In the spring of 2026, 19.2% of Canadian businesses told Statistics Canada they'd used AI to produce goods or deliver services in the past year. Two years earlier it was 6.1%. So adoption tripled. Fewer than a third of those firms had trained their own staff on it (Statistics Canada, 2026-06-11).
That's the gap an AI governance framework closes. And you've probably felt it already: a board member asks who approved the chatbot, a customer's security questionnaire has a new AI section, or someone finds out HR's screening tool has been ranking résumés since March.
Here's the short answer. An AI governance framework is the set of roles, rules, records and technical controls that decides which AI your organisation may use, who answers for each system, and how you prove it's working safely. For a Canadian organisation, a practical one has ten parts: an obligations register, named owners, a short AI policy, an AI system inventory, risk and impact assessments, data rules, security controls, human oversight, third-party AI rules, and a monitoring and review cycle.
This guide gives you that template, mapped line by line to the NIST AI Risk Management Framework, ISO/IEC 42001 and the Canadian rules that actually apply: PIPEDA, Quebec's Law 25, OSFI Guideline E-23, and the federal and Ontario public-sector directives. We build AI governance for Canadian organisations from North York, so this is written from the build side, not the policy side.
What is an AI governance framework?
An AI governance framework is how an organisation decides, records and checks everything it does with AI. It covers people (who owns what), process (how a new AI use gets approved and reviewed) and technology (the logging, access limits and review gates that make the rules real).
Think of it as the difference between "we have an AI policy" and "we can show you the approval record, the owner and last month's monitoring report for every model we run." The first is a document. The second is governance.
It isn't the same as data governance, though it leans on it. An AI data governance framework covers where training and prompt data comes from and who may use it. An AI model governance framework covers the life of each model, from approval to retirement. A full AI governance framework includes both, plus the legal, security and human pieces around them.
What is the primary objective of an AI governance framework?
The primary objective is accountability you can prove. Every AI system has a named owner, a documented purpose, a known risk level and evidence that its controls run. The main purpose of AI governance follows from that: letting you use AI faster, because approvals stop being a debate every time.
That second point gets missed. Good governance isn't a brake. Teams without it either freeze (nobody will sign off) or sprawl (everyone signs off on their own). Both are slower than a clear process.
Is there an AI governance framework Canadian organisations must follow?
No single one is mandatory, and Canada has no AI statute in force. The Artificial Intelligence and Data Act (AIDA) died with Bill C-27 when Parliament was prorogued in January 2025. Minister of AI Evan Solomon has said it won't return as drafted.
But "no AI law" doesn't mean "no rules." AI that touches personal information, credit decisions or government services is already covered by laws you know. Here's what actually reaches a Canadian organisation on 2026-09-30:
If you are... | What applies to your AI | Status |
|---|---|---|
Any private-sector business | PIPEDA, including accountability (Principle 4.1) and meaningful consent | In force. Bill C-36, tabled 2026-06-15, would replace it and add transparency duties for automated decisions. Not passed when we checked |
Serving people in Quebec | Law 25: tell people when a decision about them was made exclusively by automated processing (s. 12.1), and run a privacy impact assessment before a new system that handles personal information (s. 3.3) | In force since 2023-09-22 |
A bank, insurer or other federally regulated financial institution | OSFI Guideline E-23, which pulls AI and machine learning models into model risk management, including third-party models | Final, effective 2027-05-01 (OSFI) |
A federal department, or selling automated decision tools to one | Treasury Board Directive on Automated Decision-Making and its Algorithmic Impact Assessment, four impact levels | Updated 2025-06-24 (TBS) |
An Ontario public-sector body | Bill 194 (Enhancing Digital Security and Trust Act), with AI duties set by regulation | Royal Assent 2024-11-25; AI rules depend on regulations, check status |
Any organisation in Ontario | IPC-OHRC Principles for responsible AI: valid and reliable, safe, privacy protective, human rights affirming, transparent, accountable | Non-binding guidance, 2026-01-21 (IPC Ontario) |
Offering AI to people in the EU | EU AI Act. Standalone high-risk deadline moved to 2027-12-02 | Only if you have EU users |
The practical rule: build your AI governance framework for the rows that apply to you, not for the strictest regime you've read about. A Mississauga distributor with no EU customers doesn't need an EU AI Act programme. A federally regulated trust company does need E-23 evidence by May 2027.
