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.