ModulesSAI-100
SAI-100 table of contents
Sovereign AI Foundations
Establish the vocabulary, deployment boundaries, shared responsibilities, and control objectives needed to reason about sovereign AI systems.
Learning outcomes
What you should be able to do
- Define sovereignty in terms of enforceable technical control
- Map data, model, infrastructure, operational, and evidence boundaries
- Distinguish private deployment from a demonstrably controlled AI system
- Create an initial sovereignty control objective map
Curriculum
Work through 4 sections in order.
The chapters are individually addressable documentation pages. You can link directly to a concept from another program, architecture decision, or implementation guide.
Orientation
Establish a precise definition of sovereign AI and a common technical vocabulary.
Introduction to sovereign AI
Understand the purpose of sovereign AI and the difference between deployment location and enforceable control.
Sovereignty control dimensions
Evaluate data, model, infrastructure, operational, and evidence control as separate but connected dimensions.
Sovereignty terminology
Distinguish sovereignty from privacy, residency, localization, autonomy, portability, and security.
System and boundaries
Define the system first, then map trust changes, flows, dependencies, and deployment patterns.
Define the AI system
Set the intended purpose, actors, assets, dependencies, lifecycle, and system boundary before selecting controls.
Trust boundaries and data flows
Map where trust changes and where data, artifacts, requests, identities, tools, and evidence cross a boundary.
Deployment boundaries and patterns
Compare public cloud, private cloud, on-premises, hybrid, edge, restricted-network, and air-gapped patterns without treating one as automatically sovereign.
Control and evidence
Turn sovereignty goals into testable control objectives and verifiable evidence.
Write effective control objectives
Translate sovereignty goals into testable statements of what must be allowed, prevented, approved, observed, retained, and recovered.
Evidence by design
Design evidence alongside controls so important decisions, releases, configurations, and operating events can be verified.
Apply and assess
Produce practical foundation artifacts and verify the module outcomes.
Foundation architecture workshop
Apply SAI-100 to a synthetic private knowledge assistant and produce a system definition, boundary map, controls, evidence plan, and backlog.
Knowledge check and completion criteria
Use scenario questions and an artifact checklist to verify the SAI-100 learning outcomes.
Practical completion package
- One-page AI system definition
- Context and trust-boundary diagram
- Control-objective register
- Evidence map
- Prioritized readiness backlog
Current release boundary
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.