Private GPT

Private GPT is what generative AI looks like when governance is a starting constraint instead of a remediation project. It keeps the capability inside the perimeter and the audit trail intact.

Problem

The default path to generative AI runs through a public endpoint, and for many organizations that path is closed. Sending proprietary or regulated data to a third party is not a risk to be managed - it is a line that cannot be crossed. Retrofitting governance onto a public deployment leaves exactly the gaps auditors look for.

Capability and compliance are usually treated as a trade. They do not have to be.

When governance is bolted on after the model is chosen, residency, access, and audit always end up partial.

Architecture

Private GPT makes governance an architectural property.

• An organization-private deployment keeps the model inside the perimeter.
• A residency and isolation boundary guarantees where data lives and where it does not.
• An access and policy engine controls who can invoke what.
• End-to-end audit logging makes every interaction reviewable.

This is the deployment posture detailed in the enterprise GenAI architect framework, and it shares its governance spine with the rest of the systems.

Where it’s going

Private GPT is in pilot with regulated design partners, where the test is not model quality but whether governance holds up under real compliance scrutiny.

The next work is policy depth: finer-grained access control and richer audit, so the same deployment serves a research team and a regulated business unit without compromise. The reference architecture continues to mature alongside the broader ecosystem.