Souveraineté de l'IA en entreprise : le guide pour sécuriser vos projets IA
AI sovereignty refers to a company's ability to retain control over its data and AI models: where they are hosted, under which jurisdiction they are processed, who can access them, and how they are used to train or improve a third-party system. It is now one of the main barriers to AI adoption in the enterprise, right after a lack of internal maturity. This guide details the concrete levers for building a sovereign AI strategy without slowing down your projects.
What is AI sovereignty?
AI sovereignty covers the full set of choices that determine who retains control over an AI system and the data it processes: where it is hosted, the applicable legal framework, access rights, and whether or not the data is reused to train a third-party model.
This topic has become especially important with the rise of generative AI: more and more data teams send sensitive data (customer records, contracts, HR data) to third-party models without always knowing where it travels or who can access it. AI sovereignty is therefore not a theoretical question: it is a prerequisite for deploying AI in production under the right conditions.
Why AI sovereignty wrongly stalls so many projects
For data and AI leaders, data security and sovereignty consistently rank as the top objection before launching a project, ahead of cost and technical feasibility. The result: many AI POCs stay stuck before reaching production, for lack of a clear governance framework to reassure IT, legal, and sometimes the end client.
At turnK, we have run more than 600 CRM, ERP, data and AI modernization projects, with compliance requirements increasingly present in project specifications. It isn't sovereignty itself that blocks projects, but the absence of a method to build it in from the scoping phase, rather than addressing it urgently once the project is already underway.
The concrete levers for securing your AI projects
A sovereign AI strategy rests on three pillars: the technical choice of hosting and models, data governance, and making the move to scale reliable. Here is how to activate each one.
Choosing hosting and models suited to your constraints
The first lever is choosing models and infrastructure compatible with your regulatory requirements: hosting in Europe, data non-reuse clauses, or open-source models that can be deployed on your own infrastructure. This is the case, for example, with Mistral AI, whose models offer sovereign deployment, an option increasingly sought after by companies facing strict confidentiality requirements.
Structuring clear data governance
Before even selecting a tool, you need to map the data involved, define who can access it, and track its use. This governance must be documented and shared with IT and legal from the scoping stage, not after deployment.
Making the move from POC to production reliable
A sovereign AI project that is poorly industrialized remains fragile. It is therefore essential to follow a proven method for moving from pilot to production, especially when the project relies on internal data: making your AI projects more reliable with RAG helps you keep control over the sources used by the model, a key point for both sovereignty and answer quality.
AI sovereignty: the mistakes that make projects fail
The first mistake is choosing a tool before defining the governance framework: security then has to be retrofitted, at the cost of extra delays and expense. The second is treating sovereignty as a purely technical topic, when it also involves legal, business teams, and sometimes end clients.
In regulated sectors, these issues are even more sensitive. The work led by StackEasy, our group's entity dedicated to AI, at EuroBank Systems shows how well-built data governance can modernize an ERP while strengthening compliance rather than weakening it.
Structured support, from audit to production
Securing the sovereignty of your AI projects doesn't mean starting from scratch: it usually begins with an assessment. turnK offers a Data & AI in production offer that builds in data governance from the scoping stage, with StackEasy stepping in via an AI audit run by StackEasy when the topic goes beyond tool integration and requires advanced AI expertise. With 98+ certifications on reference platforms (Salesforce, HubSpot, Odoo) and dual AI expertise (Anthropic, OpenAI, Google), turnK and StackEasy support your teams from audit through to adoption.
the most common questions
It is the ability to retain control over your data and AI models: hosting, jurisdiction, access rights, and how data is used by third parties.
Because it's often addressed too late, after the tool is already chosen, which blocks the move from POC to production without clear governance.
No, but it's recommended for sensitive data; models like Mistral AI offer sovereign deployment suited to these needs.
No, if it's built in from the scoping stage: it actually secures the move to scale and avoids costly rework later on.
