FAQ · Governance & security
What is Sovereign AI? Is it the same as private deployment?
Sovereign AI is about an organization’s control over data boundaries, model choices, operating evidence, and day-to-day capability — not about putting everything on-prem. A business can move from Rent to Orchestrate to Adapt and Own as risk and readiness justify it.
Key points
- Sovereignty is first a control question: who can access data, how models are routed, whether runs are auditable, and whether the team can take over and improve
- Rent: use cloud models for low-risk, non-core work to validate value quickly, without taking on the full cost of private infrastructure on day one
- Orchestrate: unify model entry, permissions, cost, logs, and evaluation so cloud capability is governable, replaceable, and reviewable
- Adapt: when output structure, sensitive fields, or domain rules need more stability, consider private models, SLMs, or LoRA; fine-tuning changes behavior, not every governance condition
- Own: for critical workflows, own the data boundary, evaluation baseline, runbook, and iteration rhythm; whether to build infrastructure depends on quality, cost, and operating capacity
- Choose deployment mode during qualification and business/data readiness — do not assume “all cloud” or “all private” first
How to do it
- Classify data and actions as public, internal, sensitive, or blocked; mark what must stay in-boundary or require human review
- Compare cloud, private, and hybrid options on risk, quality, latency, cost, vendor dependence, and operating capacity
- Run a 7-day fast validation inside the chosen boundary, then decide whether to enter the standard pilot or migrate more capability
- Make permissions, logs, evaluation, rollback, runbook, and owner part of handoff — owned capability is more than owning deployment files