Shadow AI in the Workplace: What It Is, Why It Happens, and How to Manage the Risk

We have all seen these common use cases: an employee asks an AI assistant to summarize a customer document. Someone uploads an unreleased campaign brief to a personal AI account to brainstorm headlines. A rep connects an AI meeting assistant to customer calls to make taking notes easier.

In each case, someone is simply trying to automate a process and work faster. However, if the organization hasn't approved the AI tool, account, or use case, something else may be happening behind the scenes: Shadow AI. It’s a growing reality for businesses as employees find new ways to use AI faster than organizations can formally evaluate and approve them.

A 2026 PagerDuty survey conducted among 1,250 non-IT office professionals at large organizations found that 66% of respondents who had used AI for work had done so despite believing it wasn't permitted under company policy. 72% also said they believed they understood how to use AI in their jobs better than the teams responsible for managing AI at their companies.

Employees clearly see value in AI. However, a rogue AI tool can process and expose proprietary information in a single session, before anyone on your security team knows the tool was ever used. The business challenge is figuring out how to capture value without losing visibility into where company information is going, which tools are being used, and how AI is influencing everyday work.

What Is Shadow AI?

Shadow AI refers to any AI tool, service, or embedded AI feature used within an organization without IT or security team approval, oversight, or governance. The critical phrase is without oversight. An employee using a company-managed, IT-approved AI platform is not shadow AI. An employee pasting a client contract into a free public chatbot from their work laptop is, without question, shadow AI.

Sanctioned AI use involves vendor vetting, data processing agreements, access controls, and audit trails. Unsanctioned AI use has none of those safeguards. The data goes somewhere; it may be stored, and your organization has no record of what information left the building. Consider what that means in practice. A single session with a public chatbot could expose proprietary information with no log entry to show for it. And that’s not all. Shadow AI extends much further than chatbots, including:

• AI meeting assistants
• Writing and research tools
• Coding assistants
• Image and video generators
• Browser extensions
• AI-powered SaaS applications
• Personal AI subscriptions
• Automated AI agents and workflows

Shadow AI does not mean that using ChatGPT, Gemini, Claude, Copilot, or another AI platform at work is automatically unsafe or prohibited. The larger issue is governance. A company may have an approved AI environment with established privacy controls, security requirements, and acceptable-use policies. The same organization may prohibit employees from using personal or unapproved accounts with company information. That distinction matters.

Why Is Shadow AI Growing?

Shadow IT, employees adopting software without IT approval, has been a known problem for decades. Shadow AI is its faster, more data-hungry successor. 

The increase is understandable. Employees realize that generative AI can help them complete dozens of everyday tasks more efficiently. And AI tools are also easy to access. In many cases, all an employee needs is a browser and a personal account. The result is a growing gap between how quickly employees adopt technology and how quickly organizations can evaluate, approve, and govern it.

Employees want to work faster

Someone staring at a lengthy document may think, "AI could summarize this in 30 seconds." That instinct is understandable. The potential problem begins when the employee focuses on what the AI can do without considering what information they're giving it.

Consumer AI tools are readily available

Consumer AI tools are readily available through free services, personal subscriptions, browser extensions, and applications that employees may already use. However, not all AI tools meet security, privacy, compliance, or data-protection requirements. 

Policies may be unclear

"Don't use AI" is very different from explaining which tools employees can use, which accounts they should use, what information is allowed, which activities require approval, and whom they should ask when they're unsure. When those answers aren't clear, employees may create their own rules.

AI is increasingly invisible

Not every AI tool announces itself as an AI application. AI capabilities are increasingly embedded inside the software that employees already use. That makes understanding the organization's AI footprint more complicated than maintaining a simple list of chatbots.

Before using AI for business, employees should ensure the solution is approved for use and avoid entering confidential, proprietary, personal, or sensitive company information into unapproved services.

What Are Examples of Shadow AI in the Workplace?

