Shadow AI examples can be surprisingly boring. Someone has a deadline, the approved tool is slow or missing, and a free AI account is one browser tab away. The person doing it probably doesn’t think of it as risky.
That’s why this post sorts the examples by the data they touch. If you need the full background first, our guide to shadow AI covers definitions and detection. This post goes narrower: the specific patterns that show up in law firms, energy companies, clinics and portfolio companies, and the fix that fits each one.
One framing note. The scenarios below are composite, hypothetical patterns. They aren’t client stories, and the people in them are invented to make the pattern concrete.
A quick test for whether something is shadow AI
Before the examples, a working test. A tool use is shadow AI when the answer to any of these is “no” or “I don’t know”:
- Did someone with authority approve this tool for this kind of work?
- Is the data going into it allowed to leave your environment?
- Do you know the terms that govern what the provider keeps?
The second question does most of the work. The same chat tool can be harmless with a public press release and a real problem with a client’s merger file.
Shadow AI examples at law firms
The personal account at 11 p.m.
Picture a senior associate finishing a brief section late at night. The firm’s approved assistant isn’t rolled out yet, so they paste three paragraphs into a personal ChatGPT account and ask it to tighten the argument. The paragraphs include the client’s name and two facts from a sealed filing.
The exposure is confidentiality first. ABA Formal Opinion 512 ties a lawyer’s use of generative AI to Model Rule 1.6, the duty to keep client information confidential. There’s a records problem too: that chat history sits in a personal account, outside the firm’s retention schedule and outside any legal hold.
The fix that holds up is an approved tool under commercial terms that’s as easy to open as the personal one, plus a plain rule about client facts that people can remember at 11 p.m.
The notetaker nobody invited
A client call starts, and an AI notetaker joins as a participant. Maybe a lawyer’s calendar connected it months ago. Maybe the client brought it. Either way, a third-party service now holds a recording and transcript of a privileged conversation, under terms the firm never reviewed.
Fixes here are part policy, part setup. Decide who may admit bots to client meetings and say so in writing. If you want AI recaps, use one you control. In Teams, the organizer sets the Copilot option for each meeting (during and after, only during, or off). Turning it off also disables recording and transcription for that meeting. Recording consent rules vary by state, so check what applies before anyone records a client call.
The extension that reads the screen
Someone installs a browser or Outlook add-in that promises one-click email replies. To work, it asks for permission to read mail. In Microsoft Entra ID, by default, users can consent to an app accessing their own mailbox without an admin. The firm now has a third party reading privileged email for one user, and nobody in IT clicked anything.
The fix lives in Entra. In its guidance on configuring user consent, Microsoft recommends allowing user consent only for apps from verified publishers. You can also turn on the admin consent workflow, which lets people request apps instead of granting access themselves. Changing the setting doesn’t revoke grants that already exist, so review existing app permissions too.
Discovery documents in a consumer tool
A paralegal uploads a production set to a consumer AI tool to find every mention of a contract term. It works. It may also move documents covered by a protective order into a service nobody approved. Check what the order allows before any AI tool touches produced documents. Our guide on how law firms find and contain shadow AI walks through the checks.
Shadow AI in energy and oil and gas
Field teams are practical. An inspector takes photos of corrosion on a line, drops them into a consumer AI app on their phone, and asks for a write-up. A planner pastes equipment readings into a chat tool to find a pattern.
What leaves is operational detail like locations and asset conditions, sometimes contractor information too. Personal phones and contractor devices make this harder to see, because they may sit outside your device management. We covered the operational side in our post on shadow AI and pipeline safety.
Here the fix starts with a mobile option that’s approved and quick, and a short list of what never goes into any AI tool, approved or not.
Shadow AI in healthcare and medical device companies
In a clinic, the pattern is a transcription or note-summary app on a phone, used during or after patient visits. For a HIPAA covered entity, this one has a clear reference point. The HHS business associate FAQ lists “the vendor of an app, that provides transcription services to a physician” as an example of a business associate. A business associate handling PHI needs a business associate agreement before PHI goes to it. A free app someone downloaded doesn’t come with one.
Medical device companies see a quieter version. An engineer pastes test data or proprietary code into a public tool to troubleshoot it. Even without PHI, that can expose trade secrets and design history you’d rather keep in controlled systems.
Fix: an approved transcription option with a BAA where PHI is involved, and a clear line around patient data and design data.
