As European public bodies intensify their digital transformation, data sovereignty has become a defining concern. The challenge is not only about protecting data from misuse, but also about ensuring that governments retain control over how data is stored, shared, and used — especially when advanced technologies like AI come into play.
To support vigilance and decision-making, here are 10 key questions public bodies should ask, why each question matters, and how to steer towards sound answers.
- Do we have a shared vision on data and AI across all levels of government?
Why it matters: Without alignment, scaling AI and data-driven innovation remain fragmented. Municipalities, provinces, and national institutions often pursue different strategies, creating silos.
Answer range: Some governments operate with independent approaches, others push for national coordination.
How to steer:
- Develop a multi-level governance model that defines which decisions must be harmonized nationally (e.g., standards, ethics) and which can remain local (e.g., service design).
- Use federated data strategies: each body controls its own data, but shares according to common agreements.
- Establish a national AI & data strategy forum where municipalities, provinces, and ministries align regularly on goals, terminology, and priorities.
- What governance agreements are in place for data sharing?
Why it matters: Data sovereignty depends on clear rules: who owns what, who can access what, and under which conditions. Without governance, AI strategies collapse under mistrust.
Answer range: Ad-hoc agreements versus structured governance with standards, access rules, and clear responsibilities.
How to steer:
- Introduce mandatory baseline agreements across government: metadata standards, security classifications, retention policies.
- Create a data-sharing contract template for public bodies to use when collaborating.
- Build trust frameworks where access is conditional not just on legal agreements, but on continuous compliance monitoring.
- Are we balancing standardization and flexibility effectively?
Why it matters: Over-standardization can stifle innovation, while too much flexibility breeds fragmentation.
Answer range: Either rigid frameworks that hinder adoption or overly loose agreements that lead to incompatible systems.
How to steer:
- Apply the principle of “freedom within a framework”: define a limited set of mandatory standards (interoperability, security, data quality) but leave implementation choices open.
- Encourage sandboxing: allow agencies to test new technologies in controlled settings, then translate successful practices into standards.
- Use compliance audits to ensure flexibility does not undermine sovereignty goals.
- Does privacy legislation and GDPR block AI innovation — or enable responsible scaling?
Why it matters: Many see GDPR as a brake on progress. In reality, it ensures citizens’ rights and trust, both prerequisites for sustainable AI use.
Answer range: Some bodies perceive GDPR as a burden, others as a trust-building mechanism.
How to steer:
- Position GDPR not as a barrier, but as a design principle. Build privacy-by-design into every project.
- Invest in data anonymization, synthetic data, and federated learning techniques that enable AI innovation without exposing personal data.
- Use GDPR compliance as a competitive advantage in building citizen trust and strengthening legitimacy.
- How do we define and safeguard digital sovereignty in practice?
Why it matters: In a cloud- and AI-driven world, true sovereignty is not about isolation but about retaining meaningful control over infrastructure, standards, and decision rights.
Answer range: From reliance on foreign cloud providers to sovereign cloud strategies and open-source adoption.
How to steer:
- Define sovereignty criteria explicitly: data location, contractual clauses, reversibility, and auditability.
- Prioritize European-based cloud providers or hybrid solutions that guarantee compliance with EU regulations.
- Maintain exit strategies from vendors to prevent lock-in, including contractual clauses for data portability.
- Do we require maximum control before innovating, or can innovation and sovereignty progress together?
Why it matters: Waiting for perfect control can paralyze progress, but blind adoption risks lock-in.
Answer range: Conservative stances that delay innovation versus pragmatic approaches that combine pilots with safeguards.
How to steer:
- Adopt a “dual-track” approach: innovate in low-risk areas while building stronger sovereignty safeguards in parallel.
- Use pilot projects as learning grounds for governance models, not just for technology.
- Create a sovereignty checklist for all innovations: where is data stored, who has access, what are the exit options?
- Are we making optimal use of open technology?
Why it matters: Open standards and open-source tools foster transparency, interoperability, and trust. They also reduce dependence on proprietary ecosystems.
Answer range: Limited experimentation versus systematic adoption of open technology in procurement and design.
How to steer:
- Encourage the use of open standards wherever possible, while recognizing
that proven proprietary solutions also play a role in scalability and compliance.
- Require vendors to demonstrate interoperability and explainability of AI solutions.
- Support initiatives that promote transparency, whether they are open-source or commercial.
- How do we ensure trust and engagement from citizens and stakeholders?
Why it matters: Data governance is not just technical — it requires societal buy-in. Citizens expect transparency and fairness in how their data is used.
Answer range: Minimal citizen involvement versus participatory governance where stakeholders co-shape rules and oversight.
How to steer:
- Establish citizen panels or advisory boards on data and AI use.
- Communicate not only compliance, but also the purpose and benefits of data initiatives.
- Provide citizen dashboards showing how their data is used and safeguarded.
- Build trust incrementally: start with low-risk, high-benefit projects (e.g., traffic optimization) before moving into more sensitive domains.
- Are we embedding ecosystem thinking in AI projects?
Why it matters: No single public body can achieve sovereignty and innovation alone. Collaboration with knowledge institutions, businesses, and civil society accelerates progress.
Answer range: Isolated pilots versus structured ecosystems with shared data infrastructures and governance models.
How to steer:
- Develop public-private innovation hubs where data governance is a shared responsibility.
- Use federated data spaces to enable collaboration without centralizing control.
- Involve universities and research institutes as neutral brokers, ensuring scientific rigor and independence.
- Anchor ecosystem collaboration in legal and ethical frameworks that guarantee sovereignty is not compromised.
- Do we have the right skills, culture, and leadership for sovereign data use?
Why it matters: Technology alone does not guarantee sovereignty. Skilled staff, ethical awareness, and strong leadership are vital to embed governance in daily practice.
Answer range: Skills gaps and fragmented leadership versus coordinated training, culture-building, and accountability structures.
How to steer:
- Invest in continuous training on AI, data governance, and digital sovereignty for public officials.
- Create a data ethics curriculum tailored to civil servants.
- Foster a culture of collaboration over data hoarding by rewarding joint projects.
- Ensure leadership accountability: define sovereignty KPIs for senior executives.
The long view on data sovereignty
For European public bodies, data sovereignty is not a static state but a continuous balancing act: between innovation and control, flexibility and standardization, openness and protection. By asking the right questions — and steering towards answers that prioritize trust, interoperability, and sovereignty — governments can avoid fragmentation and lock-in, while building a resilient digital foundation for public services.
This vigilance will ensure that Europe’s digital government is not only innovative but also sovereign, transparent, and trusted by the citizens it serves.
