Shadow AI: how to find unapproved AI use in your company

What is shadow AI?

Shadow AI is the use of AI tools for work without approval from the company's IT, security or legal team. It is a form of shadow IT with two differences: the tool processes and may keep what staff type, and some AI tools can act in company systems. Shadow AI takes five common forms:

• Public AI assistants used through personal or free accounts.
• AI features inside approved software, such as writing, search or meeting-summary features that a vendor adds in an update.
• Browser extensions that read the pages staff open in order to summarize, translate or rewrite them.
• AI note takers that join calls, record them and send the summary to an outside service.
• AI agents and automations that staff connect to email, files, the CRM or code repositories with their own credentials.

Most shadow AI starts with a good intention: someone wants to finish their work faster. That is why the goal of this guide is visibility and safer choices, not blame.

How common is shadow AI?

IBM's Cost of a Data Breach Report 2026 (opens in a new tab) studied 602 organizations that had a data breach between March 2025 and February 2026. IBM reports (opens in a new tab) that security incidents involving unapproved AI use rose from 20% to 43% of these organizations in one year. Of those incidents, 49% led to data loss or compromise and 21% to regulatory fines.

Staff surveys show the same pattern. In a global study by KPMG and the University of Melbourne (opens in a new tab) of more than 48,000 people in 47 countries, almost half of employees admitted using AI in ways that go against company policies, including uploading sensitive company information into free public AI tools. Only 40% said their workplace had a policy or guidance on generative AI use.

Why staff use AI tools without approval

Shadow AI is mostly a process problem, not a discipline problem. Three causes are common:

• The tools are useful. AI drafts, summarizes and translates in seconds. Staff who find a tool that saves an hour a day keep using it.
• Approval is slow or unclear. When a request takes months, or nobody knows who decides, staff stop asking.
• There is no approved alternative. A policy that only says no leaves staff with the free tool they already know.

Hidden use makes the problem harder to measure. In the same KPMG study, 57% of employees said they hide their use of AI and present AI-generated work as their own. If the first step of your program punishes past use, people will simply hide it better.

The real risks of shadow AI

Confidential and personal data in public tools

Free and personal versions of AI tools usually run under consumer terms. Depending on those terms, the vendor may keep prompts and files, let its staff review them or use them to train models. Once a customer list, a contract or source code is pasted in, you usually cannot pull it back or prove where it went.

Unvetted AI features in approved software

A vendor you approved years ago can add an AI feature in a routine update. The feature may send your data to a third-party model provider that your contract and your security review did not cover. Treat a new AI feature like a new tool: someone should decide whether to switch it on.

Browser extensions and personal accounts

An AI browser extension can often read everything on the pages staff open, including web mail, CRM records and internal portals. Personal accounts are a second blind spot. You have no admin controls, logs or deletion rights, and when an employee leaves, the chat history with company data leaves with them.

AI agents connected without review

An AI agent can read email, change records and send messages. When an employee connects one with their own credentials, the agent receives all of that person's access, which is usually far more than its task needs. It is also exposed to prompt injection: tricking an AI with hidden instructions placed in an email, document or web page it reads. An agent nobody reviewed has no approval steps and no logs to limit or trace the damage.

Regulatory exposure

Laws on personal data apply to AI tools like any other software. Article 28 of the GDPR (opens in a new tab) requires a contract when a processor handles personal data on your behalf, and a personal account on a free AI tool comes with no such contract. In the United States, the HIPAA rules (opens in a new tab) let a covered entity disclose protected health information to a business associate only with written assurances, usually a business associate agreement.

In the EU, Article 4 of the AI Act asks companies that use AI at work to take measures to support their staff's AI literacy. The European Commission's AI literacy questions and answers (opens in a new tab) explain the duty. Training people well on tools you do not know about is difficult.

How to find shadow AI in your company

No single source shows all AI use. Combine several, start with the data you already have and record every finding in one inventory. Before you review staff activity, check with your legal and human resources (HR) teams: in many countries, monitoring employees requires notice, a legal basis or consultation with staff representatives.

Record each tool in the inventory with the same fields, so you can compare them later:

• The tool, the vendor and the plan or account type.
• The teams and approximate number of users.
• The tasks it is used for and the data classes that go into it.
• Any connection to company systems, such as email, files, the CRM or code repositories.
• Where you found it: logs, app consents, expenses, device inventory or the survey.
• The decision, the owner and the date of the next review.

Network, DNS and proxy logs

Firewall, web proxy and Domain Name System (DNS) logs show which AI services company devices reach. Build a list of AI service domains, search the last 30–90 days of logs and count users and upload volume for each service. Logs show that a service was used, not what staff typed into it, and they miss personal phones and home networks.

SaaS and OAuth app reviews

In your identity provider and your email and file platforms, review which third-party apps staff have granted access through OAuth, the "sign in with" and "allow access" screens. Look for AI apps with permission to read mail, files or calendars. Then open the admin console of each approved software-as-a-service (SaaS) tool and check which AI features exist and which are switched on.

Expense reports and card statements

Search expense claims and company card statements for AI subscriptions and usage charges. A personal plan claimed as an expense shows that a team finds a tool useful enough to pay for it. That makes it a strong candidate for an approved business version.

Browser extension and endpoint inventories

Endpoint management tools can list the browser extensions and desktop apps installed on company devices. Search for AI assistants, writing tools, meeting recorders and apps that run AI models locally. For each extension, check the permissions it requests, such as reading and changing data on all websites.

A no-blame staff survey

Ask staff which AI tools they use, for which tasks and with what kind of data. State clearly that the survey is for finding safer options, not for discipline, and report results by team rather than by name. The survey finds what logs miss: personal devices, mobile apps and the tasks where AI saves the most time.

Our shadow AI audit plan turns these five sources into a schedule with owners and deliverables.

How to assess what you find

Rate each tool in your inventory on four questions. One higher-risk answer is enough to move the tool up your list.

Then put each tool into one of three groups: approve it (often after moving users to a company account), replace it with an approved alternative or block it. When sensitive data went into a tool with weak terms, treat it as a possible incident. Record what was shared, ask the vendor about deletion and check with counsel whether any notification duty applies.

For tools you want to approve, our AI vendor security questionnaire gives you the questions on data handling, model training and AI-specific risks.

How to fix shadow AI without banning everything

A total ban tends to move AI use to personal phones and home computers, where you see nothing at all. A better approach makes the safe route easier than the unsafe one.

1. 1Offer approved alternatives. Buy business versions of the tools staff already use, or approve equivalents, with company accounts, SSO and terms that exclude your data from model training.
2. 2Publish a clear policy. List the approved tools, the data each may process and the person to ask. Our AI acceptable use policy template gives you a starting text.
3. 3Make requests fast. Name one owner, use a short request form and set a target answer time, for example 5 business days for low-risk tools.
4. 4Train staff. Cover data classes, checking AI output, prompt injection and how to request a new tool. Keep training records.
5. 5Monitor and review. Repeat the log, app consent and expense checks every quarter, block high-risk services at the proxy and update the approved list as needs change.

Our post on writing an AI usage policy your staff will follow shows how to keep the rules short enough to use.

How we run shadow AI discovery

In our AI security consulting projects, we work with your team in your own systems to combine these sources into one inventory of AI tools and agents. We rate each one by data and access, help you decide what to approve, replace or block and adapt the usage policy and request process to what we found. Staff get approved tools before the old ones are blocked.