Shadow AI is the use of AI tools at work that an organization has not captured in its approved systems and processes. Employers can manage it more effectively by finding out what staff are using and why, assessing the tools and data involved, providing secure alternatives, and making disclosure safe. The aim is to reduce risk and improve visibility—not assume every employee is acting maliciously or that a ban will make the behavior disappear.
What is shadow AI?
The UK National Cyber Security Centre (NCSC) defines shadow AI as “the use of AI technology which isn’t captured in an organisation’s approved systems and processes.” It is a form of shadow IT, sometimes called grey IT. The defining feature is the organization’s lack of visibility or approval—not simply that an employee uses AI.
For example, an employee who uses a personal AI chatbot to summarize a work meeting, rewrite a document, or compile information may be using shadow AI if that service and use are outside the employer’s approved processes. The same task is not shadow AI merely because it involves AI: an approved tool used under the organization’s rules is different.
Why do employees use unapproved AI tools?
Often, employees are trying to get work done rather than bypass security for its own sake. NCSC guidance on shadow IT says unofficial tools and workarounds commonly arise when approved services or processes do not meet a practical need. A request process may be slow, a needed service may be unavailable, or an approved tool may lack useful functionality.
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AI can appear to offer a quick answer for tasks such as rewriting documents, summarizing meetings, or compiling information. That makes employee use useful operational feedback: it can reveal a task the organization has not made easy to complete safely. NCSC recommends open, no-blame communication so staff are more likely to share what they use and why.
What are the risks of shadow AI at work?
Data exposure and loss of control
Entering company or customer information into an unapproved AI service can increase the risk of data breaches, intellectual-property loss, or failure to meet regulatory requirements. That does not mean every prompt causes a breach. The concern is that the employer may not know what information is being submitted or have sufficient control over how the service handles it.
Consumer services can differ in how they store, retain, or use submitted information. Whether an organization has appropriate privacy controls matters; do not assume every provider handles data in the same way.
Reduced organizational visibility
When staff use services outside approved systems, the organization may have less visibility into the tools, information flows, and controls involved. This can make it harder to understand where sensitive or proprietary material is going and to manage the associated risks.
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AI agents and access to systems
An AI agent may have access to data, services, or privileges so it can carry out tasks. The NCSC warns that if an agent is exploited, an attacker could gain access to resources the agent can legitimately reach. Employers assessing an agent should therefore consider both its intended task and the scope of its integrations and permissions.
Privacy and compliance concerns
The UK Information Commissioner’s Office (ICO) has published future scenarios involving employees using agents without employer permission and possible privacy harms or data-protection compliance errors. These are scenarios, not ICO guidance and not a determination that a particular use is compliant or noncompliant. Applicable privacy, employment, sector, and AI rules depend on the jurisdiction and the specific use.
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How can employers manage shadow AI?
1. Find out what staff use and what they need
Invite employees and managers to disclose the tools they use, the tasks they use them for, and the kinds of data involved. Ask what approved options they tried and what prevented those options from working. Make reporting part of improving services, not an automatic trigger for blame.
2. Address the unmet need
Check whether the cause is missing functionality, lack of access, or an approval route that is too slow or difficult to use. Where possible, bring the activity into the open by solving the problem that led people to work around the approved process.
3. Assess the tool, the task, and the data flow
There is no single checklist that suits every organization, but an assessment should follow the risks involved. Consider:
- What task the tool performs and whether it meets a genuine work need.
- What information employees submit, including whether it is sensitive, customer-related, or proprietary.
- How the service handles submitted data and what privacy controls are available.
- What visibility and governance the organization can maintain over use.
- For agents, which systems, data, and privileges they can access and whether that access is appropriately scoped.
4. Provide approved alternatives and clear rules
Offer approved tools that meet common work needs, and explain which tasks and data are appropriate for each. A secure alternative is more useful when employees can access it in practice and understand how to use it safely.
5. Make disclosure safe
A positive, no-blame approach can help employees report tools and workarounds before they become harder to see. NCSC cautions that blaming or punishing staff may discourage disclosure and reduce the organization’s visibility.
6. Keep governance current
AI tools, employee needs, and data flows can change. Maintain an up-to-date view of approved tools, review guidance and assessments as use changes, and keep the process for getting a tool assessed practical. The NCSC says shadow AI is unlikely to disappear completely and recommends reducing its risks rather than assuming it can be eliminated.
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How should employers choose between restricting and approving AI?
A restriction, a controlled pilot, or broader approved access should be weighed against the work need and the organization’s ability to manage the risks. The NCSC does not prescribe a universal scoring framework, but its guidance points to four useful considerations:
| Consideration | Question for the employer |
|---|---|
| Task fit | Does the option help employees complete the task they actually need to do? |
| Data handling | What information is involved, and how does the service handle it? |
| Visibility and control | Can the organization govern access and understand how the tool is being used? |
| Practicality | Can employees use the assessment and approval route without undue friction? |
If a policy is easy to state but leaves a real work need unmet, employees may continue using tools outside the organization’s view. If a tool or use case creates risks the employer cannot adequately assess or manage, restricting it may be appropriate. The decision should follow the task, data, and controls rather than treating all AI use as identical.
What do the available figures say about shadow AI use?
In an article published on 7 September 2026, the NCSC reported that one study found 71% of employees said they used AI tools that their employer had not approved. That is a result from one study as reported by the NCSC, not a universal estimate of workers or organizations.
The UK Department for Science, Innovation and Technology (DSIT) completed 3,500 interviews for its 2025 business AI adoption survey, with fieldwork from 12 February to 2 May 2025. DSIT explicitly says the survey does not provide insight into shadow-AI adoption, so it should not be used to estimate how many employees use unapproved AI.
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The NCSC’s definition and recommendations cited here are UK government sources. Employers elsewhere should apply the privacy, employment, sector, and AI requirements relevant to their jurisdiction and use case.
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Sources
- National Cyber Security Centre: The hidden risks of shadow AI
- National Cyber Security Centre: Shadow IT guidance
- Information Commissioner’s Office: Scenarios for the future of agentic AI
- Department for Science, Innovation and Technology: AI adoption research
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