In short
Shadow AI is the use of AI tools, browser extensions, desktop apps and agents that an organisation has not approved or does not know about, often through personal accounts. It risks company data reaching unchecked providers. The fix is discovery first, then a policy that allows, restricts or blocks each tool, enforced by a tool such as Workforce AI Security.
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What is shadow AI?
Shadow AI is any use of artificial intelligence at work that sits outside the organisation's knowledge or approval. It is the AI version of shadow IT, with two differences that make it harder to manage:
- It is easier to start. Most AI tools need nothing more than a browser and an email address, and many are free.
- It is where the data goes. The whole point of an AI tool is to give it information to work on, so unapproved use usually means company data leaving the business.
Shadow AI is not only standalone chatbots. It includes AI features switched on inside SaaS apps you already use, AI browser extensions, desktop AI apps, meeting note-takers, coding assistants in developers' editors, and AI agents connected to company systems through MCP servers. It also includes personal accounts on a tool the organisation has approved for business use: the brand is sanctioned, the account is not.
Most shadow AI is well meant. People use it because it helps them do their jobs, which is why the answer is rarely a blanket ban.
Shadow AI examples
Common patterns (illustrative, not incidents at any particular organisation):
- A manager pastes a staff performance review into a free chatbot to soften the wording
- A sales team installs an AI email-writing extension that reads every page they open
- An AI note-taker joins client calls because one person signed up and connected their calendar
- A developer uses a personal coding assistant account against the company code base
- Someone connects an AI agent to their work mailbox and file storage to "save time on admin"
- A finance analyst uploads a management accounts spreadsheet to an AI tool to build charts
- Staff use the personal version of an AI tool that the company has licensed, because they were already signed in
Each one is a reasonable thing to want to do. Each one also moves data somewhere the organisation has not assessed.
Shadow AI risks
The risks follow the data:
- Unknown data handling: you do not know whether the provider uses inputs to train models, how long it keeps them or where they are stored and processed. Some providers process data outside the UK, which raises international transfer questions under UK GDPR.
- Personal data without a lawful basis: if personal data goes into an unapproved tool, your organisation, as controller, may not have the contract, transparency or DPIA the processing needs. See AI and UK GDPR.
- Confidential and client information: contracts, pricing, plans and client files may breach confidentiality obligations once shared.
- Credentials and source code: secrets pasted into prompts, or assistants reading whole repositories.
- Excess permissions: extensions and agents that can read pages, files and mailboxes, and act on them.
- No audit trail: if something goes wrong, there is no record of who sent what, where.
For the full list, see our guide to AI security risks, and for the data side specifically, AI data leakage.
Shadow AI discovery: how to find it
You cannot manage what you cannot see, so discovery comes first. The common approaches each see part of the picture:
| Approach | What it finds | What it misses |
|---|---|---|
| Ask people (survey or amnesty) | Tools people are happy to admit to, and why they use them | Anything people forget or do not think of as AI |
| Expense and procurement records | Paid subscriptions | Free tools and personal accounts |
| DNS, proxy or firewall logs | Traffic to known AI domains from the office network | Off-network use, AI features inside approved SaaS apps, and what was actually sent |
| SaaS and identity logs | Sign-ins and app connections made with work accounts | Personal accounts and tools that do not use your sign-in |
| Browser and endpoint visibility | AI apps, extensions and sessions per user, plus desktop apps and agents where a desktop agent is installed | Unmanaged personal devices |
Asking people is worth doing anyway: it tells you what they need, which shapes what you approve. But for an accurate inventory, visibility at the browser and on the device is what closes the gaps.
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How Workforce AI Security discovers shadow AI
Discovery is the first of the four stages in Check Point's description of Workforce AI Security. According to Check Point, it gives you:
- Visibility of sanctioned and shadow AI: AI apps and tools, browser extensions, code assistants, agents and MCPs in use, from coding agents to shadow AI, including public tools such as ChatGPT, Gemini, Claude and Copilot
- An AI application catalogue with risk scoring, which you can search for any app, including ones not yet used in your organisation
- Adoption trends: which applications are driving AI adoption, broken down by area of risk
- Session-level visibility: a breakdown by app, session and user, so you see who is using what
- AI inside connected SaaS platforms: detection of AI use within SaaS apps, not just standalone AI sites
- Use-case classification: AI-powered analysis sorts prompts into tens of use-case categories, such as Marketing, Debugging, Legal and Email & Communication, so you understand why people use each tool
Discovery runs through a lightweight browser extension. A desktop agent extends it to desktop AI apps, coding assistants and agents; that broader coverage is in the Enterprise tier, while Essentials covers AI used through web applications. See how Workforce AI Security works for the full picture.
