What Is Shadow AI and Why Is It a Security Risk for Businesses?
Shadow AI is the use of AI tools without approval or oversight from a company’s IT or security team. Usually, it starts when an employee opens a personal AI account to complete a work task faster. The risk has grown, too, because employees aren't waiting for formal AI programs. In the 2024 Work Trend Index based on 31,000 people across 31 counties, Microsoft and LinkedIn found that 75% of knowledge workers were already using AI at work. Among those users, 78% brought their own AI tools into the workplace. For businesses planning to expand AI use in 2027, the message is clear: employees are already using it. A secure AI plan must address that reality before sensitive data leaves the company’s control.
Featured in this Shadow AI Article:
- How Does Shadow AI Leak Your Data?
- Shadow AI Requires a Unique Security Approach
- What are the Risks of Different Security Engines You Use Every Day?
- Preparing for Shadow AI in 2027
- AI Readiness eBook
- FAQs We Hear About Shadow AI
Why Does Shadow AI Happen?
Shadow AI usually happens because employees find useful tools before the business has approved a secure alternative. Maybe it's a public chatbot to summarize a customer document or connecting an AI meeting assistant to company conversations without reviewing its permissions. You'll also see AI features appear inside browser extensions or existing software, which makes unauthorized use difficult for IT teams to see.
The problem stems from not knowing which information is safe to share or there's no visibility into what happens to that information after submission.
In 2026, Cyberhaven Labs reported that 39.7% of observed based on billions of data movements across 222 companies, that workplace AI interactions involved sensitive corporate data. They also found that one-third of employees accessed AI tools through personal accounts, making interactions outside normal business oversight.
Preparing for Shadow AI in 2027
A 2027 AI plan should make the secure option easier than the unauthorized one. That creates measurable value across the business:
- More visibility: IT teams can see which approved AI platforms are interacting with company data.
- Lower exposure: Employees are less likely to enter sensitive information into personal AI accounts.
- Faster adoption: Teams can use AI without waiting for case-by-case approval.
- Stronger accountability: The business can establish clear ownership for selecting AI tools.
Going into 2027, telling your employees not to use AI won't provide enough protection. Businesses need approved tools that support productivity without forcing employees to work around security policies.
How Can Shadow AI Expose Business Data?
Shadow AI exposes data when employees enter sensitive information into a tool the company hasn't evaluated. The exposure happens through a written prompt or an uploaded file. In other cases, an AI engine receives permission to access company systems. Once information reaches an unapproved platform, you won't know where it's processed or how long it is retained.
Several types of business data may be put at risk like customer records, employee details, contracts, financial forecasts, proprietary code, login credentials, and internal strategy documents.
The security concern is not theoretical. The OWASP Top 10 for LLM Applications identified sensitive information disclosure as a critical risk for generative AI applications. Its 2025 report also recognizes prompt injection as an AI security risk. Basically, data poisoning appears separately within the same framework.
Shadow AI makes these risks harder to control because the business doesn't know which tools are processing its data. Without that visibility, security teams can't apply the same protections used within approved business systems.
Shadow AI is More Than am Exployee Training Problem
Training matters, but employees can't protect information if the company hasn't defined acceptable AI use. A general warning to “be careful with AI” leaves too much room for interpretation. Employees need to understand which tools are approved and clear guidance about the information those tools can process.
A defined AI governance policy gives businesses several advantages:
- Clearer employee decisions: People know which AI tools are safe for work.
- Consistent data handling: Sensitive information receives the same protection across approved use cases.
- Faster vendor reviews: IT can evaluate new tools against documented requirements.
- More effective monitoring: Security teams know what approved activity should look like.
- Stronger incident response: Employees know how to report accidental AI data exposure.
In July 2024, the National Institute of Standards and Technology published its Generative Artificial Intelligence Profile. This cross-industry resource helps businesses incorporate AI risk management into the design of generative AI systems as well as deployment help. The NIST framework gives businesses a credible starting point for developing AI governance around their goals.
Shadow AI Requires a Unique Security Approach
Traditional cybersecurity controls protect company-managed accounts or applications. However, Shadow AI operates through a personal account. It also involves an unapproved service that sits outside the managed environment.
