Can I use AI on my internal company data without it leaking?

Yes, with a retrieval-based, permission-aware setup. Keep the data in systems you control, retrieve only the few relevant pieces at query time, and pass the requesting user's identity into the retrieval so the model only sees what that user could already open. Add logging of which documents fed which answer. The model then acts as a reasoning layer over data that never leaves your control.

Is self-hosting the only way to get private AI?

No, and for most companies it is overkill. A commercial API on a business or enterprise tier with contractual no-training terms, plus region pinning through your own cloud account, gives strong privacy without the burden of running models. Self-hosting open weights makes sense when you have data that legally cannot transit a third party at all. Even then, self-hosting only addresses the vendor; you still need internal access control to prevent over-sharing inside your own company.

Does private AI mean my data never goes to the model provider?

Not necessarily. With a contracted commercial API, your prompts transit the provider but are not used for training and are deleted after a short window. That is private in the sense that matters for most businesses. If you have data that legally cannot leave your environment at all, you move to single-tenant cloud or self-hosted open weights, where the data stays inside your boundary. The right answer depends on your specific legal and contractual constraints.

Private AI for Business: Keeping Your Data Yours

Q: What is private AI for business?

Private AI is AI used on your own data in a way that keeps that data under your control: not used to train a vendor's model, kept in a region and tenancy you choose, and only ever operating on data you have authorized. It ranges from a commercial API under no-training contract terms, through single-tenant cloud deployments, to fully self-hosted open-weight models. The right level depends on the sensitivity of the data, so you match it to what each use case actually requires.

What is private AI for business?

Private AI is AI where you keep control of the data going in and the outputs coming back. Three things make it private: your inputs are not used to train the vendor's model, the data stays in a region and tenancy you control, and the system only operates on data you have authorized.

There is a real spectrum here, and naming it stops people overspending:

Commercial API, private terms. You call Claude or another model through a business or enterprise agreement that disables training and limits retention. Your data transits the vendor but is not learned from or kept.
Single-tenant cloud. You run the model through Azure OpenAI, AWS Bedrock, or Google Vertex inside your own cloud account, with the data pinned to a region. The model weights are managed; the data stays in your boundary.
Self-hosted open weights. You run an open-weight model (Llama, Mistral, Qwen) on infrastructure you own. Nothing leaves your network, and the capability you give up to get there has shrunk: Stanford's 2025 AI Index found the gap between the best open-weight and closed models on the Chatbot Arena leaderboard fell from 8.0% to 1.7% over 2024.

Privacy increases left to right; so does cost and operational burden. The art is matching the tier to the sensitivity of the data, so you reserve the most locked-down option for the cases that need it.

How does private corporate AI keep data from leaking?

Three mechanisms do most of the work, and they stack.

Contract first. The cheapest privacy control is the data processing agreement. On business and enterprise tiers, vendors commit in writing that your prompts and outputs are not used for training and are retained only briefly. This alone moves you off the consumer default where your data may be used to improve the product.

Boundary second. Deploying through your own cloud account (Bedrock, Vertex, Azure) means the data never leaves your tenancy and never crosses into a shared environment. You pick the region, and you can prove it.

Scope third. Even with perfect contracts, an assistant that can read your whole SharePoint is a leak waiting to happen internally. Private corporate AI scopes retrieval to the requesting user and logs every access, so the model is private from the vendor and from the rest of your own company too.

How do you use AI with internal company data safely?

The useful version of private AI connects the model to your real systems: the CRM, the knowledge base, the ticketing system, the data warehouse. That is where the value is, and where the risk is.

The safe pattern is retrieval-augmented, permission-aware, and logged:

Keep your data in your systems. Don't dump everything into a third party. Retrieve the few relevant chunks at query time from a store you control (a vector database on your own infrastructure, or your existing systems like Supabase, NetSuite, or HubSpot). This is the same retrieve-from-your-own-data-layer pattern that makes an assistant useful without copying the corpus out.
Pass the user's identity into every query. The retrieval layer filters to documents that user is already allowed to see, so internal access rules carry over to the AI.
Send the model only what it needs. The model sees only the few retrieved snippets needed for that one answer.
Log the lineage. Record which documents fed which answer, so you can audit and explain any output later.

Done this way, the model is a reasoning layer over data that never leaves your control.

Where does private AI fall short?

The trap is treating self-hosting as automatic privacy. Running an open-weight model on your own servers does keep prompts off a vendor's API. It does nothing about the internal leak where the model surfaces documents one employee should never see another's. Most real privacy incidents are internal over-sharing.

The second shortfall is operational. A self-hosted model someone stood up on a single unpatched GPU box, with no access logs and no rotation of who can reach it, is often less private in practice than a contracted API from a vendor with SOC 2 controls. Privacy is the weakest link in the chain, and the chain includes your own ops discipline. Decide what data sensitivity you actually have before you take on the burden of running models yourself; for many companies the contracted, region-pinned API is both more private and better operated than what they could self-host on day one. This is the same build-versus-buy judgment you make on any internal system, and the free AI project cost calculator puts numbers on the self-hosting side before you commit.