Shadow AI can look surprisingly ordinary. A marketing employee could paste an unreleased campaign brief into a personal AI account to generate ideas. A salesperson might upload customer information to create personalized outreach. Someone in HR might use an unapproved AI service to draft offer letters with candidate names, salaries, and start dates. A finance employee could upload an internal spreadsheet for analysis. A developer might paste proprietary code into an AI coding assistant. An employee could invite an AI meeting bot into a confidential internal or customer call. In each case, the employee may have a legitimate business objective. The question is whether the tool, account, information, and use case have been appropriately reviewed.

What Are the Risks of Shadow AI?

Shadow AI creates more than one type of risk. The exact exposure depends on the AI platform, its configuration, the information involved, and what the employee asks it to do.

Sensitive data exposure

Shadow AI can create data security risks through seemingly routine workplace tasks. An employee might use an AI tool to summarize a customer email without realizing that the prompt could include names, email addresses, account details, or confidential business conversations. Uploading a spreadsheet for analysis could expose financial data, employee information, or customer records in much the same way. The risk is not necessarily the prompt itself, but where that information goes once it is entered into an unapproved AI platform.

Privacy and compliance concerns

Some types of information may be subject to regulatory, contractual, or organizational requirements. Sending that information to an unauthorized third-party application could create issues beyond ordinary cybersecurity concerns.

Intellectual property exposure

AI use may involve proprietary code, product designs, research, marketing plans, internal documents, and other intellectual property. The more valuable the information, the more important it becomes to understand where it is going.

Inaccurate AI output

Shadow AI isn't only about what employees put into AI. It is also about what comes out. Generative AI can produce inaccurate, incomplete, or misleading information. When AI-generated content is used without appropriate human review, those errors can affect customer communications, analysis, decisions, and workflows.

Unauthorized integrations

AI tools are becoming more capable of connecting to email, calendars, cloud storage, CRMs, and other business systems. That makes an AI tool's permission just as important as its prompts.

AI Governance Goes Beyond the IT Department

AI governance cannot be the exclusive domain of IT. Technology teams can evaluate tools and security controls, but they do not always know why someone needs a particular application. Managing workplace AI requires collaboration.

• Leadership helps define the organization's AI strategy.
• IT and security evaluate technology, access, and risk.
• Legal and compliance help address regulatory, contractual, and privacy considerations.
• HR can support policies, training, and employee communication.
• Department leaders understand the real-world workflows AI is being used to improve.
• Employees are often the first people to discover useful new AI applications.

Good AI governance connects all of them.

How Can Businesses Manage Shadow AI?

The goal of managing shadow AI isn’t simply to catch employees using it. It should be to understand how the AI tool is being used and why. For example, if an entire department starts using AI meeting assistants, the organization may not have a compliance problem. It may have an unmet business need.

A practical management approach can be organized in five steps:

Discover: Identify the AI applications, accounts, and workflows employees already use. Don't assume the official technology inventory tells the entire story.

Assess: Understand what each tool does, what information it receives, which systems it can access, and which business processes it supports. Not every AI application presents the same level of risk.

Define: Establish clear expectations. Employees should understand which AI tools are approved, which information is restricted, what AI applications require review, and where to go with questions.

Enable: Governance works better when employees have useful alternatives. If AI genuinely helps employees work more efficiently, removing the tool without addressing the underlying need may push the same behavior elsewhere. Give employees approved ways to accomplish legitimate tasks.

Educate: AI is changing too quickly for a policy document that employees read once and then forget. Ongoing security awareness and AI education can help employees recognize new risks as tools and workplace practices evolve.

From Shadow AI to Smarter AI Governance

As organizations continue adopting AI, finding the right balance between innovation and control is more important than ever. Employees are already showing where AI can help them save time, solve problems, and make everyday work a little easier.

The opportunity for businesses is to better understand how employees use AI, what they are trying to accomplish, and what information those tools may access or handle. With that visibility, organizations can build a practical AI framework that includes a vetted list of approved tools, a clear process for requesting and reviewing new ones, and transparency around why certain applications may be restricted. The result is an approach that supports productivity without losing sight of security, governance, and trust.

The goal is not to limit innovation. It is to give employees a clear path for using AI responsibly so they can keep exploring new ideas and working more efficiently, with the right guardrails in place.