AI GDPR: using artificial intelligence without weakening data protection

AI GDPR refers to the rules, checks and controls that must be put in place whenever an artificial intelligence system processes personal data. For companies, the issue is not only adopting new tools: it is also controlling data collection, purpose limitation, transparency, security, individual rights and model-related risks.

With the General Data Protection Regulation, the European AI Act and guidance from data protection authorities, AI compliance has become a strategic legal topic. Silex helps legal professionals and in-house teams analyse applicable requirements, structure sources and prepare reliable answers without giving up human supervision.

Why AI raises a specific GDPR issue

Artificial intelligence can process personal data at several stages: building a training dataset, training a model, enriching data, inference, generating responses, analysing documents, monitoring users or making automated decisions. As soon as a person can be identified directly or indirectly, data protection rules apply.

This applies to internal tools, SaaS products, generative assistants, scoring systems, chatbots, HR tools, document search models and platforms that analyse client documents. The fact that a system uses AI does not create an exception to the GDPR. It often increases the need for transparency, minimisation, control and documentation.

For legal teams looking at these questions, the AI for lawyers page provides a broader view of how legal professionals can use AI responsibly.

GDPR and AI Act: two complementary frameworks

The GDPR governs the processing of personal data. The Artificial Intelligence Act regulates AI systems according to their level of risk. The two frameworks do not replace one another: they apply together when an AI system processes personal data and also falls within the AI Act.

The European Commission explains that the AI Act defines several risk levels and provides for fines of up to 35 million euros or 7% of worldwide annual turnover for certain infringements. Under the GDPR, fines can reach 20 million euros or 4% of worldwide annual turnover.

Official sources: European Commission on the AI Act ; European Commission on GDPR sanctions.

GDPR principles to apply to AI systems

GDPR compliance for an artificial intelligence system is based on familiar principles, but applying them becomes more demanding when models are complex, evolving or difficult to explain.

• Purpose limitation: clearly define why data is processed and avoid unplanned secondary uses.

• Legal basis: identify the appropriate ground, such as contract, legal obligation, legitimate interest or consent.

• Data minimisation: limit data collection to what is necessary for the AI use case.

• Transparency: inform individuals about AI use, general logic and possible consequences.

• Security: protect data with appropriate technical and organisational measures.

• Individual rights: organise access, rectification, objection, erasure and challenges to automated decisions.

• Privacy by design: embed data protection at design, selection and deployment stages.

The Silex security page explains confidentiality, Swiss hosting, encryption and no training on client data.

Data protection impact assessment: when is it required?

A data protection impact assessment, or DPIA, is a central tool when processing is likely to result in a high risk to the rights and freedoms of individuals. AI systems may quickly fall into this category, especially when they involve innovative technologies, large datasets, profiling, sensitive data or automated decisions.

For an AI project, the DPIA should not be an isolated administrative document. It should describe the processing, identify risks, assess necessity and proportionality, document mitigation measures and provide for ongoing monitoring. If the residual risk remains high, prior consultation with the competent authority may be required.

Source: CNIL on impact assessments for AI systems.

What companies should check before deploying AI

A company adopting an AI tool should avoid two mistakes: treating compliance as a purely legal obstacle, or assuming that the provider takes care of everything. In practice, responsibility depends on roles: controller, processor, AI provider, deployer, internal user or joint controllers.

1. Map AI systems: official tools, business uses, pilots, extensions and embedded assistants.

2. Qualify data: personal data, sensitive data, client data, HR data and trade secrets.

3. Identify the AI Act risk level: prohibited, high-risk, transparency obligation or minimal risk.

4. Review vendor contracts: hosting, subprocessors, reuse of data, security, audit and reversibility.

5. Define internal rules: authorised tools, prohibited data, human validation and incident procedures.

6. Train teams: prompts, confidentiality, limits of generative models and result verification.

7. Document compliance: records, DPIA, tests, decisions, controls and transparency evidence.

The Silex product page presents features for legal research, document analysis and structured work with reliable sources.

Generative AI, client data and privacy

Generative AI raises specific risks: memorisation, regurgitation of data, hallucinations, bias, lack of traceability, difficult erasure, reuse of prompts, transfers outside Europe or exposure of confidential documents. For a company, privacy is not just a policy. It is operational governance.

Legal teams must decide which data can be submitted to a model, which data must be excluded, which outputs need verification and how to respond to data subjects. In regulated sectors, these requirements may be even stricter.

Sanctions: why the risk is also financial

GDPR and AI Act risk is not limited to reputation. The GDPR provides for administrative fines of up to 20 million euros or 4% of worldwide turnover. The AI Act provides, for certain infringements, fines of up to 35 million euros or 7% of worldwide annual turnover.

These amounts show that AI compliance is a board-level topic. Procurement, IT, legal, compliance, HR, marketing and business teams need to work together. A poorly governed technology can create legal, financial and operational risk.

How Silex helps with AI, GDPR and compliance topics

Silex helps legal professionals, lawyers and in-house teams analyse legal texts, recommendations, vendor contracts, internal policies and compliance documents faster. The tool does not replace legal judgment: it structures research, supports analysis and helps produce a verifiable working basis.

To assess Silex on a GDPR, AI Act or data governance use case, teams can book a demo. If you are comparing a general AI tool with a specialised legal platform, ChatGPT vs Silex explains why sources, method and security matter as much as fluent answers.

FAQ: AI and GDPR

Does the GDPR apply to all AI systems?

The GDPR applies whenever an AI system processes personal data. If no identifiable person is involved, the GDPR may not apply, but other rules such as the AI Act may still be relevant.

What is the difference between GDPR and the AI Act?

The GDPR protects personal data and individual rights. The AI Act regulates artificial intelligence systems according to their level of risk. The two frameworks are complementary.

Is a DPIA mandatory for an AI project?

It is mandatory when processing is likely to create a high risk for rights and freedoms. Many AI projects may fall into this category, especially with profiling, sensitive data, large-scale processing or automated decisions.

What data should not be sent to a general AI tool?

Client data, sensitive data, confidential documents, HR data, trade secrets and identifying information should only be processed in contractually and technically controlled environments.

Is Silex suitable for AI, GDPR and compliance work?

Yes. Silex helps legal professionals research, analyse and structure answers on legal texts, sources, contracts and compliance documents, with a secure approach designed for legal work.