Why corporates run AI on their own premises

Every AI vendor conversation eventually reaches the same sentence: “the data goes to our cloud.” For most workloads that is fine. For a holding group's general ledger — owner wealth, related-party flows, unannounced transactions — it is frequently the end of the conversation. The books cannot move to a new external tool, and no amount of certification logos changes that.

On-premise deployment answers a different question than cloud does. Not “is the vendor trustworthy?” but “does the data ever leave at all?” When the model, the analysis, and the books all run inside your own environment, the trust question shrinks to something an internal security team can actually inspect.

What on-premise actually means

The phrase gets stretched, so it is worth being literal. A real on-premise AI deployment means the source data stays in your environment, the deterministic analysis runs in your environment, and the model itself runs in your environment. Nothing phones home — no telemetry, no “anonymised usage data”, no round trip to an inference API for the hard parts.

If any one of those three leaves the boundary, you have a hybrid deployment and should describe it honestly. Hybrid can be a reasonable choice. Calling it on-premise is not.

When cloud is the right answer

Plenty of AI work belongs in the cloud: public-data workloads, low-stakes drafting, anything where the data is already outside your boundary. The test is not ideology. It is whether the people accountable for the data can defend its path — to an owner, an auditor, or a regulator — without taking a vendor's word for it.

For the workloads where they cannot, the deployment model is not a preference. It is a prerequisite.

Questions to ask any AI vendor

Where does inference run? Which sub-processors see the data? What leaves the boundary during normal operation — and during support incidents? Can the system operate with outbound network access disabled? Ask for the answers in writing, and treat vagueness as an answer too.