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The apps and AI your team uses that you can't see
Your people sign up for apps and lean on AI to get work done, usually without anyone tracking it. It's rarely rule-breaking, it's just moving fast. On a smaller team, where nobody's job is to watch for this, the quiet risk and the wasted spend add up faster than you'd think.
This is the exposure most small and mid-sized teams aren't watching. Unmanaged apps and unapproved AI are where company data slips out, where compliance gaps open up, and where you keep paying for software nobody opens. We help you see it clearly, claw back the waste, and close the gaps, without turning into the department of no.
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See what's really running
Find the wasted spend
Close the risky gaps
Calgary-based
What we mean by shadow SaaS and shadow AI
Both come from the same place: a team trying to solve a real problem faster than IT can help. The names just describe what slipped past the radar.
Shadow SaaS
Shadow SaaS
Cloud apps your team starts using without IT's say-so. A free trial on a company card, a personal login for a file-sharing or note-taking tool, a project tracker a department picked on its own. Easy to start in under a minute, and invisible to whoever's meant to be looking after your systems.
Shadow AI
Shadow AI
AI used without approval. Pasting work into a personal ChatGPT or Gemini account, switching on the AI feature inside another app, or adding a browser extension that reads what's on the page. It's the newest and fastest-growing corner of the problem, and the hardest to spot.
It's not a big-company problem
If anything, it's worse for a smaller team, because nobody's job is to watch for it. These figures come from across the market, but the pattern holds at 15 people as much as 1,500.
Shadow AI is a different kind of risk
With shadow SaaS, your data usually sits inside an app you didn't choose. That's a real risk, but a contained one. With shadow AI, the data is actively sent out to a third-party model that can keep it, learn from it, and surface it somewhere else. You can't pull it back, and these incidents tend to go unnoticed for longer. That's why we treat AI as its own track, not a footnote to the app problem.
Where the exposure shows up
How exposed are you?
Five quick questions, an honest read in return. No email needed to see where you stand. It's a rough self-check, not an audit, but it'll tell you whether this is worth a closer look.
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Where you stand
Answer the questions to see where you stand.
Where you stand
3 of 5 gaps
Real exposure
Normal, and quick to close before they cause trouble. Worth tidying up while it's small.
Assess, resolve, and keep it that way
You don't fix this by banning things. People just find another workaround. We do it in three steps: get a clear picture, sort out what matters, and put light-touch habits in place so it doesn't drift back. This is about your systems and your data, not watching your people. Most clients start with the assessment and decide from there.
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Sense-check this with your own AI
We sell this platform, so of course we'd frame it our way. Here's the fix: paste these into ChatGPT, Claude, or Gemini and see what an independent answer looks like. If it doesn't line up, tell us, and we'll talk it through.
How exposed are we?
I run a small or mid-sized business in Canada. Without sales language, explain the real risks of "shadow SaaS" and "shadow AI", meaning staff using apps and AI tools we never approved. What are the most likely ways company data leaks, what does it cost when it goes wrong, and what's the practical first step to get visibility? Cite benchmarks where you can.
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Why is shadow AI worse?
Explain how shadow AI is different from, and in some ways riskier than, traditional shadow SaaS. Cover where the data actually goes, whether it can be retained or used to train models, how hard it is to detect, and what that means for privacy obligations under PIPEDA in Canada. Be straight with me.
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What should an assessment find?
If I hire an IT provider to assess shadow SaaS and shadow AI across my company, what should a thorough assessment actually discover and hand me? What deliverables should I expect, and what questions should I ask to tell a real discovery from a surface-level scan?
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Start by seeing what's actually running
The first step is simple: a short conversation and a clear look at the apps and AI in use across your team. You'll come away with a real picture and no obligation.
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