Private AI vs Public AI: What Regulated Firms Need to Know
Public AI tools and private AI deployments solve different problems. Here is how to tell which one a regulated firm actually needs, and where the line really sits.
The phrase private AI gets used loosely, often by vendors who want to sell you something expensive. For a regulated firm, the distinction is not about prestige. It is about where your data goes and who controls the model answering your requests.
At a glance: public AI vs private AI
| Public AI | Private AI | |
|---|---|---|
| Where the model runs | Third-party servers | Inside infrastructure you control |
| Where your data goes | To the provider | Stays in your environment |
| Right for | Most work, on the right plan and terms | The rare slice of data no third party may process |
| Capability | Frontier models | Models you run yourself, usually behind the frontier |
| Main risk | Data leaving your control on unreviewed terms | Overbuilding, plus real operations burden |
What public AI actually is
Public AI means the model is run by a third party and your request travels to them. ChatGPT, Claude through the consumer apps, AI features baked into the software you already pay for. These tools are genuinely capable, and for a great deal of work they are completely fine. The question is what data you put into them, and on what plan.
The plan matters as much as the tool. A consumer account gives you no agreement, no admin visibility, and no audit trail. An enterprise-grade plan on the same underlying models gives the firm contractual no-training commitments, retention and access controls, and logs. For a regulated firm, that difference is most of the decision.
What private AI actually is
Private AI means the model runs inside infrastructure you control. Your own cloud tenant, or your own hardware. The data does not travel to a third party because the model is already where the data lives. This is what a firm reaches for when information genuinely cannot be processed by any outside provider, under any terms.
The trade-offs are real. Models you run yourself trail the frontier tools on the hardest work, someone has to operate and patch the infrastructure, and hybrid designs that route requests by sensitivity add engineering burden on top. A full private deployment is a deliberate decision for a narrow set of obligations, not a default.
Where the line really sits
The line is not the document type. It is the obligation attached to the data. The same kind of file can be fine in a public tool in one matter and strictly off-limits in another, depending on whether a protective order, a court order, or a client commitment governs it. This is why a serious AI policy classifies data by authorization, not just by what kind of document it is.
For most regulated firms, the right first move is not a private build at all. It is an enterprise-grade plan with contractual no-training commitments and retention and access controls, wrapped in real governance: a data classification keyed to obligation, an approved-tools list, human review, and training. That covers the bulk of the work, and the truly restricted material stays out of AI tools entirely until a specific tool is approved for it. Full private deployment is a heavy lift that very few firms actually need.
How to decide without overbuilding
Start with an honest inventory of what data your team actually feeds into AI, and what obligation each category carries. That inventory tells you how big the sensitive slice really is. For most firms it is small enough that an enterprise plan with the right contractual controls, plus a policy that keeps the truly restricted material out of AI tools, covers it without building anything. If the inventory turns up data that genuinely cannot be processed by any third party, that is when a private deployment earns a serious look, and only for that slice.
Key takeaways
- Public AI sends your request to a third party. Private AI runs the model inside infrastructure you control.
- The plan matters as much as the tool: enterprise terms carry no-training commitments and retention controls consumer accounts do not.
- For most regulated firms, the right first move is an enterprise-grade plan plus governance, not a private build.
- The real line is the obligation on the data, not the document type.
- Full private deployment is a heavy lift reserved for the rare slice of data no third party may process.
Talk it through
Want help drawing the line for your firm? Start with a 45-minute call.