GDPR and artificial intelligence: why your company needs private AI
How to comply with GDPR and the AI Act when using artificial intelligence in your business, and why on-premise AI is the safest solution for sensitive data.
GDPR and artificial intelligence: why your company needs private AI
Generative artificial intelligence has arrived in Spanish companies at a speed that has left legal and compliance departments behind. The team already uses ChatGPT to draft emails, the salesperson pastes proposals into Claude, the developer asks Copilot about code. Nobody asks where that data goes.
The answer is uncomfortable: to servers in the United States, managed by companies subject to US law.
The legal problem many companies ignore
The General Data Protection Regulation (GDPR) does not prohibit using cloud services or sending data to the US. But it does establish strict requirements:
- Legal basis for processing: you need a legal reason to process personal data
- Guarantees on international transfers: if data goes outside the EEA, you must ensure equivalent protection (standard contractual clauses, adequacy decisions, etc.)
- Information to data subjects: data owners must know their data is being processed with AI
- Data minimisation: only process strictly necessary data
The problem is that when an employee pastes a client email into ChatGPT, they are making an international transfer of personal data without a documented legal basis, without informing the client, and using more data than necessary.
The potential fine: up to 4% of global annual turnover or €20 million (whichever is higher).
What GDPR requires when using AI
When your company uses AI to process personal data, the GDPR applies in full. Specifically:
You are the data controller: even if you use a third-party tool, you are responsible for ensuring the processing is lawful.
You need a data processing agreement (DPA): with every AI provider that processes personal data on your behalf.
You may need a Data Protection Impact Assessment (DPIA): for high-risk processing, including systematic profiling and large-scale processing of special categories of data.
Data subjects have rights: access, rectification, erasure, portability — rights you must be able to honour, including for data processed by external AI.
The European AI Act: new obligations since 2025
The EU AI Act has been fully in force since early 2025 and adds specific obligations:
High-risk AI systems
If your company uses AI to make decisions affecting people (hiring, credit, essential service provision), you're dealing with a high-risk system with additional obligations:
- Prior conformity assessment
- Registration in the EU database
- Documented human oversight
- Training data audit
Transparency
If you interact with AI that could be confused with a human (customer service chatbots), you must inform users.
Absolute prohibitions
The AI Act directly prohibits some AI uses: social scoring systems by governments, real-time biometrics in public spaces without judicial authorisation, subliminal manipulation.
Why on-premise AI solves these problems
The most direct solution to GDPR + AI Act compliance is not transferring data. If the model runs on your servers, data never leaves your company.
International transfers: problem eliminated
With on-premise AI there is no international data transfer. Data enters, the model processes it, the response comes out. Everything within your perimeter. You don't need adequacy decisions, standard contractual clauses, or transfer impact assessments.
Full control of processing
When the model is yours, you can document exactly:
- What data the system processes
- For what purpose
- Who has access
- How long logs are retained
This documentation is exactly what GDPR requires for the records of processing activities and what the AI Act requires for high-risk systems.
Real data minimisation
You can configure the system not to log conversations, to automatically delete logs, or to directly exclude certain types of data. With a SaaS service, you have to trust the provider's policies.
Right to erasure and portability
If a client requests data deletion, with on-premise AI you can guarantee no traces remain in external systems. With cloud services, you depend on the provider's deletion process.
Sectors with special obligations
Some sectors have additional obligations that make on-premise AI practically mandatory:
Healthcare sector (special category data)
Health data is special category data with enhanced protection under GDPR. A hospital, clinic, or insurer that uses AI with clinical data must guarantee that data doesn't leave the controlled perimeter. On-premise AI is the only practical way to achieve this.
Legal and notarial sector
The information handled by a law firm is protected by legal professional privilege. Sending that information to third-party APIs, even for document analysis, can violate that privilege. The solution is AI deployed on the firm's infrastructure.
Financial sector
Client financial data is subject to additional regulation (DORA, operational resilience directives). Auditors and regulators are increasingly asking about AI data processing.
Public sector
The public sector has specific restrictions on data sovereignty. On-premise AI or in European sovereign clouds is the only model compatible with many public procurement specifications.
Data Protection Impact Assessment (DPIA)
When AI data processing is "high risk" (systematic assessment, large-scale processing of special categories, systematic monitoring), GDPR requires a Data Protection Impact Assessment (DPIA) before starting.
A DPIA for an on-premise AI system is much simpler than for an external SaaS service, because:
- Threats are local and controllable
- Data flows are known
- Security measures are implemented directly by the company
How to transition from public AI to private AI
Step 1: Audit current usage Identify what AI tools the team is using, with what data, and for what purposes.
Step 2: Data classification Separate what data is sensitive (personal, confidential, strategic) from what isn't. Not everything needs on-premise AI.
Step 3: Define priority use cases Start with cases where AI delivers the most value and data risk is highest.
Step 4: Deploy the solution Configure the model, interface, and integrations in the controlled environment.
Step 5: Training and internal policy Define what data can and cannot be used with AI, and train the team.
Conclusion: compliance as competitive advantage
Companies that have private AI and can demonstrate GDPR + AI Act compliance have a real competitive advantage in regulated sectors: they can attract clients for whom confidentiality is a vendor selection requirement.
In a market where privacy matters — and in Spain it matters more every day — having your house in order isn't just about avoiding fines. It's a sales argument.