to people.

NIST organizes AI risk work into four functions: Govern, Map, Measure and Manage. That sequence is useful for a small company too. Set accountability, understand the use context, test the risks and operate the controls.

A “human in the loop” has little value if the reviewer lacks time, context or authority. Show the source evidence, highlight uncertainty and make approval or rejection clear. Give the reviewer a way to report the reason for a correction so the case can become a future test.

Track failures, overrides, complaints, security events and unexpected data exposure. Define who can pause the system and how customers or employees can report a problem. Review high-risk systems more often and after every material change.

Governance should make useful AI easier to ship because the team knows what evidence and approvals are required. If every project invents its own review, delivery slows and important checks still get missed.

Week one: inventory systems and name owners. Week two: classify risk and approve data rules. Week three: create vendor and release checklists. Week four: test the highest-value workflow, document the result and schedule the next review. Keep the first version short enough that teams will use it.

This article provides general operational information, not legal advice. Privacy, employment, consumer-protection and sector-specific obligations vary by organization and province. Obtain qualified advice for your situation.