# What is private AI?

Private AI keeps your data off third-party servers — usually by running models locally or on infrastructure you control.

Private AI is AI where sensitive inputs — documents, code, health data, customer records — are not sent to a public cloud you do not control. NIST's Privacy Framework recommends minimizing data collection and limiting exposure as core practices; local inference is one technical way teams implement that.

That usually means one of: local inference on your laptop (Ollama, etc.), on-premise servers in your office, or a private cloud VPC with strict data residency — patterns described in Google Cloud's hybrid architecture guidance.

Private AI is not automatically more accurate — it is more controllable. Regulated industries adopt it to reduce leak risk and meet compliance (HIPAA privacy rules are a common driver in U.S. healthcare), not because local models always beat frontier cloud models.



## Sources
1. [NIST — Privacy Framework](https://www.nist.gov/privacy-framework)
2. [Ollama](https://ollama.com/)
3. [Google Cloud — hybrid and multicloud patterns](https://cloud.google.com/architecture/hybrid-and-multicloud-patterns-and-practices)
4. [HHS — HIPAA Privacy Rule](https://www.hhs.gov/hipaa/for-professionals/privacy/index.html)

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