Worth knowing: Ottawa's June 2026 national strategy, AI for All, lists "building trust" as a guiding principle and targets a fivefold increase in business adoption. Expect customers and regulators to ask for governance before a statute makes you.
Not sure which rows apply to you? Our free AI governance readiness assessment takes two minutes and gives you a readiness tier and your next three moves.
What is the NIST framework for AI governance?
The NIST AI Risk Management Framework (AI RMF 1.0, January 2023) is a voluntary US framework built on four functions: Govern, Map, Measure and Manage. It's free, vendor neutral and one of the most cited structures for an AI governance programme. Every vendor guide Google cites in its AI Overview for this topic names it.
- Govern: policies, roles, training and culture. It runs across the other three.
- Map: understand each AI system's context, purpose and who it could affect.
- Measure: test and track risks such as bias, security, privacy and reliability.
- Manage: decide what to do about each risk, monitor it and respond to incidents.
For generative AI, NIST added a profile in July 2024, NIST AI 600-1. It names 12 risks specific to generative systems, including confabulation (the model inventing facts), data privacy and information security. If you're writing a generative AI governance framework for Copilot, ChatGPT Enterprise or an in-house RAG app, that profile is your checklist.
When people search for a "NIST AI governance framework," this is what they mean. It tells you how to think about the risk. It doesn't give you a certificate.
What does ISO 42001 add to an AI governance framework?
ISO/IEC 42001:2023 is the international standard for an AI management system, and unlike NIST it's certifiable. It follows the same clause layout as ISO 27001 (context, leadership, planning, support, operation, evaluation, improvement) and adds an Annex A of 38 controls across nine areas, from AI policy to third-party relationships.
In Canada, the Standards Council of Canada is the only body that accredits ISO 42001 certification bodies (SCC). So when a customer asks for "ISO 42001," they want a certificate from an SCC-accredited auditor, not a consultant's letter.
A simple way to split them: ISO 42001 tells you what system to run. NIST tells you how to assess the risks inside it. Most organisations use NIST to organise the work and ISO 42001 when a buyer or tender wants proof. If that's you, here's how we handle ISO 42001 readiness, scoped to the systems you actually run.
What are the key components of AI governance? The 10-part template
Here's the AI governance framework template. Copy the table into a spreadsheet, keep one row per component, and add an owner and a review date to each. The NIST and ISO columns are there so an auditor can follow your work. The Canada column tells you which rule gives each component teeth.
# | Component | What it contains | NIST AI RMF | ISO/IEC 42001 | Canadian hook |
|---|---|---|---|---|---|
1 | Obligations register | Which laws, contracts and standards apply, and which don't, in writing | GOVERN 1.1 | Clause 4.1-4.2 | Table above |
2 | Roles, accountability and training | Executive sponsor, AI owner per system, review committee, training plan | GOVERN 2.1, 2.2 | Clause 5.3, 7.2, 7.3; A.3 | PIPEDA Principle 4.1 (accountability) |
3 | AI policy | Two to four pages: allowed uses, banned uses, approval route | GOVERN 1.2 | Clause 5.2; A.2 | OPC principles for generative AI |
4 | AI system inventory | Every model and AI feature: owner, purpose, data, vendor, risk tier, status | GOVERN 1.6 | Clause 4.3; A.4 | OSFI E-23 model inventory |
5 | Risk and impact assessment | Risk tier per system, impact on people, sign-off | MAP 1, MAP 5.1 | Clause 6.1.2, 6.1.4; A.5 | Law 25 s. 3.3 privacy impact assessment; TBS Algorithmic Impact Assessment |
6 | Data governance for AI | Data sources, consent basis, retention, where data is stored and processed | MEASURE 2.10 | A.7 | PIPEDA consent; Law 25 rules on transfers outside Quebec |
7 | Security and lifecycle controls | Access limits, logging, prompt-injection testing, change control, retirement | MEASURE 2.7 | A.6 | Canadian Centre for Cyber Security guidance |
8 | Human oversight and transparency | Where a human reviews, how people are told AI is involved | MAP 3.5 | A.8, A.9 | Law 25 s. 12.1 automated decisions |
9 | Third-party AI | Vendor due diligence, contract terms, model change notices | GOVERN 6.1 | A.10 | OSFI E-23 third-party models; OSFI B-10 third-party risk |
10 | Monitoring, incidents and review | Drift and output checks, incident process, yearly review | MANAGE 4.1, 4.3 | Clause 9, 10 | PIPEDA breach reporting |
A few notes from building these:
Components 1 and 4 decide the cost of everything else. If you skip the obligations register, you'll write controls for rules that don't bind you. If you skip the inventory, you'll govern the three AI systems you know about and miss the eleven AI features switched on inside software you already pay for.
