SovAIHub
Enterprise technical enablement by SovAIHub

Sovereign AI Academy

Develop the capability to architect, deploy, operate, and prove control over sovereign AI systems.

A technical body of knowledge, practical lab direction, and private enterprise cohort model for teams building AI in regulated, restricted, on-premises, hybrid, or air-gapped environments.

Explore programsRegister enterprise interest

Why the Academy exists

Running a local model is only the beginning.

Sovereign AI requires teams to understand and control the full system—not only where inference runs. The Academy organizes that capability around the technical boundaries that matter.

Infrastructure boundaries

Know where compute, data, prompts, models, and services are allowed to operate.

Artifact supply chains

Import, verify, approve, store, and promote software and model artifacts under control.

Private knowledge

Ground answers in permission-aware retrieval, citations, lineage, and safe no-answer behavior.

Egress and agent actions

Apply identity, policy, inspection, allowlists, and approval at execution boundaries.

Operations

Measure quality, reliability, capacity, cost, incidents, and lifecycle health.

Evidence

Prove what was approved, deployed, evaluated, changed, and operated.

Learning sequence

A real progression from principles to advanced workloads.

Programs reference shared modules in a deliberate order, so advanced agent and platform work rests on identity, evidence, model-serving, and private-data foundations.

Academy Level ≠ Assurance Level. Academy levels describe learning progression. A deployed system earns no assurance result through course completion; its controls and evidence must be evaluated separately. Compare assurance levels →
  1. 01

    Foundations

    Sovereignty principles, trust boundaries, threat modelling, and reference architecture.

    Assurance relationship

    Understand the assurance model; do not claim a system level.

  2. 02

    Controlled infrastructure

    Artifact supply chains, offline builds, model lifecycle, serving, routing, and hardware.

    Assurance relationship

    Implement foundations commonly associated with Assurance Level 1.

  3. 03

    Private AI systems

    Private RAG, permission-aware knowledge, identity, secure gateways, and egress control.

    Assurance relationship

    Implement controls commonly required for Assurance Level 2.

  4. 04

    Assurance and operations

    Governance, evidence, observability, reliability, FinOps, Kubernetes, and OpenShift.

    Assurance relationship

    Develop capabilities needed for Assurance Level 3.

  5. 05

    Advanced sovereign workloads

    Small models, governed agents, controlled inference, and internal serving platforms.

    Assurance relationship

    Design toward Assurance Level 4 requirements without pre-awarding the level.

Technical programs

Programs built around implementation outputs.

Each public outline states its real delivery status. Structured programs, maintained labs, assessment, and instructor support mature through controlled private delivery.

GatedDraft
advanced

Sovereign AI Architecture

Design and defend a vendor-neutral sovereign AI architecture with explicit boundaries, control points, evidence, and operational decisions.

10 modulesPractical capstone

Designed for

Enterprise architects · Solution architects · Technical leaders

View program outline
GatedPrototype
intermediate

Private RAG Engineering

Build and evaluate a private, grounded, permission-aware RAG system with citations, access controls, observability, and evidence.

5 modulesPractical capstone

Designed for

AI engineers · Solution architects · Application engineers

View program outline
GatedOutline
advanced

Air-Gapped AI Platform Engineering

Build the controlled supply chain and runtime required to operate AI workloads without direct runtime internet access.

7 modulesPractical capstone

Designed for

Platform engineers · DevOps engineers · Security engineers

View program outline

Hands-on labs

Customer-hosted exercises that produce observable evidence.

The Academy’s differentiator is practical work inside a local, customer-hosted, or approved private environment—not conceptual slideware.

Sovereign AI Lab-in-a-Box

Designed to stay in your boundary.

Lab automation, approved artifacts, synthetic documents, exercises, validation, troubleshooting, and locally generated evidence are assembled as one repeatable delivery unit.

  • Explicit prerequisites
  • Supported configurations
  • Repeatable validation
  • Reset and recovery path
  • Local evidence output
  • Documented limitations

Lab execution boundary

1

Approved artifact pack

2

Private lab runtime

3

Local evidence report

No silent collection of prompts, documents, lab outputs, or customer telemetry. Any future telemetry remains opt-in and customer-controlled.

What participants build

Inspect the artifacts, not just the syllabus.

Start with reusable worksheets, then run a control-focused synthetic lab that produces local evidence.

System definition

SAI-100 boundary, control, and evidence worksheet

Download template

Architecture decision record

SAI-120 option, consequence, verification, and exit record

Download template

Permission-aware RAG evidence

Executable LAB-230-01 controls, tests, commands, and evidence contract

Open executable workshop

Role-based outcomes

One shared body of knowledge, applied to different responsibilities.

Role paths will be introduced only when their referenced programs are genuinely available. The initial Academy shows the capability outcomes without implying certification.

Architects

Design defensible boundaries, decisions, controls, and evidence paths.

AI engineers

Build grounded, permission-aware private AI workloads and evaluate them.

Platform engineers

Operate controlled supply chains, runtimes, clusters, and updates.

Security engineers

Model threats and enforce identity, egress, tool, and approval policies.

Governance leads

Connect policy and risk decisions to verifiable technical evidence.

Connection to implementation

Learning that produces a practical implementation path.

Capstones expose real readiness gaps and produce useful artifacts. Teams can then implement internally or continue with SovAIHub for assessment, pilot, deployment, or operational enablement.

  1. 01

    Academy program

  2. 02

    Readiness assessment

  3. 03

    Pilot pack

  4. 04

    Implementation

  5. 05

    Operational handover

Public Body of Knowledge

A useful, citable foundation for sovereign AI practice.

Start with public definitions, architecture principles, trust boundaries, threat models, control checklists, and the SAI-100 Sovereign AI Foundations structure.

SAI-100 · Public

Sovereign AI Foundations

Understand sovereignty as enforceable technical control across data, models, infrastructure, operations, and evidence—not as a deployment-location label.

Explore the knowledge base

Enterprise pilot planning

Develop capability inside your own technical boundary.

Register interest in a role-aware pilot, customer-hosted prototype lab, or implementation-team enablement path.

No public checkout, account, or sensitive architecture upload required

Register enterprise interestReview AI readiness