Introduction to sovereign AI architecture
Translate system purpose, sovereignty requirements, threats, and operating constraints into a vendor-neutral architecture method.
Before you start
Module prerequisites: SAI-100, SAI-110. Check the exercise-specific tools and environment before running commands.
Module outcomes: Select an appropriate deployment pattern; Document portability and concentration decisions; Place assurance points across the lifecycle. This lesson contributes to that sequence.
Architecture connects requirements to operation
A sovereign AI reference architecture is not a product diagram. It is a vendor-neutral method for assigning responsibilities, boundaries, interfaces, controls, evidence, and operational decisions across the complete AI system lifecycle.
The architecture should make it possible to explain what the system does, where control changes, who owns material decisions, how required properties are enforced, and how the implementation can be verified and changed.
Required inputs
Begin with:
- Intended purpose, prohibited uses, users, affected parties, and decision impact.
- Data classes, knowledge sources, models, tools, interfaces, and external dependencies.
- Sovereignty control requirements across data, models, infrastructure, operations, and evidence.
- Trust boundaries, flows, assets, attack surfaces, and prioritized threat scenarios.
- Availability, latency, capacity, recovery, cost, portability, and skills constraints.
Do not choose components before these inputs are sufficiently understood.
Architecture principles
SAI-120 applies these principles:
- Purpose before platform: architecture follows the approved outcome and boundary.
- Explicit authority: every material decision and privileged action has an owner.
- Controlled interfaces: data, identity, artifacts, tools, and evidence cross boundaries through defined contracts and policy.
- Versioned behavior: models, prompts, retrieval, policy, tools, and configuration are identifiable and change-controlled.
- Evidence by design: claims and controls have planned verification evidence.
- Failure is architectural: rejected requests, unavailable dependencies, degraded models, rollback, and recovery have designed behavior.
- Portability is tested: exit and change paths exist at the levels the organization actually requires.
- Platform neutrality, explicit adapters: stable responsibilities remain independent of rapidly changing technology-specific implementation.
Quality attributes and trade-offs
Architecture must balance confidentiality, integrity, availability, safety, performance, cost, usability, observability, maintainability, portability, and assurance. Maximizing one can weaken another.
For example, strict disconnection can reduce remote exposure while increasing artifact, update, support, and operational complexity. Record such consequences in an architecture decision record.
SAI-120 outputs
The module produces a coherent view set, responsibility map, controlled-interface model, decision records, control and evidence placement, portability plan, and readiness backlog. These artifacts should be usable whether the implementation targets cloud, private cloud, on-premises, hybrid, edge, restricted, or air-gapped infrastructure.
Module worked example
The logical diagram shows a gateway, but the deployment view exposes a second model endpoint.
- Apply the control
- Draw the alternate route and close or govern it before accepting the architecture decision.
- Expected result
- Context, deployment and control views agree about reachable paths and owners.
- Troubleshooting
- Compare diagrams using the same actors, interfaces and resource identifiers; inconsistent names can hide a bypass.
Practice and completion check
Choose one control from this lesson. Identify its actor, target, enforcement point and expected result. Describe an allowed case and a denied or failed case. For a runnable exercise, retain observed output and separate it from expected output. Record unresolved limitations before marking the lesson complete.
Your lesson progress
Completion is self-reported learning progress; it does not certify practical competence.
Status: Not marked
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