Shadow AI at private equity portfolio companies
A finance lead at a portfolio company pastes sections of a board deck or a data room document into a chat tool to build a summary before a sponsor meeting. Multiply that by six portfolio companies, each with its own tools and its own idea of what’s allowed, and the sponsor has no single view of where deal information went.
What helps is a shared baseline: one short list of approved tools and data rules that every portfolio company adopts, with each company’s access kept separate.
The example that hides in plain sight: AI inside approved SaaS
This one is easy to miss. A product you’ve licensed for years adds an AI summary, a writing assistant or a search feature. Sometimes it’s on by default. The vendor passed your security review before the feature existed, so nobody checked where prompts go, or whether a model provider is now handling your data as a subprocessor.
No new app appears in a network log, because the traffic goes to a vendor you already allow. Add a standing question to vendor reviews and renewals: what AI features exist, what’s on by default, and under what data terms.
Pattern, risk and fix at a glance
| Pattern | Main risk | Practical fix |
|---|---|---|
| Personal AI account for client drafts | Confidentiality, records outside retention and legal hold | Approved assistant under commercial terms, plus a rule on client facts |
| AI notetaker on client calls | Third party holds privileged recordings and transcripts | Bot admission policy, firm-controlled recap settings, consent review |
| Email or browser extension with mail access | Ongoing third-party access to privileged mail | Entra user consent limits, admin consent workflow, review existing grants |
| Discovery documents in a consumer tool | Possible protective order breach | Approved tool, matter-level rules for produced documents |
| Field photos and readings in consumer apps | Operational and asset data exposure | Approved mobile option, short “never share” list |
| Transcription apps with patient notes | PHI disclosed without a BAA | Approved tool with a BAA |
| Deal documents pasted into chat tools | Confidential deal data across portcos | Shared baseline for approved tools and data rules |
| AI features inside approved SaaS | Unreviewed processing under old terms | AI questions in vendor review and renewal |
How to find these in your own environment
You don’t need new software to start. For Microsoft 365 organizations, the Microsoft Learn guidance on discovering AI apps is a good map. Defender for Cloud Apps has a Generative AI category in its app catalog, and you can mark apps sanctioned or unsanctioned. Purview DSPM for AI shows how sensitive data interacts with AI tools, though monitoring third-party AI sites requires onboarding devices to Purview. Check which licenses you hold before you plan around either.
Entra’s Enterprise apps list shows which AI tools people have consented to. Expense reports catch paid subscriptions that never show up in network logs. A short anonymous survey catches the rest, and people answer honestly when they trust the answers won’t be used against them.
Our shadow AI detection checklist walks through each check. If you’d like a second set of eyes on what you find, the free AI Readiness Snapshot is built for that.
Quick answers
What is an example of shadow AI?
A common example is an employee pasting client or patient information into a personal AI account to draft or summarize something faster. The tool may be fine for public content. It becomes shadow AI because nobody approved it for that data, and the organization doesn’t know what the provider keeps or for how long.
Are AI notetakers shadow AI?
They can be. An AI notetaker that an attendee connected without approval is shadow AI, and on a client or patient call it captures some of your most sensitive conversations. A notetaker your organization approved, configured and covered under reviewed terms is not. What decides it is approval and data terms.
Is using personal ChatGPT for work shadow AI?
If the work involves anything beyond public information and nobody approved the account for it, yes. A personal account runs under consumer terms, sits outside your retention and legal hold processes, and gives IT no visibility. Using a business or enterprise plan your organization approved is a different situation, with different terms and admin controls.
How common is shadow AI?
We don’t cite a percentage here, because surveys vary widely in method. The safer working assumption is that it’s present until your own discovery shows otherwise. Free tools are good, and AI features keep appearing inside software you already approved. Approved options can also lag behind what people want. Your own discovery data matters more than any industry figure.
Where to start
Pick the two patterns above that match your organization and check for them this week. At a law firm, I’d check personal accounts and mail-reading extensions first. At a clinic, start with transcription apps. The fixes are mostly approvals and settings, plus one or two clear rules. An approved tool people want to use does more than any block list.
If you want help sorting what you find, the AI Readiness Snapshot is free: a 60-minute working session, written findings and a 30-minute review of next steps. It looks at shadow AI and permissions together, because the approved tool inherits whatever your permissions allow. For the full picture, start with our shadow AI guide.
