AI Governance 101: Let Your Team Use AI Without Leaking Data or Blowing the Budget

Everyone in your company is already using AI.

According to IBM’s 2025 study, 79% of Canadian office workers are actively using AI tools in their daily work — but only one in four uses an enterprise-grade solution provided by their employer. The rest are on personal ChatGPT or Claude accounts, browser extensions, and “I just pasted the client contract into this random AI website and it was so helpful!”

That gap — between what employees are using and what IT controls — is the heart of the shadow AI problem. And it’s costing real money: breaches linked to shadow AI add an average of CAD $308,000 to the total cost of a data breach (IBM, 2025).

That’s really what AI governance is about: deciding how your company uses AI safely, consistently, and in line with your risk tolerance — without killing the productivity boost. And the productivity boost is real: enterprise AI users save an average of 40–60 minutes per day (OpenAI, 2025).

This post is a quick primer you can share with your leadership or IT team. It won’t turn you into a lawyer or a CISO, but it will give you a mental model and a practical next step.

What is AI governance (in plain language)?

Forget the buzzwords. For most medium and large businesses, AI governance comes down to four questions:

Who can use AI, and for what?
What data are they allowed to send into AI tools?
Where does that data go and how is it stored/used?
How do we monitor and adjust over time?

Under the hood, that connects to bigger frameworks like the NIST AI Risk Management Framework and the new ISO/IEC 42001 AI management standard, which both focus on building trustworthy, well-governed AI systems.

But you don’t have to start with a 100-page policy. Start with the basics.

Pillar 1 – Stop accidental data leakage

The biggest immediate risk isn’t “rogue AGI.” It’s an employee pasting confidential information into a tool you don’t control.

A few practical rules of thumb:

No more personal AI accounts for work

If staff are using their personal ChatGPT/Claude/Gemini accounts for client work, you have no visibility and no contractual protection. Consumer tools often use chats for training by default unless you turn it off or opt out, and those defaults can change over time.

Standardize on business-grade offerings

For example:

OpenAI’s business/enterprise and API offerings don’t train on your business or API data by default and include stricter privacy and compliance controls.
Azure OpenAI processes prompts and completions within Microsoft’s environment and doesn’t share them with OpenAI or other customers, nor use them to train models.
Microsoft 365 Copilot doesn’t use your M365 content to train foundation models, and integrates with your existing governance policies.

Learn more: OpenAI vs Azure OpenAI vs Microsoft 365 Copilot comparison →

Turn off training where it’s an option

In tools like ChatGPT, there are data-controls and privacy settings that let you stop your content being used for training going forward.

Redact sensitive data on the way in

Long-term, you can introduce an “AI gateway” that automatically strips names, IDs, or health data before prompts go to external models—especially helpful in regulated or sensitive environments.

If you only did one thing this quarter, move your people from random personal accounts to governed, business-grade tools with the right settings.

Pillar 2 – Control access, usage, and costs

A lot of leaders worry about “too many tokens.” The reality depends on how you’re buying AI.

There are two main models:

1. Seat-based (license) tools – like Microsoft 365 Copilot

You pay per user per month.
Under the hood, yes, there are token limits and throttling, but that’s managed by the vendor.
Your job is to decide who gets a license and how you monitor usage.

Microsoft now provides admin reporting and a broader Copilot Control System with security, governance, and analytics to see adoption and prompt volumes across your tenant.

2. Usage-based APIs – like OpenAI or Azure OpenAI

You’re billed on actual usage (tokens/requests).
Here you absolutely want to set spend limits, budgets, and alerts, and route calls through a shared API key or gateway so you can see what’s happening.

Learn more: AI Cost Control for CFOs →

Practically, a good starting setup looks like:

Assign Copilot (or similar) only to roles that benefit most (knowledge workers, client-facing teams).
Turn on usage reports in your admin center and review monthly.
For APIs, put all usage behind one internal service or proxy with per-team quotas, logging, and alerting when spend exceeds a threshold.

