Practical guide
Public AI services
Public services can offer fast access and managed capability. They may be appropriate where information, retention, contract, jurisdiction and access requirements are satisfied.
Private AI environments
Private environments can provide stronger control over models, data, identity and integration, but they also create operating, security, lifecycle and support responsibilities.
Hybrid architecture
Many institutions need a portfolio approach. Low-sensitivity workloads may use approved managed services while confidential or latency-sensitive workloads operate in more controlled environments.
What sovereignty changes
Sovereignty concerns who controls infrastructure, data, models, keys, operations, updates and legal jurisdiction. The appropriate level depends on the institution and workload.
Build a workload decision matrix
Evaluate sensitivity, residency, retention, latency, availability, integration, scale, cost, model requirements, user roles, auditability and human oversight.
Plan the operating model
Infrastructure must be maintained. Define ownership, monitoring, updates, support, incident response, capacity planning and evidence before production use.