Right now, someone in your organization is using an AI tool you don’t know about. They’re pasting customer data into it.
This isn’t a hypothetical scenario. According to recent research from BlackFog, 86% of employees now use AI tools at least weekly for work—and 49% of them are using AI tools not sanctioned by their employer. That’s nearly half your workforce operating outside your visibility.
The term for this is shadow AI, and it represents one of the fastest-growing enterprise risks of 2026. Unlike traditional shadow IT, which took a decade to become a crisis, shadow AI is accelerating on a timeline measured in months. The tools are too accessible, too useful, and too easy to hide.
What Shadow AI Actually Looks Like
Shadow AI isn’t malicious. That’s what makes it so difficult to address. Your employees aren’t trying to harm the company—they’re trying to do their jobs better. A sales rep pastes customer objections into ChatGPT to draft responses. A marketing manager uploads competitive research to Claude for analysis. A developer uses an AI coding assistant their team hasn’t officially adopted. A customer success manager feeds support tickets into an AI tool to identify patterns.
Every one of these use cases is reasonable. Every one of them is also invisible to your security, compliance, and IT teams. And every one of them creates risk you can’t quantify because you don’t even know it exists.
The explosion of shadow AI is driven by simple economics. Consumer-grade AI tools are free or nearly free. They require no procurement process, no IT approval, no integration work. An employee can start using ChatGPT, Claude, Gemini, or dozens of other AI tools in minutes, from any browser, on any device. The friction to adopt is essentially zero.
The Risks Nobody’s Tracking
When AI usage happens outside your visibility, risks accumulate in ways that are difficult to detect until something goes wrong.
Data leakage is the most immediate concern. Employees pasting sensitive information into AI prompts are essentially sharing that data with third-party services. Customer records, financial projections, product roadmaps, legal documents, personnel information—all of it can flow into AI tools that may retain, train on, or inadvertently expose that data. According to a survey cited by IBM, over 38% of employees share sensitive information with AI tools without permission from their employer.
Compliance violations compound the problem. If customer data from EU residents enters an AI system that doesn’t meet GDPR requirements, your organization bears the liability—not the AI vendor. The same applies to HIPAA-protected health information, SOC 2 data handling requirements, and industry-specific regulations. Your compliance posture is only as strong as your visibility into how data moves through your organization, and shadow AI creates massive blind spots.
Output consistency becomes another hidden risk. When employees use unauthorized AI tools to generate customer-facing content, legal advice, financial analysis, or technical documentation, you have no quality control over what AI is producing on your behalf. Hallucinations—confident but incorrect AI outputs—can propagate through your organization without anyone recognizing their source.
Cost sprawl may be the slowest-burning problem, but it’s significant. When every team and individual subscribes to their own AI tools, you lose purchasing leverage and budget visibility. Organizations often discover they’re paying for dozens of overlapping AI subscriptions, with no consolidation and no way to measure comparative value.
Perhaps most critically, shadow AI leaves you without an audit trail. When regulators, auditors, or legal teams ask how AI is being used in your organization, you can’t answer. You can only hope nothing material happened in the systems you can’t see.
Why Blocking Doesn’t Work
The instinctive response to shadow AI risk is to block it. Add ChatGPT to the firewall. Prohibit AI tool usage in the acceptable use policy. Send a memo.
This approach fails for the same reasons it failed with shadow IT twenty years ago. Employees route around restrictions because they need these tools to compete. The sales rep who uses AI to respond to customers faster wins more deals. The developer who uses AI coding assistance ships features more quickly. The marketing manager who uses AI for research produces better analysis.
When you block AI without providing alternatives, you don’t eliminate usage—you just push it further underground. Employees use personal devices, personal accounts, and personal networks. The shadow gets darker, and your visibility gets worse.
There’s also a talent dimension. The most effective knowledge workers have already integrated AI into how they operate. Telling them they can’t use these tools at your organization is effectively telling them to work less effectively—or to work somewhere else.
The Governance Gap
The fundamental challenge with shadow AI is simple: you can’t govern what you can’t see. Most organizations have some form of AI policy. They may have approved certain tools, defined acceptable use cases, and established data handling requirements. But policies only work when they’re applied to known activity.
Shadow AI, by definition, exists outside that framework. It’s the AI usage that happens in the gaps between your policies and your enforcement capabilities. Closing that gap requires visibility before control—you need to discover what’s actually happening before you can decide what should be allowed.
This is where traditional security tools fall short. They’re designed to detect known threats and block prohibited applications. Shadow AI is neither. It’s legitimate tools being used for legitimate purposes in ways that happen to bypass your governance framework. Detecting it requires understanding the full landscape of AI usage across your organization, including tools you haven’t explicitly approved.
How Olakai Addresses Shadow AI
Olakai takes a visibility-first approach to shadow AI. Rather than starting with blocking, we start with discovery. What AI tools are actually being used in your organization? Who is using them? What data is flowing through them? What outcomes are they producing?
This visibility layer creates the foundation for informed governance. Once you understand the full picture of AI usage, you can make intelligent decisions about what to allow, what to restrict, and what to redirect to approved alternatives. You can identify high-risk usage patterns before they become incidents. You can consolidate redundant subscriptions and negotiate enterprise agreements with vendors you’re already using at scale.
Importantly, visibility enables a partnership approach with employees rather than an adversarial one. When you can see which AI tools are delivering real value, you can fast-track their official adoption. When you can identify risky usage patterns, you can work with teams to provide safer alternatives. You shift from “you can’t use that” to “let me help you use this more effectively.”
Getting Started
The first step isn’t blocking. It’s understanding. Before you can govern AI usage effectively, you need to know what’s actually happening. Many organizations are surprised by what they discover—both the scope of AI usage and the specific tools that have gained traction without official approval.
That discovery process should answer several key questions. What AI tools are employees using, both sanctioned and unsanctioned? What types of data are flowing through these tools? Which use cases are delivering measurable value? Where are the highest-risk concentrations of activity?
With those answers in hand, you can build governance that’s proportional to risk and responsive to value. High-risk, low-value AI usage gets restricted. High-value, controllable AI usage gets accelerated. The gray areas in between get managed through policy, training, and approved alternatives.
Shadow AI will continue growing in 2026. The question isn’t whether your employees will use AI tools you don’t control—it’s whether you’ll build the visibility to govern that usage intelligently. The organizations that figure this out will turn shadow AI from a hidden risk into a competitive advantage. The organizations that don’t will remain in the dark, hoping that nothing goes wrong in the systems they can’t see.
How much AI is running in your organization that you don’t know about? Schedule a demo to find out.