How to Use AI Without Putting Client Data in a Public Tool
Regulated firms do not have to choose between banning AI and leaking client data. Here is the third path: running the model inside your own environment so sensitive data never leaves.
Most leaders at regulated firms think they have two options with AI. Ban it and watch the work get slower than competitors who use it, or allow it and hope nobody pastes the wrong document into a public chatbot. Both options are bad, and neither is necessary.
There is a third path. You keep the data and bring the model to it, instead of sending the data out to the model.
Why pasting into a public tool is the actual problem
When an employee drops a client document into a public AI tool, that text leaves your control. For a firm handling regulated, privileged, or non-public information, that single action can breach a client commitment, a protective order, or a regulatory obligation. The employee was not being careless. They were trying to get work done faster, and the tool made it easy.
The risk is not AI itself. The risk is the data leaving. Once you frame it that way, the solution gets clear: keep the data inside your environment.
The third path: private AI with a router
A private AI deployment runs a model inside infrastructure you control, your own cloud tenant or your own hardware. In front of it sits a model router. Every request passes through that one place, and policy decides where it can go.
Sensitive requests are answered by the local model and never leave. Non-sensitive work still goes to a frontier model like Claude, so you are not stuck with a weaker tool for everything just because some of your data is sensitive. The decision is made by policy, request by request, not by hoping an employee gets it right.
What this looks like in practice
For a financial services firm handling non-public client data, we stood up a local large-language-model deployment on dedicated GPU hardware. The router sends only safe requests to a frontier API and keeps everything sensitive on the local model. The firm gets modern AI capability, and non-public information never leaves their environment.
The architecture is its own compliance argument. If regulated data physically cannot reach a public API, most of the hardest questions answer themselves.
Where to start
You do not need to buy hardware on day one. The first step is mapping which of your data is actually sensitive and which is not, because that map is what the router enforces. Some firms run local models on dedicated GPU hardware. Others keep everything inside their own cloud control plane. The right architecture depends on your data, your budget, and your obligations.
Key takeaways
- The risk is not AI. It is client data leaving your control through a public tool.
- Private AI runs the model inside your own environment so sensitive data never leaves.
- A model router decides, request by request, what goes to a frontier model and what stays local.
- You keep frontier capability for non-sensitive work instead of downgrading everything.
- The architecture is its own compliance argument: data that cannot reach a public API answers most hard questions.
Talk it through
Wondering whether private AI fits your firm? Start with a 30-minute call.