Intended roles
- Enterprise architects
- Solution architects
- Technical leaders
GatedDraftadvanced program
Sovereign AI Architecture
Design and defend a vendor-neutral sovereign AI architecture with explicit boundaries, control points, evidence, and operational decisions.
- Version
- 0.1.0
- Modules
- 10
- Delivery
- Pilot planning
- Availability
- Curriculum and lab under development
- Content review
- 2026-08-02
- Automated lab
- Not yet validated
Program fit
Built for teams responsible for implementation decisions.
A systems program for designing and defending vendor-neutral sovereign AI architectures with explicit control and evidence boundaries.
Prerequisites
- Experience designing enterprise platforms or distributed systems
- Working knowledge of identity, networking, data platforms, and AI workload components
Capabilities
What the program is designed to develop.
Model trust and deployment boundaries
Create architecture decision records
Design controlled artifact and model lifecycles
Map technical controls to evidence
Produce a prioritized readiness backlog
Shared module sequence
One maintained registry, assembled for this outcome.
Program pages resolve module titles and descriptions directly from the Academy registry. Updates remain controlled in one source rather than copied across pages.
- 1SAI-100Content reviewed
Sovereign AI Foundations
Establish the vocabulary, deployment boundaries, shared responsibilities, and control objectives needed to reason about sovereign AI systems.
Open documentation - 2SAI-110Content reviewed
Trust Boundaries and Threat Modelling
Model data flows, actors, assets, attack surfaces, trust zones, and risk scenarios for private AI workloads.
Open documentation - 3SAI-120Content reviewed
Sovereign AI Reference Architecture
Design vendor-neutral architecture layers, deployment patterns, assurance points, and decision records.
Open documentation - 4SAI-200Content reviewed
Internal Artifact Supply Chains and Offline Builds
Control the import, verification, approval, storage, promotion, and offline build of AI software and model artifacts.
Open documentation - 5SAI-210Draft
Model Selection and Lifecycle Management
Evaluate model fit, licensing, provenance, packaging, approval, updates, and retirement inside a controlled lifecycle.
Open documentation - 6SAI-220Content reviewed
Model Serving, Routing, and Hardware
Select and operate model runtimes, routing patterns, hardware profiles, capacity controls, and reliability targets.
Open documentation - 7SAI-230Prototype
Private RAG and Permission-Aware Knowledge
Build grounded retrieval with controlled ingestion, citations, access enforcement, evaluation, lineage, and safe no-answer behavior.
Open documentation - 8SAI-240Draft
Identity, AI Gateways, and Egress Control
Propagate identity and enforce inspection, DLP, routing, endpoint, and response policies at controlled AI boundaries.
Open documentation - 9SAI-250Prototype
Governance, Assurance, and Evidence
Turn policies and control objectives into verifiable runtime, release, decision, and audit evidence.
Open documentation - 10SAI-260Draft
Observability, Reliability, and FinOps
Measure workload health, behavior, evaluation quality, capacity, cost, incidents, and operational evidence.
Open documentation
Practical labs
Observable work inside an approved environment.
Exercises use local, customer-hosted, or explicitly approved private infrastructure and are designed to produce repeatable validation and evidence.
LAB-ARCH-01Draft
Architecture Control Room
Integrate system, supply-chain, runtime, knowledge, gateway, operations, and evidence decisions.
Lab outputs
- Architecture package
- Decision record set
- Control-to-evidence map
Last content review: 2026-08-03Last automated lab validation: Not yet validatedIndependent reproduction: Not yet completed
Lab boundary principles
- No customer documents or prompts are sent to SovAIHub by default
- Prerequisites and supported configurations are explicit
- Validation is automated or clearly repeatable
- The environment can be reset and the exercise repeated
- Limitations and unsupported configurations are stated honestly
Capstone and assessment
Finish with implementation artifacts, not attendance alone.
Capstone outputs
- System context and trust-boundary diagrams
- Architecture decision records
- Control and evidence map
- Runtime and model-serving decision
- Artifact-supply-chain design
- Readiness backlog
Assessment method
Reviewed architecture capstone using an explicit completeness, control, evidence, portability, and operational-readiness rubric.
Delivery modes
- Pilot planning
- Customer-hosted architecture workshop planning
- Enterprise interest registration
Credential guardrail: Initial delivery may use “program completed,” “assessed completion,” or “capstone passed.” It does not award a professional certification.
Connected implementation assets
Build on SovAIHub kits and solution patterns.
Program pages resolve kit details from the existing SovAIHub product registry, keeping product information maintained in one place.
Architecture Pack
SovAI Architecture Pack
Sovereign AI, RAG, agent, and governance architecture templates for planning private AI systems.
View kitArtifact Hub
SovAI Air-Gap Internal Artifact Hub
Open-source reference implementation for a controlled internal AI artifact supply chain with local registry, wheelhouse, prompt/tool manifests, approvals, and offline builds.
View kitCurrent limitations
What this program does not claim yet.
Honest release boundary
- The complete assessed program and instructor delivery pack are still in development.
- Public outlines do not include production lab answer keys or customer-specific architecture patterns.
- This program is not a professional certification or an industry accreditation.
Enterprise pilot planning
Discuss Sovereign AI Architecture for your team.
Share role mix, environment, technical constraints, data boundary, and desired implementation outputs. Do not submit sensitive architecture details through the public form.
No public checkout, account, or sensitive architecture upload required