Private AI Strategy

Public vs. Private AI: A Decision Framework for Business Owners

A practical framework for choosing public, dedicated, or private AI across law, healthcare, trades, and retail workflows.

Ask three questions

What data is in the prompt? Who could see it if the vendor is wrong? What is the blast radius if it leaks?

  • Low sensitivity: public material may fit an approved external service.
  • Medium sensitivity: internal operations need reviewed contracts, settings, and access.
  • High sensitivity: privilege, PHI, customer PII, and pricing logic may require dedicated or private infrastructure.

Hybrid is normal

Most businesses will use public tools for low-risk work and private systems for crown jewels. The mistake is using one tool for everything.

Where EntAIngled Recommends Private Deployment

We design private AI systems for operators with strict data-custody requirements. Approved external tools can still support lower-risk work outside the sensitive core.

Classify one workflow

We can document its data, users, actions, and acceptable deployment boundary.

Choose a Deployment Path

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