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Shadow AI is AI use that happens without an organization’s authorization, knowledge, or oversight. It can cluster around particular employees, teams, or workflows—especially where approved tools are hard to access or do not meet a practical need. A broad policy can miss those patterns if it says what is prohibited but does not reveal which tools people use, what they use them for, or why they turn to them.
That concentration is a useful governance hypothesis, not a proven pattern in every workplace: the surveys available differ in geography and methods, and they do not establish a universal department-by-department distribution or prove why employees adopt unapproved tools. The practical response is to find actual use, understand its context, and combine proportionate controls with workable approved options.
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What counts as shadow AI?
Shadow AI is the use of AI tools without an organization’s authorization, knowledge, or oversight. That can mean an employee signing into a public chatbot with a personal account, but it can also include other SaaS applications, AI platforms, locally deployed models, and agents. Focusing only on a list of public chatbots risks missing use elsewhere in the technology environment.
The governance gap is between formal approval and actual behavior. A tool may be outside the official process even when an employee is using it for ordinary work rather than trying to evade a rule. That distinction matters: the organization needs to manage the data and workflow involved, while also learning why the approved route was not used.
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Why broad policies can miss the pattern
A policy describes rules, not where use is happening
A general rule against putting sensitive information into unapproved tools can set an expectation, but it does not by itself identify the applications, users, tasks, or data flows that need attention. Visibility limited to chat interfaces may also miss AI features embedded in other services, platforms, local deployments, or agents. Netskope’s 2025 report describes this wider landscape and recommends identifying applications, users, and workflows (Netskope report).
Approved tools may not fit every task
In Deloitte UK’s 2026 survey, among respondents who paid for their own GenAI tools for work or used tools that may not be approved, 21% said the outside tools outperform company tools, and 14% said the tools were essential to their job but not employer-funded. These are reasons reported by that subgroup, not proof that tool gaps cause all unapproved use. They are clues worth investigating in the teams and workflows where activity appears.
KPMG’s analysis also points to perceived speed and capability, missing features, poor integration, and restrictive provisioning as factors that may contribute. Together, these findings support asking where employees encounter friction rather than assuming a policy alone will change behavior.
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Awareness and training are not universal
Deloitte’s UK GenAI Workforce Survey reported that 50% of GenAI users received no training. In a separate survey, ManageEngine reported that 93% of surveyed employees had entered information into AI tools without approval. That study, conducted by Censuswide in the United States and Canada in May 2025, included 350 IT decision makers and 350 working professionals at organizations with at least 500 employees and $10 million in annual revenue. Its result should be read within that sample, not as a global workforce estimate.
These figures describe different populations and measures; they should not be combined into a single rate. They do show why a policy’s existence cannot be treated as evidence that workers know how to follow it or that the organization can see whether it is being followed.
What the available figures do—and do not—show
Deloitte’s 2026 survey offers a UK-specific workforce snapshot. Fielded in May–June 2026 among 25,000 UK working adults aged 18–70, it reports that 63% had used GenAI. Among GenAI users, one in three said they had used it without their employer’s knowledge, 17% said they paid for their own work tools, and 46% said they used free-to-use tools at work. Those figures apply to the stated UK survey populations, not all workers or other countries.
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Other sources describe distinct samples and methods. Netskope says 60% of users in its 2025 report still used personal, unmanaged apps. Its report draws on anonymized usage data from a subset of customers who had given prior authorization; it is not a representative public workforce survey. TELUS Digital’s 2025 company press release reported that 68% of surveyed enterprise employees who used GenAI at work accessed public assistants through personal accounts, while 57% said they had entered sensitive information. Those are TELUS Digital’s survey findings, not universal estimates.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to find where employees are using AI
- Map the full environment. Identify AI-enabled SaaS applications, dedicated platforms, local deployments, and agents—not just public chatbots. Netskope recommends looking at applications, users, and workflows; its report also points to inventorying local infrastructure.
- Connect tools to users, workflows, and data. Establish who is using each discovered tool, what work it supports, and what kinds of information flow through it. This context helps distinguish a routine productivity task from a workflow that could expose sensitive data.
- Look for unmet needs and friction. Ask teams whether approved tools have the capabilities they need, integrate with their work, and are accessible without unnecessary provisioning barriers. Treat reported advantages of outside tools as leads to investigate, not proof that a specific tool is better.
- Set controls around the activity and its risk. Establish which applications are approved and apply appropriate data controls to sensitive information. Netskope recommends application controls and data loss prevention; the exact controls should reflect the tools and data your organization actually finds.
- Coach people in context and keep monitoring. Give employees practical guidance when and where it is relevant, then monitor for changes in tools and usage. New applications and workflows can create new visibility gaps, so discovery and governance need to continue.
ManageEngine’s survey release likewise recommends practical policies, relevant official tools, education, and integration into workflows. The combination matters: enforcement can reduce exposure, while better-fitting approved options and clear guidance can address reasons people bypass formal channels.
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How to judge a governance approach
Whether evaluating an internal program or a category of governance tools, compare capabilities against the activity you need to manage rather than relying on a policy statement or a single detection method.
- Coverage: Does discovery extend across SaaS apps, AI platforms, local models, and agents?
- Visibility: Can the organization identify users and the workflows in which AI is used?
- Data control: Can sensitive information be protected when it is shared with unauthorized applications?
- Enablement: Do approved tools fit employees’ tasks, integrate into their workflows, and come with contextual coaching?
- Ongoing governance: Can monitoring surface new activity and support updates to controls as use changes?
These are decision criteria, not a ranking of specific products. The right balance depends on what an organization discovers about its own tools, users, workflows, and data.
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