PRIVATE AI
Private AI in Luxembourg: Where Does Your Data Actually Go?
A practical guide to local AI, private hosting, external models and the questions Luxembourg businesses should ask before connecting sensitive documents.
“Our AI is hosted in Europe” sounds reassuring. It does not tell you where a question is processed, where a backup is stored or what a connected tool receives. Those are different decisions.
For a Luxembourg business considering AI for confidential documents, the useful starting point is a map of the entire journey—not a single promise about a server.
Follow one document through the system
Imagine a company wants staff to ask questions about its internal supplier agreements. A document may be uploaded, converted to text, split into passages and indexed. A question then triggers retrieval, and selected passages are sent to a model to produce an answer. Logs, backups and connected services can create additional copies.
Draw that chain before choosing a deployment. Note the operator, location, retention period and access rights at each step. If the answer can trigger an email or update another system, include that action and its destination too.
A private application is not automatically a local model
The application and document index can run on private infrastructure while the model handling a question is supplied by an external provider. Conversely, a locally operated model may still use an external OCR service, monitoring platform or backup. “Private”, “European” and “local” should each be defined for the particular component.
Ask who operates the infrastructure, where inference and embeddings are produced, whether prompts or passages are retained, and what the contract says about provider use. An EU location alone does not settle every question about access, transfers or confidentiality.
| Description | What it may mean | What to verify |
|---|---|---|
| Private hosting | The application runs on dedicated infrastructure. | Where models, logs and backups run. |
| EU hosting | A component is located in the EU. | Who can access it and where all processing occurs. |
| Local model | Inference runs on an organisation-controlled system. | Whether extraction, tools and storage are local too. |
What to decide before a pilot
Start with a limited, representative dataset and identify the information that must not leave the agreed environment. Then test the controls with an administrator and a real user.
- Document storage, extraction, index, model inference, logs and backups: where does each run?
- Who may see each dataset, and do retrieval permissions follow the intended access rules?
- Which external providers and tools receive content or metadata, under which terms?
- How will you handle deletion, incident response, updates and recovery?
- Can the team evaluate answer quality without putting unnecessary personal data into the test?
How DigiRAG fits
DigiRAG combines knowledge search and configurable agents. Depending on the agreed deployment, components can use private infrastructure and compatible local models or external model providers. The right arrangement depends on the documents, performance, budget and operating responsibilities.
For a dataset marked local-only, DigiRAG requires compatible local models for AI processing and excludes that resource from its MCP interface. That is a useful boundary, but it is not a substitute for examining all other components and connections in the chosen deployment.
A good local AI project has an owner
Someone needs to approve the initial sources, remove outdated material, manage access and check failures. Decide who operates the models and infrastructure, who handles an incident and how the business gets its data back if the arrangement changes.
Local deployment can reduce exposure to external processing; it does not make the organisation automatically secure or GDPR compliant. Review the actual processing and contracts with the people responsible for data protection and security.
Questions we often hear
Does private AI mean nothing leaves our premises?
Only if every relevant component and connection is configured that way. Verify inference, extraction, logs, backups and integrations individually.
Can we start with a small team?
Yes. A bounded dataset and a few representative questions make a sensible first pilot; capacity and operating arrangements should match the intended use.
