Shadow AI, the use of AI tools and accounts that the organisation has not approved or cannot see, is now normal behaviour for a large share of the workforce, and most of the numbers quoted about it cannot be traced to a source. This page collects shadow AI and employee AI use statistics only, each with its publisher, report, year and sample, taken from the publisher's own page. It covers how many employees use AI and bring their own tools, how much of that use runs through personal accounts, what data ends up in prompts, how many organisations have policies, and what shadow AI adds to breach costs. It is written for executives and security leaders who need defensible figures for board papers and business cases. For AI security statistics more broadly, see the AI Security Statistics Guide. Last reviewed: November 2026.
Key takeaways
- Most AI users bring their own tools. Microsoft and LinkedIn's 2024 Work Trend Index found 75% of knowledge workers use generative AI at work and 78% of those users bring their own AI tools.
- Personal accounts are falling but still common. Netskope's Cloud and Threat Report 2026 found 47% of generative AI users on personal accounts, down from 78% a year earlier; Verizon's 2026 DBIR puts non-corporate account use on corporate devices at 67%.
- Source code is the data most often sent. Source code made up 42% of generative AI data policy violations in Netskope's 2026 data and was the most common type submitted to external models in Verizon's DLP dataset.
- Policy lags use. Only 40% of employees in the 2025 University of Melbourne and KPMG global study said their workplace had generative AI policy or guidance, and IBM found 63% of breached organisations had no AI governance policy or were still developing one.
- Shadow AI raises breach costs. IBM's Cost of a Data Breach 2025 found organisations with high levels of shadow AI paid $670,000 more per breach, and one in five organisations reported a breach linked to shadow AI.
Employee AI use and bring your own AI
The starting point for any shadow AI discussion is how widespread AI use is, and how much of it the employer supplies.
| Figure | Source, year | Sample |
|---|---|---|
| 75% of knowledge workers use generative AI at work | Microsoft and LinkedIn, 2024 Work Trend Index (May 2024) | About 31,000 knowledge workers, 31 markets |
| 78% of AI users bring their own AI tools to work; 80% at small and medium-sized companies | Microsoft and LinkedIn, 2024 Work Trend Index | As above |
| 58% of employees intentionally use AI at work; 31% weekly or daily | University of Melbourne with KPMG, Trust, attitudes and use of AI (2025) | More than 48,000 people, 47 countries |
| 45% of employees are regular AI users, authorised or not, on corporate devices, up from 15% the previous year | Verizon, 2026 Data Breach Investigations Report | DLP telemetry from contributors |
| About 15% of users in the average organisation interact with AI apps each month | Netskope Threat Labs, Cloud and Threat Report 2026 | Customer telemetry, October 2024 to October 2025 |
| 28% of workers use generative AI at work, and more than half of them do so without formal employer approval | Salesforce with YouGov, The Promises and Pitfalls of AI at Work (November 2023) | More than 14,000 workers, 14 countries |
The spread between 15% and 75% reflects different questions: survey figures count anyone who uses AI at work at all, while telemetry counts users seen reaching AI services in a month through monitored traffic. The trend across sources is consistent: use has grown sharply since 2023, and employer-provided tools have not kept pace with it. The Salesforce figure is the oldest on this page and is best read as a 2023 baseline.
Unsanctioned tools and personal accounts
Personal accounts are the clearest measurable signal of shadow AI, because data sent through them leaves the organisation's contractual and logging controls.
- Personal accounts falling: Netskope's Cloud and Threat Report 2026 found 47% of generative AI users used personal, unmanaged accounts, down from 78% the year before, while use of organisation-managed accounts rose from 25% to 62%.
- Still the majority on some measures: Verizon's 2026 DBIR found 67% of users access AI services through non-corporate accounts on their corporate devices, which Verizon describes as a slight decrease from the previous year.
- Shadow AI as insider activity: Verizon's 2026 DBIR reports that shadow AI is now the third most common non-malicious insider action in its DLP dataset, with a fourfold increase in percentage from the previous year.
- Blocking is near universal: Netskope found 90% of organisations actively block at least one generative AI app, and the average organisation blocks 10.
