When a company adopts an external artificial intelligence tool, it rarely asks who has access to the data it processes, under which legislation its servers operate or what happens if the provider changes its terms of use. These are not technical questions, they are questions of corporate governance. It is neither an abstract concept nor one exclusive to public administrations: it is the framework that determines whether an organisation really controls its artificial intelligence or delegates that control to third parties without fully realising it.
Why technological sovereignty has become a strategic priority
For years, the adoption of external AI solutions was justified by their speed of implementation and their apparently low cost. What was not always accounted for was the cost of dependence: the difficulty of changing provider, the exposure of confidential data to foreign jurisdictions or the impossibility of demonstrating to a regulator that the system works as expected.
The geopolitical context has accelerated this reflection. Technological dependence is no longer just a privacy problem but has become a problem of security and business continuity. As the European Commission itself acknowledges, more than 80% of the digital products, services, infrastructure and intellectual property consumed by the European Union comes from providers outside its borders, an imbalance that has placed technological sovereignty at the centre of the continent's political and business agenda.
For a company, this translates into something very concrete: operational continuity. If a cloud provider changes its terms of service, if a regulatory decision in another country affects access to certain models or if the service is interrupted, organisations without technological sovereignty are left without real alternatives. Sovereignty, in this sense, is also resilience.
The European regulatory framework as a catalyst
Two regulatory frameworks have redefined what it means to operate with AI legally in Europe: the General Data Protection Regulation (GDPR) and the EU AI Act.
The Spanish Data Protection Agency (AEPD) points out that Article 25 of the GDPR, on data protection by design and by default, requires technical safeguards (such as the ability to delete histories or encrypt information) to be built into the very architecture of AI tools, and not added afterwards. This has a consequence that many organisations overlook: contracting an AI service does not transfer responsibility for data processing to the provider. If an employee uses an external AI tool in the course of their duties and shares information about customers or the company itself, the party that remains accountable to the AEPD for that processing is the organisation, not the person who pressed the button.
Regulation (EU) 2024/1689, known as the EU AI Act, is the world's first comprehensive legal framework on AI. It entered into force on 1 August 2024 and, according to the current timetable published on the European Commission's digital strategy portal, the obligations for high-risk systems (such as credit scoring, fraud detection or medical diagnosis) should be fully applicable from 2 August 2026. This point needs qualifying because it is in flux: in mid-2026 the Commission is negotiating, within the Digital Omnibus package, a possible postponement of that timetable until December 2027, although no formal adoption has yet taken place. In any case, for projects working with sensitive data, compliance is not a legal layer added at the end; it has to be built into how the system is designed and deployed from the outset.
The combination of both frameworks creates a scenario in which operating with AI without real sovereignty is at once an operational, legal and reputational risk.
The five dimensions of technological sovereignty in AI
Understanding technological sovereignty as a binary attribute (you either have it or you do not) is a common mistake. In practice it is a set of capabilities that can be held to a greater or lesser degree on different levels. At LurNova we work with a model that distinguishes five dimensions.
Data sovereignty
Knowing exactly where the data feeding the AI system resides, under which jurisdiction it operates and who has access to it. Without this dimension, any other is incomplete.
Model sovereignty
The ability to know how the model being used works, to be able to audit it, adapt it or replace it without being locked into a provider. Private models deployed on own or dedicated infrastructure are the most direct answer to this need.
Infrastructure sovereignty
Having control over the execution environment, whether on own premises, in a private cloud or in sovereign cloud environments with verifiable legal guarantees.
Operational sovereignty
The independence to operate the system without depending on the provider's availability or commercial decisions, including the portability of configurations, trained models and historical data.
Audit sovereignty
The ability to trace, review and explain the decisions the system makes, both for internal use and to demonstrate compliance to regulators, customers or stakeholders.
No organisation needs the maximum in all five dimensions at once. What it does need is to be clear about where it accepts dependence and where it cannot afford it. A realistic first step for any company is to audit in which dimension there is currently a dependence that had not been identified as such.
What technological sovereignty is not
The term is sometimes used loosely, and that leads to the wrong decisions. Buying a tool from a European provider does not guarantee sovereignty if the servers are outside the EU or if the data are processed on third-party infrastructure without verifiable guarantees. Nor is hosting a model in a local provider's cloud sovereignty if the contract does not include adequate data processor clauses.
Using open-source technology can be an important lever towards sovereignty, but it does not guarantee it on its own. An open-source model deployed without audit capability or access control still has vulnerabilities that compromise operational independence. The EU Open Source Strategy itself, presented in June 2026 as part of the Tech Sovereignty Package, places open source at the heart of European industrial policy precisely because it makes it possible to audit and maintain a solution without depending on a single provider, but that audit has to actually take place, not merely be possible in theory.
Technological sovereignty and competitive advantage
There is a narrative that presents technological sovereignty as a cost: the organisation gives up the convenience of generic solutions in order to gain control. It is a simplification that deserves qualification.
Companies that implement AI with sovereignty criteria not only avoid regulatory risks, they also build differentiated assets. A private model that works with the company's own data, tuned to its processes and hosted on controlled infrastructure, produces results that are more accurate and more aligned with the business than any generic solution. That knowledge is not available to competitors or to the provider: it is an asset exclusive to the organisation. In a market where more and more customers, partners and investors ask about the traceability and security of AI systems, those who already have these capabilities in place start with an advantage.
Conclusion
Technological sovereignty in AI is neither a philosophical debate nor an ideological preference. It is a strategic decision with concrete consequences for regulatory compliance, operational continuity, the protection of knowledge assets and the ability to compete in the long term. At LurNova we start from the premise that artificial intelligence that is not secure is not intelligent. If you are assessing how to implement AI in your organisation with the level of control your business requires, we can support you from diagnosis to deployment.
CEO of LurNova, with extensive experience in technology and digital transformation. Involved in developing technology projects and applying artificial intelligence in organisations, he combines technical knowledge with business vision.
