No silent global learning
Private project data is not treated as public model-training material.
Customer-controlled data and runtime boundaries
Private Industrial AI
Private AI keeps customer workflow data, retrieval and model execution inside an agreed boundary. Data does not automatically contribute to shared training, and sensitive tasks can use self-hosted runtimes.
StatusEnterprise deployment direction
Reviewed2026-08-20
Direct answer
Private AI keeps customer workflow data, retrieval and model execution inside an agreed boundary. Data does not automatically contribute to shared training, and sensitive tasks can use self-hosted runtimes.
Design boundary
What the system will not pretend to be.
No permission bypass through retrieval
Vector or graph matches remain subject to document access.
No one-size hosting
Runtime selection follows customer security, residency and operational needs.
01Data boundary
Customer data remains purpose-limited.
Tenant, project and document permissions control storage, retrieval, training eligibility and execution context.
02Training boundary
Contribution requires explicit choice.
A customer can select private use, governed shared contribution or a defined licensed dataset arrangement.
03Runtime boundary
Sensitive tasks can remain self-hosted.
Interoperability allows security and residency requirements to filter the available models and runtimes.
Operating matrix
Evidence moves through explicit controls.
| Subject | Input | Intelligence operation | Human / policy control | Output |
|---|---|---|---|---|
| Private | Customer workflow data | Customer-scoped retrieval/training | Tenant isolation | Private intelligence |
| Contribute | Explicitly selected episodes | Governed shared training | Purpose and consent record | Shared improvement |
| Licensed | Defined dataset rights | Contracted use | License and revocation terms | Authorized dataset |