Establish the vocabulary, deployment boundaries, shared responsibilities, and control objectives needed to reason about sovereign AI systems.
Learning outcomes
Curriculum
The chapters are individually addressable documentation pages. You can link directly to a concept from another program, architecture decision, or implementation guide.
Establish a precise definition of sovereign AI and a common technical vocabulary.
Understand the purpose of sovereign AI and the difference between deployment location and enforceable control.
Evaluate data, model, infrastructure, operational, and evidence control as separate but connected dimensions.
Distinguish sovereignty from privacy, residency, localization, autonomy, portability, and security.
Define the system first, then map trust changes, flows, dependencies, and deployment patterns.
Set the intended purpose, actors, assets, dependencies, lifecycle, and system boundary before selecting controls.
Map where trust changes and where data, artifacts, requests, identities, tools, and evidence cross a boundary.
Compare public cloud, private cloud, on-premises, hybrid, edge, restricted-network, and air-gapped patterns without treating one as automatically sovereign.
Turn sovereignty goals into testable control objectives and verifiable evidence.
Translate sovereignty goals into testable statements of what must be allowed, prevented, approved, observed, retained, and recovered.
Design evidence alongside controls so important decisions, releases, configurations, and operating events can be verified.
Produce practical foundation artifacts and verify the module outcomes.
Apply SAI-100 to a synthetic private knowledge assistant and produce a system definition, boundary map, controls, evidence plan, and backlog.
Use scenario questions and an artifact checklist to verify the SAI-100 learning outcomes.
Create a system definition, trust-boundary map, control objectives, and evidence plan.
Design exercise; executable implementation pending
This is the public SAI-100 curriculum and self-assessment structure. It does not represent professional certification, production approval, or legal advice. Instructor rubrics and customer-specific patterns remain part of future private delivery.