Data catalogs
Knowing what data exists is not the same as knowing what can reach it.
A data catalog gives data teams a picture of the estate — lineage, ownership, glossary, stewardship. What it was not built to model is the AI actor on the other end of a call, or what that actor and the person behind it are allowed to reach together — which is the part that actually decides whether a call is safe.
A data governance program needs all three answered.
Description, access, and evidence sound like the same thing from a distance. They are answered by different mechanisms, and a program that only has the first one cannot say who — or what — actually reached a given column.
Description answers: what data exists
Lineage, ownership, and glossary — the picture a catalog was built to maintain.
Access answers: what can reach it
Which role, together with which AI actor, is allowed to reach a given column — a question a catalog does not model.
Evidence answers: what actually did
A decision record exists because a call happened and was evaluated, not because a report was scheduled.
Six questions, asked the same way every time.
Every cell below states what that category actually does for that row — not a checkmark, not an X, and not left out to make the comparison look one-sided.
| Criterion | Data catalog | FaburAI |
|---|---|---|
| Discovery model | Governs what is cataloged — added by a crawler, a connector, or a manual entry. | Governs what is cataloged and what is observed calling in. An AI actor nobody registered still appears the moment it makes a call. |
| What it governs | The data estate itself: tables, columns, lineage, glossary, and ownership. | The relationship between AI actors and the data estate, down to which column a role and an agent may reach together. |
| When it acts | On ingestion or a scheduled crawl — a description, refreshed periodically. | Continuously, at the moment of every call an AI actor makes to that data. |
| What it produces | A glossary, a lineage graph, and a stewardship record for people to search and understand. | A decision record: the AI actor, the user role behind it, the column reached, the outcome, and the rule that decided. |
| Data residency | Typically the catalog vendor's own cloud platform. | Stays inside the customer's own environment and never leaves it. |
| Where enforcement happens | Nowhere — a catalog describes the estate. It does not decide or enforce access to it. | Inside the customer's environment, at the moment of the call, evaluated locally against policy already retrieved. |
One describes the estate. One decides against it, every call.
Laid out side by side, the gap is enforcement. A description of the estate does not stop a call from reaching it — only a decision, made at the moment of the call, does.
Tables, columns, and schemas discovered across the data estate.
Ownership, meaning, and provenance recorded for people to search.
A description someone can search — not a decision about a call.
An agent, acting for a user role, reaches for a specific field.
Against policy already in place, inside the customer's environment. Allow or deny.
Not once — again on the next call, and the one after that.
A catalog answers "what is this column." FaburAI answers "who, and what, reached it, and when" — the second question needs the first one answered first, which is why FaburAI's own catalog exists rather than asking every customer to build one from nothing.
FaburAI can connect to your data catalog.
It becomes one more observed source in the same estate graph as every other AI surface FaburAI governs — no rip-and-replace, and nothing about the catalog itself has to change.