Shadow AI is the use of AI tools, or AI features inside other software, for work without the knowledge or approval of IT, security or whoever is responsible for the data involved. It covers employees using personal accounts on public AI chat tools, teams signing up for AI apps on a credit card, and AI features switched on inside existing SaaS products. It is a newer form of shadow IT, with a sharper data problem: people tend to paste the very information you most need to protect into the prompt.
At a glance
- Shadow AI is common because AI tools are cheap or free, easy to sign up for and useful.
- The main risk is data: confidential, customer or regulated information sent to a provider under terms you haven’t reviewed.
- Other risks include unchecked AI output used in decisions or customer communication, and untracked spend.
- Discovery tools can find many AI apps in use, but personal devices and embedded AI features are harder to see.
- Most organizations manage it with approved alternatives, clear policy and monitoring rather than blanket bans.
What problem it solves
Shadow AI is a problem, not a solution; the term names a risk buyers need to manage. People adopt AI tools because they save time on writing, summarizing, coding and analysis. When the company hasn’t provided an approved option, or the approved one is slow to arrive, staff find their own.
The consequences show up in several places. Sensitive data may be stored, logged or, depending on the provider’s terms, used to improve its models. Contract and privacy commitments to customers may be broken without anyone knowing. AI-generated output, which can be confidently wrong, may reach customers or decisions without review. And spending scatters across expense reports. Naming the problem lets leadership move from “are people using AI?” (they almost certainly are) to “which uses do we allow, and how do we see the rest?”
How it works
Shadow AI usually enters through a few routes:
- Public AI chat tools used through a browser with personal or free accounts.
- Standalone AI apps for transcription, writing, design or coding, signed up for by individuals or teams.
- AI features inside approved software, such as meeting summaries or writing aids, enabled by default or by a local admin without a review.
- Browser extensions and plugins that read page content and send it to an AI service.
- Connections to company data, where an AI tool is granted access to email, files or a CRM through an OAuth approval.
Managing it typically combines discovery (web filtering logs, a cloud access security broker (CASB), SaaS management platforms, expense review), data controls such as data loss prevention (DLP) that can detect and block many sensitive uploads, review of third-party app permissions, and an approved set of tools that staff actually want to use.
When it matters for buyers
- When leadership or the board asks about AI risk. A discovery exercise gives you facts instead of guesses.
- When you handle regulated or contractually protected data. Health, financial, personal or customer-confidential data sent to an unreviewed provider can create compliance issues; check obligations with counsel.
- When writing AI governance policy. Policy without visibility and approved alternatives tends to be ignored.
- When selecting an approved AI assistant. Giving staff a sanctioned tool with acceptable data terms is often the most effective control.
- When renewing security tools. Ask how your CASB, secure web gateway or DLP handles AI apps; our cloud access security broker overview covers the category.
Questions to ask vendors
For security and SaaS management vendors:
- How do you identify AI apps and AI features inside other SaaS apps, and how current is that catalog?
- Can you inspect and block sensitive data in prompts and file uploads, and on which devices and browsers?
- Can you see AI tools connected to our email, files or CRM through third-party permissions?
For AI tool providers:
- Is our data used to train or improve your models, and can we opt out by contract?
- Where is data stored and processed, how long is it kept, and who can access it?
- Do you support single sign-on, admin controls and audit logs on the plan we’d buy?
How it differs from shadow IT
Shadow IT is any technology used without IT approval: a file-sharing app, a project tool, a personal router. Shadow AI is the AI-specific subset. The controls overlap, but shadow AI deserves separate attention because the tool’s whole purpose is to process the information users give it, because AI features can appear inside software you already approved, and because output quality becomes a risk in its own right. Teams that already run a shadow IT program can usually extend it rather than start over.