Alia, Spain's LLM: the 4 Levels of AI Sovereignty and Which One Actually Matters for Your Business
The Spanish government has built Alia, its own language model. But sovereignty over AI is not a single thing — it has four distinct layers, and only one of them directly affects businesses.
What is Alia
Alia is the large language model (LLM) project driven by the Spanish Government through the Secretary of State for Digitalisation and Artificial Intelligence. It is a set of models trained with a special focus on Spanish and Spain's co-official languages — Catalan, Basque, Galician and Valencian — developed under criteria of technological sovereignty with publicly supervised, curated corpora.
The question many executives ask when they hear about Alia is: what does this actually change for my company? The short answer: it depends on which layer of sovereignty we are talking about.
The 4 Layers of AI Sovereignty
When we talk about sovereignty over artificial intelligence, we are not talking about a single dimension. There are four distinct layers, each with different implications.
1. Model sovereignty
Who creates the base model, under what licences and with what roadmap?
This is where Alia plays a direct role. Having a Spanish LLM reduces dependence on closed foreign providers — OpenAI, Anthropic, Google — and allows the public ecosystem to build on a nationally controlled technological foundation.
For most private companies, this layer is relevant but indirect: it shapes which models will be available, under what conditions and with what continuity guarantees, but it does not change their operations today.
2. Data sovereignty
What corpus fed the model, with what traceability and quality criteria?
This layer determines what is inside the model and what is not. An LLM trained on Spanish public data and official documentation has a very different profile from one trained on generic Anglo-Saxon corpora.
Again, this layer is important at a country level — whoever controls the training data has influence over the model's biases and capabilities — but for enterprise users, what matters most is not where the model comes from, but what happens with their own data when they use it.
3. Linguistic and cultural sovereignty
Is the model good at Spanish and co-official languages? Does it understand the Spanish legal and administrative context?
This layer has immediate practical impact for many sectors. A model that understands the Spanish Official Gazette (BOE), the terminology of the General Council of the Judiciary or the nuances of Spanish commercial law is significantly more useful for a law firm or compliance team than a generic English-optimised model.
Alia directly targets this layer. For sectors like legal, public administration or finance in Spain, this is a tangible advantage.
4. Inference sovereignty
Where does the model run? Who manages the infrastructure? What data goes in, comes out and is audited?
This is the layer that truly matters for businesses. And it is the one that Alia, on its own, does not solve.
Inference sovereignty is the difference between sending your data to an external API — with all the GDPR, AI Act and data-leakage implications that entails — and running the model within your own perimeter, with full traceability and no critical third-party dependencies.
How Does This Affect Your Business?
Of the four layers, only inference sovereignty affects you directly today. And the European regulatory framework is pushing precisely in that direction.
The EU AI Act requires traceability and operational control over high-risk AI systems. The GDPR prohibits transferring personal data to jurisdictions without an adequate level of protection — which includes many AI APIs hosted in the US. The National Security Framework (ENS) is mandatory for the public sector and its technology providers. And DORA requires banks to demonstrate operational resilience and reduce dependence on critical third parties.
The result is that many AI tools currently used without governance in businesses — sending contracts, case files or client data to public APIs — will become unviable as the regulatory framework is enforced.
The Practical Solution: Private Inference
The answer is not to give up on AI. It is to run it within your own perimeter.
This means designing an architecture where:
- Models (whether Alia, open-source models like Llama or Mistral, or others) run on your infrastructure or on sovereign European infrastructure
- None of your organisation's data leaves the perimeter to be processed
- There is full traceability: what was processed, with which model, at what time and by whom
- Regulatory compliance (GDPR, AI Act, ENS) is part of the design, not a later patch
At Galileo Studio we call this Private & Governed AI. We design these architectures for organisations that handle sensitive information — law firms, compliance teams, public sector, banking — and need to leverage the power of generative AI without giving up control.
Conclusion
Alia is an important step forward for Spain's technological sovereignty at country level. But for businesses, the relevant question is not who trained the model — it is where it runs and who controls what happens to their data.
Inference sovereignty is the layer your business can and should manage today. The regulatory framework is already in motion. The question is whether you build the architecture before it is required, or after.