Shadow AI Detection

Shadow AI is the use of AI applications, models, agents, integrations, or services without an organization's required visibility, approval, or governance controls. It includes personal ChatGPT/Claude/Gemini accounts, browser extensions, desktop copilots, embedded AI features inside approved SaaS tools, direct API calls, and AI agents or MCP-connected tools created outside approved workflows.

Effective Shadow AI detection combines signals from multiple layers - network/DNS, browser, endpoint, identity, SaaS, and the data actually submitted to a model - to identify unauthorized AI use. Each layer sees a different slice of the problem, so the right combination depends on your architecture and threat model.

A firewall or DNS control can flag connections to known AI destinations, but it typically can't tell you which user was involved, which account they used, or what data they submitted. Network visibility answers "where is AI traffic going" - it's strongest combined with browser, identity, and data-layer context.

The key question to ask any vendor is whether their detection architecture carries identity context alongside the AI destination - not just the domain. The same AI service can be low-risk under a managed corporate account and high-risk under an unmanaged personal one, so this distinction is a core evaluation criterion for enterprise Shadow AI programs.

Not usually. A blanket block tends to push employees toward other tools or unmanaged workarounds rather than eliminating the underlying need. A policy engine that can allow, monitor, protect (mask sensitive data), restrict, block, or redirect - decision by decision - keeps legitimate AI use flowing while still closing the actual risk.

Yes. AI browser extensions can read the contents of whatever page or document is open, and desktop AI applications run outside the browser entirely - both are common Shadow AI surfaces that a domain-only blocklist misses.

Increasingly, yes. Unauthorized agents introduce their own model calls, credentials, tool execution, and data connections. Once an agent can act through APIs and MCP-connected tools without a human initiating every step, Shadow AI stops being just a chatbot problem and becomes an identity and permissions problem too.

Discovery answers "what AI exists in my organization" and builds the inventory. Detection answers "what AI is being used right now" and enables real-time enforcement. Governance answers "what should happen when someone uses it" - connecting discovery and detection to policy: discover, detect, classify, decide, enforce, and audit.

WalledAI recognizes AI destinations at the browser and network layer it already governs for sanctioned tools, so no separate endpoint agent is needed to identify unsanctioned AI usage. Policy decides in real time whether to allow, protect, restrict, block, or redirect the interaction.

Yes. Rather than only blocking, WalledAI can classify the sensitive information in an interaction and have Walled Redact mask it before it reaches an external model - so the employee's work continues and the model gets the context it needs without the underlying sensitive values.

Yes. Every detected attempt - allowed, protected, restricted, blocked, or redirected - is logged with the user, AI destination, account type, data involved, and outcome, alongside your existing governance records, so it's part of the same evidence trail used for compliance reporting.