Shadow AI: Finding and Managing the Unsanctioned AI Tools Already Inside Your Company

Ask a CIO how many AI tools their organization uses, and they’ll likely name a few approved platforms. Ask their security team to actually measure it, and the number is usually far higher. Browser extensions that rewrite emails, meeting bots that record and transcribe calls, AI features quietly switched on inside existing SaaS applications, coding assistants installed by individual developers and free tools signed up for with corporate email addresses all add up quickly.

This is shadow AI: artificial intelligence tools used without IT or security approval. Like shadow IT before it, it usually comes from good intentions. Employees want to work faster. But it creates real risks that security leaders can’t manage until they can see them.

Why shadow AI is harder to spot

It hides inside approved tools. Many SaaS vendors have added AI features to products you already use, sometimes enabled by default and sometimes powered by third-party model providers your contracts don’t cover.

It lives in the browser. Extensions and web apps don’t require installation privileges, so traditional software inventories miss them.

It joins your meetings. AI note-takers can join calls automatically once one participant connects them, recording conversations with customers, partners and colleagues who never agreed.

It connects to your data. Many tools request OAuth access to email, calendars, files or code repositories, often with broad permissions granted by a single click.

Step 1: Discover what’s in use

Build visibility using several sources, since no single tool sees everything:

• Network and secure web gateway logs reveal traffic to AI services
• SaaS security and CASB tools identify AI applications and AI features within approved apps
• OAuth and identity provider reviews show which AI tools have been granted access to corporate accounts and data
• Browser management inventories installed extensions
• Expense reports surface paid subscriptions bought on corporate cards
• Employee surveys uncover tools and use cases that technical monitoring misses

Step 2: Assess the risk

Evaluate each tool on what data it accesses, where that data goes, whether the vendor trains models on customer data, its security certifications, data retention practices and contractual terms. Prioritize tools with broad data access or high usage.

Step 3: Decide and communicate

Sort tools into clear categories: approved, approved with restrictions, under review and prohibited. Publish the list so employees know what they can use. When you prohibit a tool, offer an approved alternative whenever possible.

Step 4: Build a fast intake process

If getting an AI tool approved takes three months, employees will go around the process. Create a lightweight review for lower-risk tools and a clear path to request new ones, with decisions measured in days rather than months.

Step 5: Govern continuously

Shadow AI isn’t a one-time cleanup. Monitor for new tools, review OAuth grants regularly, revoke unused or risky integrations, and update the approved list as the market changes. Configure AI features in approved SaaS platforms deliberately rather than accepting vendor defaults.

Make governance a partnership

The employees using shadow AI are often your most innovative people. Involve them in evaluating tools and sharing use cases. A governance program that enables safe experimentation will uncover more shadow AI than one built on punishment.

The bottom line

You can’t secure what you can’t see. Organizations that discover their AI footprint, assess risk, offer approved alternatives and make approval fast will turn shadow AI from an unmanaged risk into a governed source of productivity.