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AI’s timeline is very much still being written, but one thing is clear – companies are now in the midst of shifting from experimentation to widespread implementation after having determined strong use cases, with security and trust now becoming higher priorities.
The question is no longer about whether employees are willing to embrace AI, because that much is clear. It’s now about whether their employers know how AI tools are actually being used, whether they’re providing the right type of solutions, and whether their governance supports real-world use cases.
Off the back of that, companies are now struggling to tame shadow AI as workers go off to explore their preferred tools, rather than being confined to workplace-provided alternatives. But while organizations have years of experience handling shadow IT, shadow AI is presenting new challenges.
Shadow AI is harder to tame than Shadow IT – gaining visibility is the first step
Rather than being blocked from downloading certain software, workers can almost painlessly head to their chosen AI tool directly from the browser or via a personal account without approval or restrictions. As much as two-thirds (67%) of enterprise AI use now takes place through unmanaged personal accounts, even when an organization already provides enterprise-grade licenses.
But those sanctioned AI tools are clearly working for employees, who are seeing higher productivity. At the end of the day, this is a major win for companies who are under pressure to prove ROI, but shadow AI presents security risks that enterprise-grade software generally negates.
Teramind has revealed that 86% of organizations lack visibility into how data moves to and from AI tools, and it’s not just knowledge workers who are to blame. Nearly seven in 10 C-suite execs also admitted to prioritizing speed over security.
I spoke with Teramind VP of Strategy Leeron Walter to understand why shadow AI has become more of an issue than we might’ve thought, and what organizations can realistically do to regain visibility and control while continuing to meet workers where they feel most comfortable and productive.
How do you define shadow AI, and why does it happen inside approved tools?
Shadow AI is any AI usage that operates outside organizational visibility and governance - whether through banned apps, personal accounts, or AI features embedded in tools you already pay for.
The reason it's hiding inside approved platforms is simple: vendors are racing to embed AI into everything. Your licensed Microsoft 365, your PDF reader, your CRM - they all have AI features now.
Our research shows 67% of enterprise AI usage runs through unmanaged personal accounts on corporate-licensed platforms. The perimeter didn't move. It dissolved.
Do executives actually follow the AI policies they sign off on?
Not always. Our data is unambiguous: 69% of C-suite leaders prioritize speed over security when using AI tools, versus just 37% of frontline employees.
Executives feel competitive pressure more acutely, so they rationalize bypassing policies.
What goes through an employee's head when they choose productivity over compliance - and can companies change that?
They're doing a fast cost-benefit calculation: "Missing this deadline hurts me now. A data breach is someone else's problem later." 60% of employees in our research said productivity benefits outweigh security risks when deadlines are involved.
You don't fix that with more restrictions - 48% said they'd use AI even if it were explicitly banned. You fix it by making the secure option just as fast and frictionless as the risky one. Remove the tradeoff entirely.
Is Gen Z really more likely to work around AI rules?
Yes, but not because they're reckless - because they're impatient with policies that feel arbitrary. For them, AI is a basic utility, like a search engine.
Blocking it doesn't register as a security measure; it registers as the company being behind. Meet them with speed and enablement, not bureaucracy.
Why do traditional DLP tools miss AI traffic?
Because they were built to catch files moving, not ideas being processed. Shadow IT was about unauthorized storage - a file uploaded to Dropbox.
Shadow AI is about unauthorized processing - sensitive data pasted into a chat prompt. There's no file transfer to intercept. The data moves through an encrypted browser session, and legacy DLP tools are pattern-matching against file types and network transfers, not semantic content in a chat box.
The threat model changed; the tools didn't.
What does the first 90 days of gaining AI visibility actually look like?
Days 1–30: Observe, don't block. Deploy behavioral telemetry to build a full Shadow AI inventory - browser extensions, clipboard activity, personal account usage inside approved platforms. Understand what's actually happening before you touch anything.
Days 31–60: Categorize risk. Which tools train on user data? Which departments depend on them? This is when you find out Engineering lives in an unvetted coding assistant.
Days 61–90: Enable and enforce. Roll out approved alternatives for high-risk tools. Implement real-time coaching - block the risky action, surface the safe alternative immediately. Goal: not zero AI usage, but 100% visible AI usage.
What does an enablement-first AI approach actually look like - and how do you stop it becoming shadow AI with extra paperwork?
You build paved roads. Give employees a fast, secure, approved AI path so they don't need to go off-road. That means enterprise AI tools with zero-retention data policies, integrated into existing workflows - not buried in a separate portal.
To avoid it becoming theater, your AI tool approval process needs to be agile. If the review takes six months, employees use the consumer version today and say nothing. Govern the data, not the application - allow the tool, but monitor and control what data flows through it in real time.
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