AI is everywhere in business now. It is embedded in the tools teams use every day, and every week another popular SaaS platform rolls out yet another AI feature. The pace of adoption is staggering, and the noise around it is constant.

But there is a quieter problem growing alongside all of this: Shadow AI.

Shadow AI is the use of AI tools, models, or AI-powered features without formal oversight from IT or security teams. It goes well beyond employees signing up for a free chatbot account. Think of the AI summarization feature quietly turned on inside your CRM or the meeting notes app that transcribes a confidential strategy call and stores it on an unvetted server. These things happen without permission, without logging, and in most companies, without anyone watching them.

Most businesses are only just starting to notice.

Understanding the Threats of Shadow AI

Data leakage

The most immediate danger is data leakage, and it rarely looks like a dramatic breach.

Someone might quietly sign up for an AI tool, or AI capabilities might get introduced inside pre-approved workflows without anyone flagging them. Sometimes, these features ride inside already-approved applications, so they feel completely routine to the person using them. That is what makes them so hard to catch. Confidential customer data, intellectual property, and unreleased product details can all end up flowing to an endpoint that falls completely outside your visibility. And once it is there, it becomes a black box.

The Samsung Electronics incident is one of the clearest real-world examples. In 2023, engineers at Samsung’s semiconductor division pasted proprietary source code into ChatGPT to debug it, fed it internal equipment code to optimize, and used it to generate minutes from a confidential meeting. Three separate incidents in about 20 days, all well-intentioned. But once that data left the building, Samsung lost all ability to retrieve it, delete it, or control who else might see it. The company responded by banning generative AI tools entirely, which itself shows how few good options exist once the data is already gone.

That is the recurring pattern: a policy failure that produces the same result as a breach, except it comes from the inside.

Expansion of the attack surface

Beyond data leakage, shadow AI also widens the attack surface of your business.

Every unsanctioned AI tool an employee brings in can come with its own unsecured APIs, weak authentication, or backend infrastructure your security team has yet to assess. Personal devices running AI apps outside your mobile device management add another blind spot. And unmanaged integrations, like a plugin that connects an AI note-taker to your calendar, email, or CRM, often request far more access than the task actually requires. Once that access is granted, it rarely gets reviewed again.

Personal accounts make this worse. Research from Harmonic Security found that roughly two-thirds of enterprise AI activity runs through personal accounts on platforms the company already pays for. Employees naturally reach for a personal login because it is faster than waiting on procurement. So you can end up paying for a secure, governed AI tool while most of the actual usage happens through ungoverned personal accounts that bypass every control you build.

Why Shadow AI Is a Bigger Risk Than Shadow IT

Shadow IT is a known quantity. The concept has been around for a while, and security teams know how to tackle it, including network monitoring, device management, SaaS discovery tools, and approval workflows to find and control unauthorized apps and devices. Shadow AI, by contrast, lacks all that infrastructure.

What AI does have, though, is an absolutely staggering pace of adoption. Every part of the business is touched by it, and the speed at which AI capabilities are spreading makes it extraordinarily hard to keep up with, let alone govern.

Shadow AI is also fundamentally more complex than shadow IT. With shadow IT, you can maintain a list of tools that are allowed or off-limits and enforce it. Shadow AI is far less predictable. Approved tools can quietly introduce AI features that go completely unnoticed, and employees can paste confidential information into AI prompts in ways that fall outside existing policy frameworks. Overall, the toolbox for gaining visibility into AI usage is still immature compared to what exists for unauthorized SaaS apps, and AI features often operate invisibly within traditional logging systems.

The numbers back this up. According to Netwrix’s 2026 Data and Identity Security Report, only 20% of organizations fully monitor or govern employee use of shadow AI, and just 11% describe themselves as fully ready for AI risk, with enforced policies and continuous monitoring actually in place. Most companies are, by their own admission, flying blind.

Executives are a major driver of shadow AI

Perhaps the most uncomfortable finding is where the risk concentrates. A 2026 survey from Microsoft partner TrustedTech found that nearly two-thirds of senior decision-makers admit to using unapproved AI tools, compared to just 31% of lower-level employees. Three in four employees recognize the security and privacy risks, so awareness is there. But executives are choosing speed over caution anyway, and they are doing it with the most sensitive information in the company: financial data, strategic plans, unreleased product details, and customer records.

That matters beyond the data itself. When leadership bypasses the policies they have asked everyone else to follow, it signals that speed matters more than security. And naturally, it becomes far harder for a CISO to hold the rest of the organization to a standard that executives themselves ignore. Governance only holds when leadership models it first.

Facing Shadow AI: What Growing Businesses Can Do

Any conversation around shadow AI has to start with one fundamental truth: banning AI only pushes usage further underground. That approach failed for shadow IT, and it will fail here too. A more realistic response has three parts.

First, communicate the risk, regularly and loudly. It takes more than a single memo or onboarding slide. Employees and executives alike need repeated, specific reminders about what shadow AI is, why it matters, and what data should stay away from unapproved tools. It has to be a recurring topic, something people hear often enough that it becomes second nature.

Then, put an acceptable use policy in place, but treat it as the beginning. On its own, a policy rarely stops someone under deadline pressure from copying a customer contract into a chatbot. So, it needs to be paired with real monitoring, clear examples of what is allowed and what is off-limits, and consequences that get enforced.

And finally, provide centrally managed AI tools that are genuinely good enough to use. Every piece of research on shadow AI points to the same root cause: people go around the front door because it is locked or slower than the alternative. If the sanctioned tool is worse than what is freely available online, employees will keep choosing the unsanctioned option, risks and all. The organizations getting ahead of shadow AI are the ones that made the secure path the easy path.

Shadow AI is already running inside tools your team uses every day. The businesses that get ahead of it now will be in a very different position than the ones that wait for their own Samsung moment to find out the hard way.

Build an Secure AI Strategy with The Right Help

The Astute Technology Management team has been working in close partnership with businesses to help them understand how to make the most of their AI investment, while keeping themselves safe. If you have any questions, feel free to contact us at 614 389 4102 or at [email protected], our staff in both Columbus and Cincinnati is ready to help!