From discovery to control: allow, restrict or block
Once you know what is in use, decide a status for each tool and data type. A simple three-way split works for most organisations:
- Allow: approved, business-grade tools with suitable data terms, possibly with DLP still checking prompts for the most sensitive data
- Restrict: tools people may use, but with sensitive data redacted, copy and paste limited, uploads controlled or a warning shown
- Block: tools whose terms, location or risk are unacceptable
Check Point says Workforce AI Security can block access to unauthorised AI apps, apply different policies to managed and unmanaged apps, redact sensitive data inline and replace it with labelled placeholders, restrict copy and paste, and guide users with an explanation of what happened and why. That guidance matters: people who understand the rule are more likely to move to the approved tool than to look for a workaround.
Write the decisions into an AI acceptable use policy so staff know where they stand, and read our guide on how to block AI tools for when blocking is the right answer.
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What a shadow AI policy should say
A shadow AI policy does not need to be long. It usually works best as a section of your AI acceptable use policy rather than a separate document, and covers:
- Approved tools: which AI services, extensions and agents are allowed, and with which account (work, not personal)
- Data that must stay out: personal data, client information, credentials, source code and anything marked confidential, unless the tool is approved for it
- How to ask for a new tool: a simple request route, so people have a better option than signing up quietly
- Agents and connectors: who may connect an AI agent or MCP server to company systems, and who approves it
- What is monitored: a plain statement that AI use is discovered and that some data may be redacted or blocked
- What to do if something goes wrong: how to report data shared with the wrong tool
Our AI acceptable use policy template includes wording you can adapt.
How we help
Forever Systems can start with a shadow AI discovery pilot using Workforce AI Security, so you get a factual picture of AI use across a group of users before deciding anything. We then help you agree an allow, restrict or block decision for each tool, turn that into policy, and roll it out in stages, starting with monitoring and moving to redaction and blocking as people adjust.
To find out what AI your people are really using, contact us.
How Forever Systems helps you use AI safely
We are an independent UK IT and cyber security provider. We supply Check Point Workforce AI Security and help organisations put sensible, workable rules around AI at work.
- See what is really in use: we can start with a discovery pilot, so you know which AI tools, browser extensions, coding assistants and agents your people use before anyone writes rules
- Policy you can enforce: we help you decide what to approve, restrict or block, and turn your AI acceptable use policy into Workforce AI Security policies
- The right tier: Essentials for browser-based AI, Enterprise when desktop apps, developer code assistants and agents are in the picture, confirmed at quote
- Staged roll-out: from monitoring to warnings, redaction and blocking at a pace your people can absorb, with reviews along the way
- Around the tool: Microsoft 365 permissions ahead of Copilot, device management for the extension and agent, and safe use of AI training for staff
We work with businesses, schools, charities and public sector organisations. Our guides are general information, not legal advice.
Frequently asked questions
What is shadow AI?
Shadow AI is the use of AI tools, browser extensions, desktop apps or agents that an organisation has not approved or does not know about, including personal accounts on AI services. It matters because company and personal data may be shared with providers whose terms and security have not been checked.
What is the difference between shadow AI and shadow IT?
Shadow AI is a type of shadow IT. The difference is that AI tools are designed to receive information, so unapproved use usually means data leaving the business, and AI agents can also take actions with the user's permissions.
How do you detect shadow AI?
Combine a staff survey with technical discovery. Network logs show traffic to known AI sites from the office, but browser and endpoint visibility is needed to see AI features inside SaaS apps, extensions, desktop apps and agents. Workforce AI Security provides this through a browser extension and, in the Enterprise tier, a desktop agent.
Should we ban shadow AI?
Usually not outright. Approve a business-grade tool, restrict others with redaction or copy and paste limits, and block the tools with unacceptable risk. A blanket ban tends to push use onto personal devices where you have no visibility at all.
Does a shadow AI policy need to mention agents and extensions?
Yes. AI browser extensions and agents connected through MCP can read pages, files and mailboxes and act on them, so your policy should say which are allowed and how new ones are approved.
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