Public AI, in short
Public AI tools send your prompts and data to a vendor's servers. They are fast to adopt and often the right choice for low-sensitivity work. The tradeoff is that your data leaves your environment and you rely on the vendor's controls.
What makes AI private
Private AI keeps your data inside a boundary you define. That can mean a private cloud account, a hybrid setup, or models running on your own hardware. The common thread is control over where data goes, who can reach the model, and how its use is recorded.
The pieces that matter
Three things make private AI real: a clear data boundary that defines what the model can see and where it lives, access control that decides who can ask and act, and a record of use so activity can be reviewed. Without these, private is only a label.
Where the model runs
You have options: cloud AI under your own account, private cloud, hybrid, or on-premise. Each trades cost, control, and operational effort differently. Private does not have to mean on-premise.
When this matters
- Your data is sensitive or regulated.
- You cannot send certain content to public tools.
- You need a record of who accessed what.
- A vendor's default terms do not match your risk tolerance.
What to avoid
- Assuming private always means on-premise.
- Treating private as a feature you buy rather than a design you make.
- Moving everything in-house before you understand the workload.
- Skipping access control and audit because the tool feels internal.