Public and low sensitivity
Approved general or enterprise AI may be appropriate when policy and information permit.
Government Sovereign AI
Match the environment to the public-sector workload.
Government workloads vary enormously. Public information, internal administration, sensitive policy work, research, regulated data and operational systems should not automatically be placed in the same AI environment.
Workload routing
Different public responsibilities require different controls
The architecture should reflect information, model behaviour, knowledge sources and operational authority.
Internal institutional work
Identity, permissions, retention and authoritative sources become more important.
Sensitive or regulated work
Private, hybrid or specially governed environments may be required.
Jurisdictional context
Models should be tested for Canadian law, institutions, terminology, bilingual requirements and public-sector practices.
Agents
Tool permissions, approvals, evidence and escalation should be explicit.
Physical systems
Operational Sovereignty governs what intelligence is permitted to do in infrastructure or robotics.
Government pathway
Classify → Evaluate → Architect → Govern
Keep the decision explainable to technical and non-technical stakeholders.
Classify
Information, consequences and institutional responsibility.
Evaluate
Models, authoritative sources and contextual behaviour.
Architect
Deployment environment, infrastructure and controls.
Govern
Human authority, monitoring, change control and evidence.
Continue the journey