Is there an AI law in Canada that marketing teams must follow?

There is no single AI law telling marketing teams what to do, but existing privacy legislation applies whenever AI tools handle personal information, and anti-spam rules apply to commercial emails however they were drafted. Which statutes apply depends on your sector and province, so confirm with your privacy officer or counsel.

Can employees use ChatGPT or other AI tools for work?

They can if the organization approves the tool and the account type, and staff know what information may go into it. The main risks come from personal accounts on free tools, where terms have not been reviewed and nobody can see what was shared.

What should an AI use policy for a marketing team include?

An approved tool list with vendor terms, data categories that say what may go where, approval rules for external content, monitoring and incident handling for automations, and clear responsibilities for leadership, the policy owner and staff. One or two pages is usually enough.

Do we have to disclose that content was created with AI?

Disclosure rules vary by context and platform, so check what applies to you. As a working standard, never present an AI assistant as a person, and make sure a human has reviewed and stands behind anything published under your name.

AI Governance Checklist for Canadian Marketing Teams

Why a checklist, and why now

In most marketing teams, AI arrived before the policy did. Someone uses a chatbot to draft social posts, someone else pastes a client brief into a free tool to summarize it, a third person connects an AI feature in the email platform. None of it is malicious. Most of it is fine. The problem is that nobody knows which parts are not fine, including the people doing it.

Governance fixes that by writing down a small number of decisions so individuals do not have to make them on the fly. It is not about slowing things down. A team with clear rules adopts AI faster, because people stop wondering whether they are allowed to.

What follows is a checklist in six parts. A short version to copy is at the end. A note before starting: this covers privacy at the level of principles, for planning purposes. It is not legal advice, and an organization handling sensitive personal information should involve its privacy officer or counsel.

Privacy: the principles that apply

Canada does not have a single AI law that tells marketing teams what to do. It has privacy law, and that already applies to any AI tool that touches personal information. Private-sector organizations are generally covered by federal privacy legislation (PIPEDA), with Quebec, Alberta and British Columbia having their own private-sector laws. Public bodies, including New Brunswick municipalities and provincial agencies, fall under their own access and privacy legislation, and personal health information has separate rules again. Which applies to you depends on what you are and whose data you hold.

The principles behind these laws are broadly consistent, and they translate into practical questions for AI use:

Purpose and consent. Did people provide this information for a purpose that covers what you now want to do with it? Feeding a customer list into an AI tool to generate personalized messages is a new use; check that your notices and consents cover it.
Limiting collection and use. Use the minimum personal information the task needs. Most drafting and summarizing tasks need none.
Safeguards. Information sent to a vendor is still your responsibility. The vendor's security and data handling are part of your safeguards.
Accountability. Someone in the organization is responsible for personal information, including what processors do with it on your behalf.
Openness and access. Your privacy notice should describe AI use that involves personal information in plain language, and people can ask what you hold about them.

Two related rules marketing teams should keep in view. Canada's anti-spam legislation (CASL) governs commercial electronic messages regardless of whether a person or an AI drafted them, so automated sequences still need valid consent and an unsubscribe. And for organizations with official-language obligations, AI-drafted content in one language does not relieve you of quality in the other; bilingual by default covers that side.

Vendor controls

Most AI risk in a marketing team is really vendor risk. Keep a short list of approved tools and, for each, record the answers to these questions:

Does the vendor use your inputs and outputs to train its models, and can that be switched off? Terms commonly differ between consumer and business plans, so check the plan you actually use.
How long does the vendor retain prompts, files and transcripts, and can you delete them?
Where is data processed and stored, and which subprocessors are involved?
Does the business plan offer admin controls: single sign-on, user management, audit logs?
Will the vendor notify you of a breach, and how quickly?
Can you export your data and workflows if you leave?

Then make one rule explicit: work happens in approved, organization-controlled accounts. Personal accounts on free tools are where most accidental disclosure happens, because nobody reviewed the terms and nobody can see what was shared. Some public bodies also have rules about where data may be stored; if you are one, confirm them before choosing a vendor.

Confidential data: what may go where

People need a simple test they can apply in the moment. Four categories are enough:

Public. Already published or intended for publication. Fine in any approved tool.
Internal. Plans, drafts, internal reports, non-sensitive business information. Approved business accounts only.
Confidential. Client materials under agreement, unreleased financials, pricing, strategy, anything under a non-disclosure agreement. Only in tools specifically approved for it, and only where client agreements allow.
Personal information. Anything that identifies a person: names with contact details, customer records, applicant data, health or financial details. Not in general-purpose AI tools unless a specific use has been assessed and approved.

Agencies and consultants deserve a particular mention. If you work with outside firms, ask how they use AI on your material and which tools it passes through. If you are the outside firm, your clients will ask you the same, and you should have the answer written down. Customer-facing assistants raise their own version of this question, since what goes into the knowledge base becomes quotable; building an AI knowledge base for customer-facing teams covers permissions in detail.

Approvals: who signs off on what

Decide in advance which AI-assisted work can go out without review. For most teams, the answer for anything external is "none, yet".

Published content (web, social, ads, newsletters): a named person reviews for accuracy, voice and claims before it goes live. AI is good at inventing a plausible statistic; someone must check every fact.
Customer communications (replies, review responses, sequences): approved by a person, at least until a specific automation has an evaluated track record.
Regulated content (health, legal, financial, employment, public sector notices): reviewed by someone qualified, every time.
Customer-facing assistants: approved scope, tested answers and a defined escalation path before launch. When chatbots are worth it covers that decision.

Decide your disclosure position too. Many organizations say when content or conversations are AI-assisted; at minimum, never present an AI assistant as a person. On content quality more broadly, AI-generated versus human content explains why the review step pays for itself.

Monitoring and incidents

Every automation that runs without a person watching each output needs:

Logs of inputs, outputs and actions, kept as long as you need them and no longer.
A sampling routine. Someone reviews a set number of outputs weekly or monthly, depending on risk.
A test set rerun when prompts, workflows or underlying models change.
A kill switch and a named person who can use it.
An incident process. When something goes wrong (a wrong answer to a customer, data sent to the wrong tool), it is recorded, the cause is fixed and, where personal information is involved, it is assessed against your breach obligations.

Review the whole register of AI uses on a schedule, at least twice a year. Tools change their terms and features faster than policies get reread.

Staff responsibilities

A policy works when each person knows their part:

Leadership approves the policy, names an owner and accepts the residual risk.
The policy owner keeps the approved tool list and register current, and is the person people ask.
Automation owners run their workflows, do the sampling and report incidents.
Everyone uses approved accounts, follows the data categories, checks facts in AI output they use, and reports mistakes quickly.

Make reporting mistakes safe. The fastest way to drive AI use underground is to punish the person who admits they pasted the wrong file. Short training at onboarding and when the policy changes is enough for most teams; the goal is judgment, not memorization.

The checklist

We know which privacy legislation applies to us and have mapped where AI touches personal information.
Our privacy notices and consents cover the ways we use personal information with AI.
We have a list of approved AI tools, with vendor terms on training, retention, location and deletion recorded.
Work happens in organization-controlled accounts, not personal ones.
Staff know the four data categories and which tools each may enter.
External content and customer communications are approved by a named person.
Regulated content is reviewed by someone qualified.
Every running automation has an owner, logs, a sampling routine and a kill switch.
We have an incident process, and people feel safe using it.
The policy and register are reviewed on a schedule.

Governance is easier to build alongside the first automations than to retrofit afterwards, which is why approval rules and ownership are part of how we scope any AI and automation work. A workflow audit is a natural place to start the register.