Component 7 is where an AI security governance framework lives. Access to a model is access to whatever the model can read. The same least-privilege thinking behind zero trust networking applies to an AI assistant with a connector to SharePoint. That's why we test the model itself as well as the network around it in our AI security work.
Component 2 needs people who understand the systems. Governance fails when only lawyers own it. StatCan found just 32.0% of AI-using businesses trained existing employees on AI. If your IT team runs the models, they need the operations side too.
A check on the mappings: NIST subcategory and ISO clause numbers above match the published texts as of 2026-09-30. Check them against your own copy of ISO/IEC 42001 before an audit, because Annex A detail sits behind the standard's licence.
What does an AI governance framework example look like?
Here's one inventory row, filled in. Picture a 300-person Toronto insurance brokerage (a composite, not a client) that rolled out an AI assistant which drafts replies to policyholder emails.
Field | Entry |
|---|---|
System | Email reply assistant (vendor LLM through the CRM) |
Owner | Director, Client Service |
Purpose | Drafts replies; a staff member edits and sends every one |
Data | Policyholder names, policy numbers, claim notes |
Obligations | PIPEDA; Law 25 for Quebec policyholders; RIBO broker rules |
Risk tier | Medium: personal information, but a human sends every reply |
Controls | Claim notes masked before the prompt; CRM logs every draft; vendor contract bars training on client data |
Human oversight | 100% human review before sending |
Review | Quarterly output sample of 50 emails; next review 2026-12-15 |
That's it. One row, nine fields, and the brokerage can answer a regulator, an auditor or its own board in five minutes. An enterprise AI governance framework is this row repeated, plus the nine other components holding it up.
What are the three pillars of AI governance, and the five principles?
There's no official set of three pillars. Different sources list different ones: accountability, transparency and risk management is a common trio, and Google's AI Overview currently lists four. The one we use, because it maps to actual work, is:
- Obligation mapping: know which rules bind you.
- Inventory and ownership: know which AI you run and who owns it.
- Technical controls: logging, access and review gates that run inside the systems.
The five principles most people mean are the OECD AI Principles, adopted in 2019 and updated in May 2024 (OECD.AI). Canada adheres to them:
- Inclusive growth, sustainable development and well-being
- Human rights and democratic values, including fairness and privacy
- Transparency and explainability
- "Robustness, security and safety" (the OECD's wording)
- Accountability
So when someone asks for an OECD AI governance framework, a responsible AI governance framework or an ethical AI governance framework, they're usually asking for these principles turned into controls. In Ontario, the IPC-OHRC Principles are the local version, with six principles and a stronger human rights angle. Put the principles in your policy (component 3). Put the controls in components 4 to 10.
How is an agentic AI governance framework different?
AI agents take actions, not just produce text, so governance has to cover what the agent is allowed to do. A chatbot that gives a wrong answer is an error. An agent that issues a wrong refund, changes a firewall rule or emails a client is an incident.
Singapore published what it calls the world's first model AI governance framework for agentic AI on 2026-01-22 (IMDA). Its four dimensions are a sound base for any AI agent governance framework: bound the risks upfront, keep humans meaningfully accountable, put technical controls in place, and help end users act responsibly.
