AI and Agents on the Data Platform

Knowing which data your models and agents reach, deciding what each request should be allowed to return, catching the sessions nobody authorized, and controlling them — from inside the platform that holds the data.

The gap this closes

AI programs reach production faster than the controls around them. The question that arrives at the review — which of our data does this touch, and who is really behind the request? — is a data question, and most AI security tooling is not positioned to answer it.

• Tools above the data cannot see the data. A prompt filter inspects text. It cannot tell you whose data a request touched, how sensitive that data was, or whether the identity behind the request should have reached it. Those facts live in the platform, alongside classification, grants and lineage.
• Agents inherit human credentials. A coding assistant or analyst copilot connected with a person’s credentials looks exactly like that person to the platform. Every access review you run will confirm the person had permission, and none will notice that a model was driving.
• AI surfaces multiply faster than review cycles. An in-store engine switched on for a business unit, a new model-serving endpoint, an MCP server exposing a schema — each is a new path to the same data, created in an afternoon and inventoried at the next audit, if at all.

In one sentence

Theom runs inside your data platform, where classification, identity, lineage and policy already sit, so when a model or an agent reaches your data we already know the data, the identity and the policy — and can inventory it, alert on it, or evaluate the request inline and shape what comes back.