Private AI Isn't a Model Choice. It's an Information Boundary.
Most AI conversations focus on which model to use. The real question is: where does your data flow when you use it? Understanding information boundaries is the first step to controlled AI adoption.
Cabrillo Club
Editorial Team · December 24, 2025

The Model Conversation is a Distraction
When organizations discuss AI adoption, the conversation usually starts with: "Which model should we use? GPT-5? Claude? Grok? An open-source alternative?"
For proposal teams handling CUI, see our Compliant AI Proposal guide for boundary-aware AI architecture.
This is the wrong first question.
The model is a capability decision. The information boundary is a risk decision. One affects what you can do. The other affects what can go wrong.
What is an Information Boundary?
An information boundary defines where your data can flow. It includes:
- Your infrastructure - servers, cloud accounts, and storage you control
- Authorized third-party services - explicitly approved external systems
- The controls governing movement - policies, logging, and access restrictions
For regulated organizations, this boundary often needs to align with compliance frameworks. NIST 800-171 3.1.3 requires organizations to "control the flow of CUI in accordance with approved authorizations." You can't control what you don't see.
The Shadow AI Problem
Here's what most executives don't know: AI is already inside their organization. It arrived through:
- Browser extensions that send text to external APIs
- Personal ChatGPT accounts used for work tasks
- SaaS tools that quietly added "AI features" using external inference
- Developers testing code against public AI services
Each of these represents an uncontrolled information flow. Data leaves your boundary. You have no audit trail. Compliance becomes a question mark.
Why "Private AI" Isn't Just On-Premise
Private AI is often confused with on-premise deployment. They're related but not identical.
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Editorial Team
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