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The Standard

Seven non-negotiables for sovereign AI

Not guidelines. Not best practices. These are the architectural requirements that define whether a system is truly sovereign or just marketing.

Every SIA-compliant system must satisfy all seven principles. Partial compliance is non-compliance. If your data leaves, if your model is locked, if your audits have gaps—it's not sovereign.

1

Data Residency

Your data never leaves infrastructure you control. Not "encrypted in transit." Not "anonymized before sending." Never leaves. Period.

This means local inference, local embeddings, local vector storage. The moment data crosses a boundary you don't control, sovereignty is broken.

Test: Can you prove to a regulator that user query X never left your perimeter? If you can’t produce that proof, you fail.
2

Model Sovereignty

Open weights you can inspect, version, and modify. No black boxes. You see the architecture, you control the weights, you own the deployment.

Proprietary APIs fail this test by definition. You're calling someone else's model running on someone else's infrastructure with weights you'll never see.

Test: Can you run your entire AI system on an air-gapped network with zero internet? If not, you have hidden dependencies.
3

Vendor Independence

Your system works if any single vendor disappears tomorrow. OpenAI shuts down? You're fine. Anthropic changes ToS? Doesn’t affect you. No single point of failure in your AI supply chain.

This requires abstraction layers, fallback chains, and multi-model architecture. Dependency on one provider is dependency on their decisions.

Test: List every external service your AI requires. Now imagine each one gone. Can you recover in days, not months?
4

Audit Completeness

Every inference logged. Input, output, model version, timestamp, routing decision, context used. When the regulator asks "why did the AI say X?", you have the complete chain.

Partial logging is useless. If you can explain some decisions but not others, auditors will assume the worst about the gaps.

Test: Pick a random AI output from six months ago. Can you reconstruct exactly how it was generated, with what model, using what context?
5

Hybrid Intelligence

Smart routing between local and cloud based on sensitivity classification. Not everything needs to stay local. Public data, generic queries—route them efficiently. But sensitive data never crosses the line.

The router is the brain. It classifies in real-time, applies rules you define, and makes the call: local or cloud? The decision is logged, auditable, defensible.

Test: Does your system know the difference between "what’s the weather" and "analyze this patient record"? Automatic classification is mandatory.
6

Governance by Design

Compliance baked in, not bolted on. Security isn't an afterthought or a checklist before launch. It's in the architecture from day one.

This means: consent tracking, bias detection, explainability layers, drift monitoring, incident response—all designed in, not patched later.

Test: If a new regulation drops tomorrow, how much of your system needs to change? Governance by design means the answer is "configuration, not code."
7

LLM Agnosticism

No dependency on any single model or provider. Your architecture abstracts the model layer. When something better drops, you switch in days, not months.

We don't train foundation models—that's a billion-dollar game. We use open weights, adapt with PEFT when needed, and architect for switching. The model is replaceable. The architecture is the asset.

Test: Llama 5 releases next month. How long until your production system can use it? If the answer is "rebuild," you’re locked in.

How it comes together

The Standard defines what. The Framework defines how. See the methodology that implements these principles.

View the Framework →