AI governance
Make AI boundaries reviewable.
Governance teams need more than intent language. They need visible scope, claim boundaries, evidence status, owner responsibility, human review triggers, and a way to separate conceptual posture from implemented capability.
This page describes a framework pattern and website content surface. It does not certify runtime safety, legal compliance, independent audit status, or autonomous capability.
Practical signals
Claim boundaries
State what is conceptual, implemented, planned, unverified, and not claimed.
Evidence register
Tie each public claim to status, source, confidence, owner, and review date.
Review triggers
Route authority gaps, sensitive actions, and memory promotion to human review.
How to apply it
Map the system
Document role, audience, scope, data boundaries, and expected operating posture.
Publish the boundary
Show non-capabilities and unverified claims without making the homepage a denial wall.
Review the drift
Use the Boundary Model to flag isolation, over-control, aggression, and manipulation patterns.
Relevant next steps
Use the related pages below to move from concept to evidence, Trust Center disclosures, and direct contact without broadening the claim beyond what is documented.