The two personal account figures differ because they measure different populations and periods, but both show that a large share of AI traffic still runs outside managed accounts. Blocking apps does not remove the demand; it moves users to the next unblocked tool. See the Shadow AI Discovery Guide for how to find this traffic.
Sensitive data in prompts
| Figure | Source, year |
|---|---|
| 223 generative AI data policy violations per organisation per month on average, double the previous year, committed by an average of 3% of generative AI users | Netskope Threat Labs, Cloud and Threat Report 2026 |
| Data types in those violations: source code 42%, regulated data 32%, intellectual property 16% | Netskope Threat Labs, Cloud and Threat Report 2026 |
| Source code was the most common data type submitted to external generative AI models, followed by images and other structured data | Verizon, 2026 DBIR (DLP dataset) |
| Research and technical documentation was being uploaded to unauthorised AI systems in 3.2% of DLP policy violations | Verizon, 2026 DBIR (DLP dataset) |
| 48% of respondents had entered non-public company information into generative AI tools, and 45% had entered employee information | Cisco, 2024 Data Privacy Benchmark Study (2,600 privacy and security professionals, 12 geographies) |
The Cisco figures are notable because the respondents were privacy and security professionals, the people expected to know the rules. The Samsung ChatGPT source code leak in 2023, where engineers pasted proprietary source code and a meeting transcript into a public chatbot within weeks of being allowed to use it, is the incident behind many of these concerns. See the Generative AI Data Loss Prevention Guide for controls.
Hidden use and policy breaches
- Hiding AI use: 57% of employees say they hide their AI use and present AI-generated work as their own (University of Melbourne with KPMG, 2025).
- Policy breaches: the same study reports that almost half of employees admit using AI in ways that contravene company policies, including uploading sensitive company information to free public AI tools; it gives no exact percentage in its release.
- Reluctance to admit use: 52% of people who use AI at work are reluctant to admit using it for their most important tasks (Microsoft and LinkedIn, 2024 Work Trend Index).
- Passing off AI work: 64% of workers in Salesforce's 2023 survey said they had passed off AI-generated work as their own.
- Unchecked output: 66% of people rely on AI output without evaluating its accuracy, and 56% say they have made mistakes in their work because of AI (University of Melbourne with KPMG, 2025).
Hidden use matters to defenders because surveys undercount it and employees will not volunteer it when a policy feels punitive. Telemetry, not self-reporting, is the only reliable measure.
Policy and governance adoption
| Figure | Source, year |
|---|---|
| 40% of employees say their workplace has a policy or guidance on generative AI | University of Melbourne with KPMG, 2025 |
| 63% of breached organisations have no AI governance policy or are still developing one | IBM and Ponemon Institute, Cost of a Data Breach 2025 |
| 37% of organisations have policies to manage AI or detect shadow AI | IBM and Ponemon Institute, Cost of a Data Breach 2025 |
| 34% of organisations with an AI governance policy perform regular audits for unsanctioned AI | IBM and Ponemon Institute, Cost of a Data Breach 2025 |
| 27% of organisations had banned generative AI for the time being; 63% limited what data can be entered and 61% limited which tools employees can use | Cisco, 2024 Data Privacy Benchmark Study |
| Nearly 7 in 10 workers had not completed or received training on safe and ethical use of generative AI | Salesforce with YouGov, 2023 |
| 60% of leaders worry their organisation's leadership lacks a plan and vision for AI | Microsoft and LinkedIn, 2024 Work Trend Index |
A policy that exists but is not audited does little: IBM's figure that only about a third of organisations with an AI governance policy audit for unsanctioned AI is the more useful number for a board. Templates to start from are the Shadow AI Policy Template and the AI Acceptable Use Policy Template.
Breach cost and impact
- Shadow AI breaches: one in five organisations reported a breach linked to shadow AI (IBM and Ponemon Institute, Cost of a Data Breach 2025, 600 organisations, breaches from March 2024 to February 2025).
- Added cost: organisations with high levels of shadow AI paid $670,000 more in breach costs than those with low or no shadow AI (IBM, 2025).
