Shadow AI
Shadow AI: find it, then govern it
Shadow AI is not a product category — it is the gap between the AI an organization approved and the AI it actually runs. It grows because adoption is easy and individual: no procurement, no project, no ticket. Closing it is four steps, and Vayon AI publishes what each one can and cannot establish.
Three ways it arrives
People
Employees adopt public AI assistants for real work — drafting, summarizing, analyzing a file. The account is personal, the data is organizational.
Developers
Coding tools are connected straight to a model provider, with their own keys and their own billing, outside any gateway the organization operates.
Applications
Business systems ship their own embedded AI integrations, enabled by a vendor update rather than by a decision anyone recorded.
Four steps, in order
See it
Build an organization-wide view of AI usage from telemetry your existing security and IT systems already produce — read over their own APIs, with no additional Vayon AI endpoint agent for this path. Observations from every source are normalized and correlated, so one AI service seen from a firewall log, an identity system and an endpoint signal is recognized as one entry rather than three.
Understand it
Visibility says where AI is used; it does not say what for. Activity Intelligence connects observed activity to business use cases and shows where observed behavior differs from what was declared — which is usually the finding that changes a decision.
Govern it
A reviewed, approved AI service moves into Vayon AI's governed access surfaces and Gateway, where identity, policy and sensitivity are settled before a model runs. Nothing is promoted silently — approval is a decision someone makes.
Keep the evidence
What was decided, for whom, and on what basis is recorded, so the answer to an auditor's question is a record rather than a reconstruction.
What this page does not claim
Discovery surfaces observations for review; it does not silently promote an uncertain observation into an approved organizational asset. Which discovery sources are available in a given deployment depends on what the organization configures and licenses, and no partnership with any named system is implied. Where a source cannot prove attribution, activity stays unattributed. Optional browser and endpoint enforcement is a separate, complementary capability, not part of the agentless discovery model described here.
Frequently asked questions
What is shadow AI?
AI that reaches the organization outside any inventory it maintains. It is rarely a policy breach and almost never malicious: an employee uses a public assistant, a developer connects a coding tool straight to a model provider, a business application ships its own AI integration. Each path creates its own identities, accounts, costs and risk, and none of them appears on a list anyone owns.
Do I have to install an agent on every endpoint to find it?
Not for this model. Vayon AI builds the picture from telemetry the security and identity systems you already operate produce — SIEM, CASB, EDR, MDM and IAM among them — read over their own APIs. Which sources are available in a given deployment depends on what the organization configures and licenses. Managed browser extensions are a separate, complementary channel for real-time governance of web-based AI use, not part of this one.
Does discovering a service block it?
No. Discovery surfaces an observation for review; it does not silently promote an uncertain observation into an approved organizational asset, and it does not decide on its own that something should stop. A service someone reviews and approves moves into Vayon AI's governed access surfaces and Gateway, under the same identity, policy and audit model as every other request.
Can you tell me who used it?
Where a connected source provides trustworthy identity or ownership evidence, an observation is associated with a user, device, application or organizational owner. Where the source cannot prove it, the activity stays unattributed rather than being given an owner it cannot support — an unattributed total is more useful than a confident wrong name.