What is Shadow AI?
Shadow AI refers to the use of artificial intelligence applications and services by employees without the knowledge, approval, or oversight of their organization’s IT or security teams. Just as “shadow IT” described the unauthorized use of cloud applications a decade ago, shadow AI is the same phenomenon applied to the rapid proliferation of AI tools — ChatGPT, Claude, Copilot, Midjourney, and hundreds of others. When an employee connects an AI application to organizational data through an OAuth grant, API key, or browser extension, they create a data access path that security teams cannot see, audit, or control.
Why is Shadow AI a Security Risk?
Shadow AI introduces a category of risk that traditional security tools were not designed to handle. The core problem is data exposure: when an employee authorizes an AI application to access their work account, that application may gain read access to emails, documents, calendar events, and shared drives — often with broader permissions than the employee realizes.
The specific risks include:
Uncontrolled data access — AI apps authorized via OAuth often request broad scopes. An employee connecting a meeting summarizer might inadvertently grant it access to their entire calendar and email history, including confidential discussions.
Data residency violations — Many AI services process data outside the jurisdictions your organization has agreed to with customers. If an employee in the EU sends customer data through a US-based AI service, this may violate GDPR data transfer requirements.
Intellectual property leakage — Code assistants, document summarizers, and AI writing tools process organizational content. Without governance, proprietary code, strategic documents, and customer data flow through third-party AI models with no visibility or control.
Compliance gaps — Regulated industries (healthcare, financial services, government) have strict requirements about where data is processed and by whom. Shadow AI creates compliance violations that surface during audits, often retroactively.
Supply chain risk — Each AI application is a third-party dependency. If an AI vendor is breached, every organization whose employees authorized that app is potentially affected — and without a shadow AI inventory, your security team won’t know which vendors to worry about.
According to the Salesforce 2025 IT Trends Report, a significant percentage of employees use AI applications without IT approval. This isn’t malicious — employees adopt AI tools because they’re productive. The risk comes from the lack of visibility and governance.
How Does Shadow AI Enter Your Organization?
Shadow AI typically enters through three vectors:
OAuth Grants
The most common path. An employee visits an AI tool’s website and clicks “Sign in with Google” or “Sign in with Microsoft.” This creates an OAuth token that grants the AI application access to the employee’s workspace data. OAuth grants are especially dangerous because they persist until explicitly revoked — the AI app retains access even after the employee stops using it.
API Keys and Tokens
Developers and technical employees may create API keys to connect AI services to internal tools, databases, or repositories. These keys are often created in personal accounts, stored in code repositories, and forgotten — creating long-lived access paths that bypass all access controls.
Browser Extensions and Plugins
AI-powered browser extensions (grammar checkers, writing assistants, code helpers) run in the context of the employee’s browser session. They can read page content, intercept form data, and access any web application the employee uses — including internal dashboards and admin panels.
In all three cases, the AI application gains access through the employee’s existing credentials and permissions. No firewall is crossed, no alarm is triggered, and no IT ticket is filed. The access is invisible to traditional security monitoring.
How to Detect Shadow AI
Detecting shadow AI requires visibility into the authentication and authorization layer of your SaaS applications — specifically, which third-party applications have been granted access by your employees. There are several approaches:
API-Based Discovery (Most Effective)
Connect to your SaaS applications via API and query their OAuth grant and integration logs directly. This is what SSPM platforms do — they read the list of authorized third-party applications from each connected SaaS app and flag the ones that are AI-related.
Black Cat SSPM monitors OAuth grants, API connections, and integration logs across all connected SaaS apps to discover AI applications — including those not approved by IT. The platform identifies which AI apps have access, who authorized them, what data they can reach, and when the authorization was granted.
Identity Provider Logs
If your organization uses a centralized identity provider (Okta, Azure AD, Google Workspace), you can audit OAuth consent grants through admin APIs. This gives partial visibility but misses AI apps authorized through direct login (email + password) rather than SSO.
Network Traffic Analysis
CASBs and secure web gateways can detect traffic to known AI service domains. This reveals usage patterns but doesn’t show what data permissions were granted or which organizational data the AI app can access.
Employee Surveys
The least technical but surprisingly effective approach for initial inventory. Ask teams what AI tools they use. Many employees will voluntarily disclose — they adopted the tools to be productive, not to circumvent security.
Shadow AI Governance Best Practices
Effective shadow AI governance balances security with productivity. Blocking all AI tools frustrates employees and drives usage further underground. The goal is visibility and risk-based decision making.
Build an AI App Inventory
Start by discovering every AI application your employees have authorized. Classify each one by risk level based on the data it can access, where it processes data, and whether it has a security program (SOC 2 report, DPA, data processing agreement).
Establish an Approval Process
Create a lightweight process for employees to request new AI tools. If approval takes 48 hours instead of 4 weeks, employees will use it. The process should evaluate data access scope, data residency, vendor security posture, and compliance impact.
Monitor Continuously
Shadow AI is not a one-time inventory problem. New AI tools launch weekly, and employees authorize new ones constantly. Use continuous monitoring to detect new AI app authorizations as they happen, not during your next quarterly audit.
Set Data Access Boundaries
Work with your identity provider to limit the OAuth scopes that employees can grant to third-party applications. Restrict broad scopes (read all mail, access all files) to applications that have been explicitly approved.
Revoke Stale Authorizations
AI apps that employees tried once and forgot still have active access tokens. Regularly review and revoke OAuth grants for AI applications that haven’t been used in 30+ days.
Educate, Don’t Just Block
Help employees understand why shadow AI governance matters. Most adopt AI tools to be more productive — acknowledge that, and channel the energy toward approved alternatives that meet security requirements.
Shadow AI is one of the fastest-growing security challenges facing organizations today. With the right visibility and governance approach, you can enable your team to use AI productively while keeping your data secure. Start a free trial of Black Cat SSPM to discover every AI app in your SaaS estate in under 5 minutes.