- Data compromised: shadow AI incidents compromised personally identifiable information in 65% of cases against a 53% global average, and intellectual property in 40% against 33% (IBM, 2025).
- AI breaches and access controls: 13% of organisations reported breaches of AI models or applications and a further 8% did not know whether they had been compromised; 97% of the compromised organisations lacked AI access controls (IBM, 2025).
Shadow AI breaches increasingly arrive through OAuth grants and API keys given to AI apps rather than through copy and paste. The Vercel Context.ai OAuth breach in 2026 began with a token for an AI tool that had broad workspace access. See the SaaS and OAuth App Governance Guide.
How to read these numbers
| Source | Method | Watch for |
|---|---|---|
| Microsoft and LinkedIn Work Trend Index | Survey of knowledge workers | Knowledge workers only, not the whole workforce |
| University of Melbourne with KPMG | Representative public survey in 47 countries | Some figures cover the general population, not only employees |
| Salesforce with YouGov | Survey of full-time workers, 2023 | Oldest data here; use as a baseline |
| Cisco Data Privacy Benchmark | Survey of privacy and security professionals | Respondents are specialists, not general staff |
| Netskope Cloud and Threat Report | Telemetry from customers' security service | Customer base skews to organisations already monitoring AI |
| Verizon DBIR | Contributor DLP telemetry | Not confirmed breach analysis |
| IBM Cost of a Data Breach | Interviews about breaches at 600 organisations | Breached organisations only; vendor sponsored, run by Ponemon Institute |
Most figures on this page come from vendor-sponsored or vendor-run studies; each is cited to its publisher so readers can judge the method. Do not combine figures from different sources into a single percentage.
What the numbers mean for defenders
- Provide a sanctioned option first: the shift in Netskope's data from personal to managed accounts, from 78% to 47% personal in a year, shows that supplying enterprise AI accounts moves behaviour faster than blocking.
- Measure with telemetry: with 57% of employees hiding AI use, surveys will undercount shadow AI. Use secure web gateway, CASB, DLP and identity provider logs. See the AI Usage Monitoring Guide.
- Protect source code first: both Netskope and Verizon rank source code as the leading data type sent to AI tools. Prioritise DLP rules and repository access for code.
- Audit the policy, not just publish it: IBM found only 34% of organisations with an AI policy audit for unsanctioned AI.
- Treat AI app grants as identities: OAuth tokens and API keys given to AI apps are non-human identities with access to company data; inventory and review them.
- Train with specifics: with nearly 7 in 10 workers untrained in Salesforce's 2023 survey, short, task-based guidance on what data never goes into an AI tool is the cheapest control.
Practitioner checklist
- Use only figures with a named publisher, report, year and sample in board papers, and link the publisher's page.
- Measure your own baseline: share of AI traffic through personal accounts, number of AI apps in use and DLP events to AI destinations.
- Provide enterprise accounts for the AI tools staff already use, then restrict personal account access.
- Apply DLP rules for source code, regulated data and intellectual property on AI destinations.
- Inventory OAuth grants and API keys issued to AI apps and remove unused or over-scoped ones.
- Publish an AI acceptable use policy and audit compliance against it at least quarterly.
- Deliver short, role-specific training on what data must never be entered into AI tools.
- Refresh this evidence base when the next editions of the annual reports are published.
Standards and references
- IBM: Cost of a Data Breach Report 2025 release
- Netskope Threat Labs: Cloud and Threat Report 2026
- Verizon: 2026 Data Breach Investigations Report, Executive Summary
- Microsoft and LinkedIn: 2024 Work Trend Index, AI at Work Is Here
- University of Melbourne: Global study on trust, attitudes and use of AI 2025
- KPMG: Trust, attitudes and use of artificial intelligence
- Cisco: 2024 Data Privacy Benchmark Study release
- Salesforce: The Promises and Pitfalls of AI at Work
Related NHI Mgmt Group resources: AI Security Statistics Guide · Shadow AI Discovery Guide · Top 10 Shadow AI Risks · Shadow AI Policy Template