In practice, add four fields to the inventory row for every agent:
- Identity: the agent has its own service account, never a human's credentials.
- Tool permissions: a written list of every system and action it may touch.
- Action log: every tool call recorded, with the prompt that caused it.
- Kill switch: a named person who can stop it, and a tested way to do so.
If your agents run on your own hardware for data residency reasons, those controls sit in your network, which is exactly where we build agentic AI systems.
How do you build an AI governance framework in 90 days?
Building an AI governance framework doesn't need a year. For a mid-sized organisation, 90 days is realistic if you start with the inventory and stay small.
Days 1-15: scope. Fill in the obligations register. Name an executive sponsor. Decide, in writing, which regimes don't apply.
Days 16-40: inventory. Find every AI system, including AI features inside Microsoft 365, your CRM, your HR platform and your help desk. Assign an owner and a risk tier to each.
Days 41-65: controls. Write the two-to-four-page policy. Run impact assessments on high-tier systems first. Wire logging and human review gates into the systems themselves.
Days 66-90: operate. Set review dates, an incident process and a training plan. Run your first quarterly review before day 90, so the cycle is real.
Picture how this plays out in Montreal (another composite). A 150-person logistics firm uses an AI tool to shortlist job applicants. In week three, the inventory shows it rejects some applicants with no human involved.
Under Law 25 s. 12.1, that means the firm has to tell those applicants a decision was made by automated processing. It also has to let them ask for the reasons and submit observations to a person who can review the decision.
The fix isn't a new policy. It's one configuration change: a recruiter reviews every rejection before it's sent. The system is no longer "exclusively automated," the risk tier drops from high to medium, and the firm keeps a clean record of why.
Want a second pair of eyes on your inventory? A senior architect will review your AI system list and tell you which rows are high risk. Talk to us about AI governance.
When does AI governance consulting make sense?
You can build all ten components yourself. The template above is enough to start. AI governance consulting makes sense when one of three things is true:
- A deadline you can't move. An OSFI E-23 date, a tender that asks for ISO 42001, or a customer audit.
- Your AI runs on infrastructure nobody has mapped. Governance evidence lives in logs, identity systems and network paths. If you can't pull the log for a decision a model made last Tuesday, the gap is technical, not legal.
- You're regulated in more than one place. A firm with Quebec clients, Ontario public-sector contracts and an OSFI-regulated parent has three rulebooks to reconcile.
Here's a third composite. Picture an Ottawa software company selling a case-triage tool to a federal department. The department has to complete an Algorithmic Impact Assessment under the Treasury Board Directive, and the answers depend on how the vendor's model works.
The vendor that can hand over its inventory row, test results and logging design wins the contract. The vendor that sends a two-page ethics statement waits.
Pick a partner who can do both halves: map the rules, and build the controls. Most AI consultancies have no infrastructure team, and most infrastructure firms have no AI practice. We have CCIE Data Center, CCIE Security and CCDE engineers on the same team as our AI practice, working with clients in Toronto, Montreal and the Ottawa public sector. For regulated sectors, see how this fits financial services and government.
What should you do this week?
Bottom line: an AI governance framework is an inventory with owners, a short policy and controls you can prove. Canada has no AI statute, but PIPEDA, Law 25, OSFI E-23 and the public-sector directives already reach most AI that touches people.
Three things to do this week:
- Build the inventory. List every AI system and AI feature, with an owner for each. It takes an afternoon.
- Fill in the obligations register. Use the table in this guide and cross out the rows that don't apply.
- Pick your top three risky systems and run components 5, 7 and 8 on them first.
Want a quick read on where you stand? Take the free AI governance readiness assessment. If you'd rather have the gaps closed than listed, book a call with a senior architect and we'll scope your AI governance framework to the rules that actually bind you.
Written by Saeid Ghobadi, CCIE Data Center and founder of SMEnode. Legal status checked 2026-09-30. This guide is general information, not legal advice. Check your obligations with counsel before relying on it.