You’re not trying to micromanage every prompt; you’re trying to avoid silent, uncontrolled sprawl.

Pillar 3 – Standardize where your models run

From a governance perspective, where the models live matters almost as much as which model you use.

For many organizations, a good pattern is:

Productivity AI stays inside your main suite

E.g., Microsoft 365 Copilot for working with Word, Excel, Outlook, Teams and SharePoint content, with assurances that Microsoft isn’t using that tenant data to train their foundation models.

Custom / line-of-business AI runs via governed platforms like Azure OpenAI

There, prompts, outputs, and fine-tuned models are kept inside your tenant; they’re not shared with OpenAI, not used to train foundation models, and can be constrained by your existing access controls and compliance setup.

Everything goes through a small number of “front doors”

Instead of 20 different tools, you might have:

1 internal chat interface,
1 document assistant,
1 code assistant.

All three talk to the same governed back-end (Azure, API gateway, etc.).

That consolidation makes it much easier to answer questions like, “What AI did we use in this project?” or “Did any of this data leave our region?”

Pillar 4 – Policies, training, and culture

Technology alone won’t save you if your culture is “do whatever seems handy.”

You don’t need a 40-page AI policy. Start with:

A one-page acceptable-use guide

Download our AI Acceptable Use Policy template →

That answers:

What tools are allowed?
What data is never allowed (e.g., SINs, health data, unreleased financials, Indigenous community data without consent, etc.)?
How should staff label AI-assisted work?
Who do you ask if you’re not sure?

A short training session (recorded once, reused forever)

Show real examples of safe vs risky prompts.
Explain the difference between “public consumer” tools and “approved business” tools.
Reinforce that asking for help is encouraged.

A feedback loop

Make it easy for teams to propose new AI use-cases.
Review them quickly and either approve, tweak, or reject.

Over time, this becomes normal: “We use AI all the time here — we just do it in a way that doesn’t burn our clients or our reputation.”

A note on Canadian regulation

As of early 2026, there is no federal AI-specific legislation in Canada. Bill C-27 (which included the Artificial Intelligence and Data Act, or AIDA) died when Parliament was prorogued in January 2025. New legislation is expected but unlikely before 2027.

In the meantime, PIPEDA remains the primary federal privacy law governing AI use, and Quebec’s Law 25 effectively sets the toughest national standard — requiring Privacy Impact Assessments for AI deployments, transparency on automated decision-making, and cross-border transfer assessments. Even organizations based outside Quebec should consider aligning with Law 25’s requirements if they have customers or employees there, as it effectively “future-proofs” you against eventual federal legislation.

For policy templates, the Government of Canada’s Implementation Guide for Managers of AI Systems and the NIST AI Risk Management Framework Playbook provide foundational language you can adapt.

Where to start (and how we can help)

If you’re reading this thinking “we are absolutely not doing any of this”… that’s normal. Most organizations adopted AI bottom-up: a few enthusiasts, a couple of pilots, then suddenly it’s everywhere.

A realistic first step might be:

Pick your official AI tools (e.g., Microsoft 365 Copilot + Azure OpenAI).
Lock down account types and training settings so you’re not feeding sensitive data into random consumer services.
Draft a simple one-page AI use guide and walk your teams through it.
Start logging and reporting on usage from one place (admin center or your own gateway).

From there, you can grow into more formal frameworks and certifications if you need them.

Explore our complete AI Governance solutions →

Need help implementing AI governance?

This is exactly the kind of thing we do at DigitalStaff. We can:

Audit where AI is already being used in your organization
Map the risks and compliance gaps
Set you up with a practical governance playbook your team will actually follow
Implement enterprise AI platforms with proper controls
Train your team on safe AI usage

From “turn off training and standardize tools” all the way up to “AI gateway + logging + policy pack”—we help Canadian businesses implement AI governance that enables innovation while protecting what matters.

Get your free AI governance assessment →

Have questions about AI governance for your organization? Contact us or explore our AI Governance hub for detailed guides on platform selection, policy templates, cost control, and compliance.