It often starts with a well-intentioned shortcut: an employee installs a free AI tool or connects a plug-in to speed up their work. Before long, critical company data is moving through tools no one approved, and no one’s monitoring. 

This quiet adoption of generative AI, outside of IT’s oversight, is becoming one of the most pervasive security gaps in organizations. 

According to Infosecurity Magazine, 85% of companies globally have experienced cyber incidents, with 11% attributed to the unauthorized use of shadow IT.

But here’s the thing: we can’t afford to ignore AI. Just like companies that dismissed the internet in the early 2000s got left behind, organizations that resist AI today risk falling behind in productivity, innovation, and talent retention.

Banning AI outright isn’t the answer. Governance is.

This article explains what Shadow AI is, why it’s spreading fast, and what executives and IT leaders must do now to secure their organizations, without slowing innovation.

1. Why it’s time for leaders to govern AI, not ban it

The commonly used term “Shadow IT” refers to any technology (apps, software, or services) used by employees without formal approval from the IT department. While it’s not new (remember the rise of SaaS and personal productivity apps?), it has become harder to control in the age of cloud tools and remote work.

Shadow AI is the new evolution of Shadow IT, fueled by generative AI.

It describes AI tools adopted by employees without IT’s knowledge. Think:

• Unapproved AI meeting notetakers that record your calls

• ChatGPT plug-ins connected to work calendars or CRMs

• Freemium browser extensions that analyze internal data

Unlike traditional SaaS tools, Shadow AI interacts directly with sensitive data: voice, text, calendar events, customer records. And it’s often invisible to IT.

Employees are turning to AI tools to meet ambitious goals and work more efficiently, and that’s not a bad thing. These actions aren’t ill-intentioned; they’re often driven by a desire to do better, faster.

Organizations embracing AI are already seeing measurable gains in productivity and profitability across the board. It’s up to leaders to put the right guardrails in place so innovation can thrive without compromising security or compliance.

2. Real examples of how Shadow AI slips in

Even well-intentioned employees can introduce massive risk. Here are some examples of how this can happen:

• An executive assistant installs a free AI scheduler that requests calendar access. Now, every exec meeting is exposed to a third-party tool.

• A sales development rep (SDR) adds a free notetaker to record client calls. No one else on the team knows the tool exists, and the recordings are stored without encryption.

• A marketer uses a browser extension to analyze content. The plugin captures sensitive performance data and sends it to an unknown endpoint.

• A product manager uses a freemium AI roadmap tool to draft feature plans. The tool uploads confidential product strategy documents to external servers without internal approval.

• A software engineer installs an AI code assistant that auto-suggests snippets. It silently sends proprietary source code to an external API, creating an unlogged data exposure.

These aren’t edge cases; they’re more common than you think.

3. What are the risks of not governing Shadow AI?

Sensitive data leaks

Shadow AI tools often store transcripts and summaries on third-party servers. Without strict access control, anyone might access confidential data.

Compliance violations

AI tools may operate outside GDPR, HIPAA, or company privacy policies, especially when consent isn’t clearly obtained.

No audit trails

IT can’t track usage, data storage, or sharing practices for tools it doesn’t know exist.

Fragmented knowledge

When employees use different tools independently, institutional knowledge becomes fragmented and insecure – reducing the likelihood that these tools will actually improve productivity.

4. How can executives and IT leaders prevent Shadow AI without slowing innovation?

Below are some examples of the steps forward-thinking organizations are taking to prevent Shadow IT while they encourage the usage of new technologies. 

For a full checklist and a policy template, download the Shadow IT Prevention Guide for AI Meeting Notetakers built for IT leaders and execs navigating this new frontier.

1. Standardize which AI tools are approved company-wide

Rather than banning AI altogether or letting every team choose their own solution, smart companies are defining a list of vetted, secure tools that employees can use. This ensures data remains centralized, reduces risk exposure, and improves cross-functional collaboration.

2. Establish clear rules for what data can be recorded, stored, and shared

Not all meetings should be recorded, and not all content should live forever. The best organizations are defining rules around consent, data sensitivity, and storage: What can be captured? Who can access it? How long should it be retained? These guidelines protect both privacy and IP.

3. Centralize insights in secure, searchable systems

AI tools can surface incredible insights, but only if those insights are accessible, secure, and easy to find later. Forward-thinking teams are consolidating their knowledge in searchable systems that mirror access controls and ensure visibility for the right people (and no one else).

Closing thoughts

Security and innovation don’t have to be at odds. With the right guardrails and the right tools, you can embrace AI while protecting your company, your data, and your reputation.

You don’t need to solve this alone.

Our Shadow IT Prevention Guide includes everything your organization needs to get ahead of this trend:

• A 3-step Shadow IT audit checklist

• A customizable AI notetaker usage policy

• Company-wide communication templates (email, Slack, in-meeting)

• Security recommendations for selecting the right AI tools