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 | Non-sensitive, low-risk work | Regulated, privileged, non-public data |
| Capability | Frontier models | Frontier for safe work, local for sensitive |
| Main risk | Data leaving your control | Overbuilding if the line is drawn wrong |
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 only what data you put into them.
For non-sensitive work, drafting internal notes, summarizing public material, general research, public AI is the right tool and there is no reason to overbuild.
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 the information is regulated, privileged, or non-public and simply cannot leave.
The trade-off people assume, that private means weaker, is not real if it is built correctly. A model router lets you keep frontier capability for the safe work and use local inference for the sensitive work.
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.
Most regulated firms end up running both. Public AI for the bulk of low-risk work, private AI for the slice of data that cannot leave. The skill is drawing the line in the right place so you neither leak data nor overspend protecting things that do not need it.
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 many firms it is smaller than they feared, which means a focused private deployment plus clear policy covers it, rather than a wholesale rebuild.
Key takeaways
- Public AI sends your request to a third party. Private AI runs the model inside infrastructure you control.
- Public AI is fine for non-sensitive work. There is no reason to overbuild for it.
- Private AI is for data that is regulated, privileged, or non-public and cannot leave.
- The real line is the obligation on the data, not the document type.
- Most regulated firms run both, split by sensitivity, with a router enforcing the split.
Talk it through
Want help drawing the line for your firm? Start with a 30-minute call.