AI systems add another layer of exposure because they do more than store information. They process what employees provide. The tools then generate new outputs based on that information. Connected AI applications then retrieve company content or initiate actions through other systems. This knowledge changes the security question. You can't focus only on whether a tool has been breached. You must first determine whether that tool should receive company data.
Those decisions become even more important when AI is connected to internal knowledge. The risk also increases when AI performs automated tasks. An assistant that only drafts text presents a different level of exposure than an agent with permission to access files. The risk changes again if that agent can take action inside a business application.
A risk-based approach helps the business apply stronger controls where they matter most:
- Low-risk use: The tool works only with public or nonconfidential information.
- Moderate-risk use: The tool processes internal content but cannot act outside its assigned environment.
- High-risk use: The tool accesses sensitive data or takes action inside connected systems.
This distinction prevents the business from treating every AI use case the same. It also keeps highly connected tools from receiving approval based on controls designed for simple chatbots.
Curious which AI engines are the most secure and provide the best output with the minimal amount of risk? check out these resources:
- Microsoft Copilot vs. ChatGPT: What's the Difference and Which Should You Use?
- How Are Copilot Chat and Microsoft 365 Copilot Different?
- Copilot Cowork vs. Claude Cowork: Choosing the Right AI Agent for Your Business
- ChatGPT and Other AI Engines: What Leaders Need to Know Before Using AI at Work
How Should Businesses Prepare for Shadow AI in 2027?
A 2027 AI security plan should give employees a safe way to use AI while keeping company data under appropriate oversight. Trying to block every AI tool without providing an approved alternative may push usage further outside the company’s view. The plan should begin with an inventory of the AI tools employees already use, including standalone chatbots. Embedded AI features require the same visibility. Browser extensions or meeting assistants shouldn't escape review simply because they appear smaller than enterprise platforms.
Then decide which company information approved tools may access. Permissions should reflect each user’s role. Broad access makes more data available than the AI needs to complete its assigned task.
A practical 2027 plan should establish the following:
- An approved AI tool list: Give employees clear options for common business tasks.
- Defined data boundaries: State what information employees may enter or upload.
- Role-based permissions: Limit each tool to the data required for its intended purpose.
- A named approval owner: Assign responsibility for reviewing new AI applications.
- Vendor security standards: Require providers to explain how business information is processed.
- Practical employee training: Use realistic examples based on the work employees perform.
- AI-aware incident response: Document what employees should do after sharing data with the wrong tool.
- Ongoing monitoring: Review AI usage as new tools enter the business.
Training should show employees why uploading a contract to an unapproved tool creates risk as well as direct them toward a secure alternative that meets the same business need. You're making the policy useful rather than restrictive.
The incident response plan (IRP) must also account for AI-related exposure. If sensitive information is submitted to the wrong tool, employees need a clear method for reporting the event. The response team can then assess which data was involved before determining the next action.
Just in case you didn't know, Centre’s AI readiness assessments and support help with that.
Are You Ready for AI?
Before diving into AI adoption, make sure your business is prepared to handle the cybersecurity challenges that come with it. Download our Complete Guide to AI for SMBs for a comprehensive checklist, security best practices, and expert insights to keep your data safe and secure.
Download your FREE eBook and AI Readiness Checklist here!
Other FAQs We Get About Shadow AI
What's an example of Shadow AI?
Shadow AI occurs when an employee uses an AI tool for work without company approval. One example is uploading a customer contract to a personal chatbot account for summarization. Another example is connecting an unapproved AI assistant to company meetings. The employee may have a legitimate business goal. However, the tool could process sensitive information outside the organization’s approved controls.
Can companies prevent Shadow AI by banning AI tools?
A ban may reduce some visible use, but it does not address why employees turn to unapproved tools. A stronger approach gives employees an approved option. It also defines what information that tool may process. Monitoring can then support the policy. Role-based access provides another layer of control without forcing productive AI activity underground.
What should a 2027 AI security plan include?
A 2027 AI security plan should document approved AI tools. It should also define acceptable business use. At minimum, the plan should cover permitted data access, ownership of AI approvals, vendor security reviews, employee education, usage monitoring, and AI-related incident response. The NIST AI Risk Management Framework can help organizations incorporate risk management into the design of AI systems. It also applies to deployment and ongoing use
