Every AI tool, agent, extension, and MCP server across browsers, endpoints, network, and cloud. No agents to install.
Shadow AI is not one problem in one place. It arrives by four independent routes, each invisible to a different part of the security stack. A tool that watches one route reports a clean environment while the other three run unobserved.
A chat tool opened in a tab, an extension added in two clicks, a personal account signed in on a corporate profile. None of it needs an install privilege or a purchase order, so none of it reaches a procurement list. Verizon found the average company had more than 15% of users with unauthorized AI extensions installed on their browsers.
Desktop AI applications, coding assistants, and locally installed MCP servers sit below the browser and outside SaaS discovery entirely. There is no tenant, no seat count, and no renewal date to find them by. They are installed by the people most trusted to install things, which is why they are reviewed least.
Traffic to model endpoints reveals AI resources with no application, no listing, and no owner. It is the only surface that finds a resource not attached to a person, which is how an unknown MCP server becomes visible before anyone has classified it.
Model services stood up inside your own cloud accounts are approved the moment they exist and inventoried by nobody. Any shadow AI programme that defines shadow as outside our estate never looks here.
Forty seven percent of security professionals say they lack visibility into the shadow AI tools employees are using today, and a second, unrelated survey lands on the same figure. That measures the gap people can already see. The four surfaces above are where it lives.
AIBound builds a live catalog of every AI resource across your fleet. No agents, no network taps, no disruption, and a full inventory in under 24 hours.
| Resource | Surface | Users | Status |
|---|---|---|---|
| ChatGPT (personal) | Browser | 184 | Pending Review |
| Claude Desktop | Endpoint | 42 | Approved |
| Cursor + MCP | Endpoint | 67 | Pending Review |
| Perplexity Ext. | Browser | 231 | Pending Review |
| Azure OpenAI | Cloud | 12 | Approved |
| Unknown MCP srv | Network | 3 | Blocked |
AIBound deploys read-only and agentless. That is why the inventory is complete in under 24 hours rather than in the quarter it takes to negotiate an endpoint rollout. There is no binary to package, no change window to book, and no coverage gap while deployment catches up with the fleet.
247 days
Average time to identify and contain a breach, up 2.5% and reversing a five year decline. IBM Cost of a Data Breach Report 2026.
The median enterprise is not slow at AI governance because it lacks intent. It is slow because the instrument that would produce the evidence takes longer to deploy than the problem takes to grow. An inventory that takes two quarters to assemble is not an inventory. It is a snapshot of a fleet that has already moved.
Discovery runs continuously rather than as a periodic scan, so a tool installed on a Tuesday enters the inventory rather than next quarter's report. Every resource lands with an owner and a status, which makes day one a working queue rather than a document. That is the handoff into approval workflow, and into risk scoring.
An AI resource is anything in your environment that reaches a model, including AI applications, browser extensions, agents, models, and MCP servers. AIBound inventories each as a first class entry rather than as an attribute of something else. Every entry carries the surface it was found on, a user count, an owner, and a status, which is what makes the inventory a queue instead of a report.
The network surface finds AI resources that have no client application, no software listing, and nobody named against them, because it observes traffic to model endpoints rather than software on a device. It is the only one of the four that can find a resource which is not attached to a person. That is how an unregistered MCP server becomes visible before anyone has classified it.
SaaS discovery finds sanctioned applications, meaning applications with a tenant, seats, and an invoice. AI discovery has to find things with none of those: an extension installed by one user, a locally run MCP server, a personal account on a corporate device, or a model service in your own cloud. Verizon found the average company had more than 15% of users with unauthorized AI extensions installed on their browsers.
A full AI inventory is available in under 24 hours, because deployment is agentless and read-only. There is no software to package, no endpoint rollout to schedule, and no change window to book before discovery starts returning results.
It stays current. AIBound runs continuous discovery rather than periodic scans, so a resource that appears on a Tuesday enters the inventory instead of waiting for the next scan window. That difference matters more for AI than for software generally, because the population of AI tools inside an enterprise changes faster than a quarterly scan cycle can describe it.
Grades are mapped to the EU AI Act and to the NIST AI Risk Management Framework. Mapping means a grade can be reported against the structure those frameworks use. It is not certification. The NIST AI Risk Management Framework, document NIST AI 100-1, is published by NIST in its own words as "intended for voluntary use" and operates no certification or attestation scheme, so "mapped to" is accurate and "attested against" would not be.
This gives the level of detail I'm looking for… we don't have that at the moment.
Start with a complete, live AI inventory, no agents required.