AI Governance for SMEs: 7 Dimensions to Assess Now

AI technology today is widely available, accessible, and affordable. What's missing is the structure to deploy it safely in a business context.

It covers the 7 dimensions to assess if you want to use AI in your SME without exposing yourself to avoidable risks, whether legal, security, operational or strategic. It's written for executives and IT leads of Canadian SMEs subject to Quebec's Bill 25 and Canada's PIPEDA.

Each dimension contains: what it covers, why it matters, what to look for in your organization, and a concrete action to take this week.

To measure where your SME stands across the 7 dimensions, you can also use our free self-assessment (35 questions, about 30 minutes).

1. AI Usage Framing

The core question: Have you defined what's allowed, what's framed, and what's forbidden?

Without a clear framework, your employees use AI tools by their own criteria. Internal data can then end up in tools nobody approved, with no control to prevent it. The cause is rarely bad faith: shared rules are missing.

What to look for:

• A one-page AI usage policy, written and shared
• A list of approved AI tools (and forbidden ones)
• A decision tree to evaluate a new use case before deployment
• An identified person responsible for questions and approvals

Action this week: Write a one-page AI usage policy. It doesn't need to be exhaustive — it needs to be applied. Three clear rules beat twenty rules nobody reads.

2. Personal Information Protection (Bill 25)

The core question: Are you compliant with Quebec's Bill 25 in your AI usage?

Quebec's Act respecting the protection of personal information in the private sector, as amended by Bill 25, provides for penal fines of up to $25 million or 4% of worldwide turnover. For Canadian SMEs operating federally, the Personal Information Protection and Electronic Documents Act (PIPEDA) also applies. Key obligations cover consent, transparency on automated decisions, and the right of access to processed personal information.

What to look for:

• Explicit consent flows when personal information is passed to an AI tool
• Documentation of automated decisions (who decides what, on what basis)
• A designated Personal Information Protection Officer
• A trace of access to personal information by AI tools

Action this week: Inventory the AI tools currently used in your SME. For each, note what types of data they process. Identify those touching personal information and apply the Bill 25 test.

3. Data Sovereignty and Residency

The core question: Are you maintaining control of your data?

Many SaaS AI tools host or process your data in the United States, where the CLOUD Act allows US authorities to access it under certain conditions. Under Bill 25, any transfer of personal information outside Quebec triggers a privacy impact assessment requirement. For many SMEs, this is a risk that hadn't been identified.

What to look for:

• The exact location of data centers for each AI tool used
• Contractual agreements covering international transfers
• Exit clauses allowing you to recover your data in an open format
• A privacy impact assessment for tools involving transfers outside Quebec

Action this week: Map your data flows for each AI tool. Where is it stored? Where is it processed? Who can access it? For critical cases, consider solutions hosted in Canada, which reduces this exposure.

4. Security and Supply Chain

The core question: Have you applied your security practices to your AI tools?

AI tools rely on many open-source software components, often maintained by volunteers. The compromise of a single component can reach many downstream organizations, as the LiteLLM supply-chain attack of March 2026 showed. Security tools themselves can be turned against their users.

What to look for:

• A software bill of materials (SBOM) for critical AI tools
• Pinned dependencies on verified versions (no "latest")
• Isolated environments for development and data analysis
• Vulnerability monitoring on the supply chain
• A documented incident response plan

Action this week: For each critical AI tool, ask your IT team: what exact versions of components are deployed, and how do we know when a vulnerability affects one of these components? If the answer is "we assume the vendor handles it," that's a blind spot.

5. Use-Case Risk Management

The core question: Are you assessing the risk of each use case before deploying it?

Not all AI use cases are equivalent. An internal assistant summarizing documents is low risk. A system that automatically makes credit, pricing, or hiring decisions is high risk — with direct legal and reputational consequences in case of error or bias.

What to look for:

• A classification of each use case by risk level (low, medium, high)
• A human in the loop for high-risk decisions
• A decision trail (who validated what, on what criteria) for sensitive cases
• A periodic review of deployed use cases

Action this week: List all AI use cases in your SME (existing or planned). Classify each as low, medium, or high risk. For high risks, verify that a human validates each decision before it takes effect — not after.

6. Intellectual Property

The core question: Are you protecting your IP in your AI usage?

Two often-ignored questions: who owns the content generated by your AI tools? And is your data being used to train the vendor's model? If your proprietary data trains a model accessible to your competitors, you have a structural problem — not a future problem, a current problem.

What to look for:

• A review of each AI tool's terms of service, specifically the clauses on data usage
• Contractual clauses explicitly excluding training the model on your data
• Clear attribution of IP on generated content (yours, or the vendor's?)

Action this week: Review the terms of service of your main AI tool. Look for "training," "model improvement," or "your content." If you find nothing explicit, assume your data is feeding the vendor's model.

7. Training and Culture

The core question: Are your teams equipped to make the right choices day-to-day?

Prohibition doesn't work — employees circumvent restrictions, either out of ignorance or pragmatism. The right approach is ongoing training: clear rules, approved tools, and a culture that makes those choices natural.

What to look for:

• An AI training plan for all relevant employees (not just technical teams)
• An internal AI champion available for questions
• A quarterly review of tools used in practice (often different from theory)
• Regular communication on new risks and best practices

Action this week: Schedule a one-hour AI training session for your non-technical employees. Three topics: what's allowed, what's forbidden, and who to contact when in doubt.

Next steps

AI governance can start this week. It rests on framing, security and training decisions, made one use case at a time. Without them, a project risks stalling at the pilot stage or ending with an incident.

To measure where your SME stands across the 7 dimensions: our free self-assessment covers 35 questions, takes about 30 minutes and gives you a score per dimension with priority actions.

If you already have a first use case in mind: you can start a mandate with us. We look together at the available data, the expected result and the conditions for getting started.

To go deeper on security: our LiteLLM supply-chain attack analysis shows how the compromise of a single software component can affect many organizations.