The architectural perimeter within which private AI runs: ownership, access, identity, evidence, and control designed as one structure.
Secure Enterprise Boundaries describe the architectural perimeter within which Private AI systems run: ownership, access, identity, evidence, and control designed as one structure rather than assembled from separate products.
Inside the boundary, sensitive data and regulated workflows operate on infrastructure the enterprise owns. Models are served locally, identity is enforced at every crossing, and every access leaves evidence. Outside dependencies are deliberate and explicit, so they can be counted.
For regulated industries, Secure Enterprise Boundaries are a precondition for adopting AI: HIPAA, CMMC, GLBA, and NERC CIP environments cannot rely on a third party's terms of service as their perimeter, so the boundary has to be their own.
See also Private AI, Digital Transformation and Enterprise Architecture.