That’s why shadow AI detection matters. When AI tools are used without clear approval or visibility, business data can end up in places you don’t control. Permissions can be broader than anyone intended.
But there are ways to set up systems so your team can use AI safely, without creating a privacy, security, or compliance problem.
Why Shadow AI is Showing Up Everywhere
AI adoption isn’t happening only through big “AI projects” anymore. It’s happening through features.
For most teams, the intent is good: keep work moving. But AI can change how and where your data is shared, putting it beyond your immediate control. Agent-like tools can read from one system and act in another. And they can do this without asking what’s appropriate for your business.
Canadian guidance is already pointing to the need for clear safeguards. The Canadian Centre for Cyber Security highlights that generative AI introduces real security considerations and recommends practical steps to reduce risk.
On the privacy side, the Office of the Privacy Commissioner of Canada’s principles emphasize accountability and privacy-protective use, especially when personal information could be involved.
The Shadow AI Detection Review
Begin with curiosity, not enforcement. Most Shadow AI shows up because someone is trying to work faster. Or they really believe a tool is innocent. Education, not suspicion, might be the solution.
Hold a short, simple check-in with each team, using a quick form or a 15-minute conversation per department.
Ask:
- What AI tools/features do you use for work?
- What do you paste or upload into them?
- Work account or personal account?
- Does it connect to email, files, chat, calendar, or CRM?
- What problem does it solve for you?
Build an AI inventory
Now turn what you learned into one simple list. Don’t overcomplicate it.
Your inventory only needs a few fields:
- Tool/feature name
- Who uses it and what their role is
- What it’s used for
- Where it lives e.g., in a browser extension, built-in app feature, standalone website/app, etc.
- What it connects to, if anything
This is also a good time to look for the “quiet” AI you didn’t plan for: new AI features added by software updates, AI add-ons enabled by default, and browser extensions installed without review.
Follow the data
This is the core of the shadow AI detection review. The real question isn’t “Are we using AI?” It’s “What information is being shared, and where could it end up?”
Create a short list of data types that should trigger extra caution.
Then map the most common workflows:
- What gets pasted or uploaded?
- Is it being stored?
- Is it being shared outside your environment?
- Is there a way to keep the same benefit while limiting what data is used?
If you need a reference point for “what responsible use looks like,” the Government of Canada’s guidance is a dependable benchmark for setting safe boundaries.
Find broad permissions and agent behaviour
This is where Shadow AI becomes a higher risk: when a tool can read from or act within your systems.
Look for:
- AI tools that request broad access to email, files, chat, calendars, or CRM records.
- Integrations connected via OAuth that grant more access than the task requires.
- Tools that can send messages, post to channels, update records, create tickets, or move files.
- Shared accounts used to “make setup easier.”
Decide on a response
After mapping tools, data, and access, make your response clear by using a straightforward response model:
- Keep (low risk): Tools that handle no sensitive data, have no integrations, and are used only with work accounts.
- Control (medium risk): Tools that are useful but require monitoring or oversight.
- Stop (high risk): Tools that expose sensitive data, have unknown settings or retention, use personal accounts, request broad permissions, or perform agent-like actions.
This is also where a simple risk-management mindset helps. The NIST AI Risk Management Framework is a good reference if you want to align your decisions to a recognised approach without getting overly technical.
Put rules and systems in place
Shadow AI doesn’t go away because you send a memo. It goes away when the safe workflow is simpler and more convenient than the risky one.
Start with the practical:
- Maintain a list of approved AI tools.
- Prohibit using personal AI accounts for business data.
- Create a brief “never share” list with clear, relatable examples for your team.
- Restrict or remove risky extensions and overly broad integrations.
- Provide basic training on what’s allowed, what isn’t, and how to handle uncertain situations.
If you find that Shadow AI usage is already widespread, it’s usually a sign your team needs an approved, supported way to get the same benefits safely. If you want help mapping usage and tightening controls, start here.
Keep AI Useful and Predictable
By completing the audit steps outlined here, you’ve taken a major step toward uncovering Shadow AI in your workflow. You now know which AI tools are being used, what data they touch, and where hidden risks may lie. From here, the goal is clear: allow safe, low-risk tools, monitor and control the useful ones, and eliminate any AI usage that puts your business or data at risk.
If you want to make Shadow AI detection a repeatable, ongoing process, Data First Solutions can help. Explore our cybersecurity services or book an assessment to get practical, actionable steps for keeping AI usage safe and under control.
Article FAQ
How do I detect shadow AI?
Start with shadow AI detection basics: ask teams what AI tools and AI features they’re using, then compare that to what IT has approved. Build a simple inventory and focus on the biggest risk areas first.
What is an example of shadow AI?
A common example is an employee pasting customer emails or contract language into a public AI chatbot using a personal account to “speed up” a response. Another is an AI browser extension that can read what’s on screen in web apps, or an “AI assistant” feature switched on in a SaaS tool that connects to shared files.
Is ChatGPT shadow AI?
ChatGPT can be shadow AI if it’s used for business tasks without approval or oversight, especially if people are using personal accounts or sharing sensitive information. If it’s part of an approved workflow with clear rules about what can be shared and how accounts are managed, it’s not “shadow”. In this case, it’s simply a governed tool.
