Artificial intelligence is already transforming legal research, drafting, document analysis and the organisation of legal work. But as soon as an AI tool processes personal data, it raises a central question: how can its productivity gains be captured without weakening data protection, confidentiality and individual rights?
For companies, law firms and legal departments, the issue goes beyond choosing a piece of software. It requires understanding what data is processed, for what purpose, with what security guarantees, what rights individuals have, and what responsibility falls on the organisation. Silex helps legal professionals analyse these requirements, structure their research and produce verifiable answers, while keeping the legal decision in the user's hands.
Why AI changes the way compliance is managed
An artificial intelligence system can come into play at several stages: data collection, model training, document analysis, generating responses, decision support, user monitoring or the automation of internal tasks. As soon as a person can be identified, directly or indirectly, the GDPR becomes relevant.
This reality concerns generative assistants, HR tools, chatbots, SaaS solutions, internal search engines, scoring systems and platforms that analyse client documents. AI does not create an exception zone. On the contrary, it makes data governance even more important, since processing can be opaque, evolving or difficult to explain.
For legal teams, the challenge is therefore to treat AI as a governance topic, in the same way as AI-assisted legal research.
GDPR and the AI Act: two frameworks to read together
The GDPR governs the processing of personal data. The European AI Act, for its part, regulates artificial intelligence systems according to their level of risk. The two frameworks do not replace one another: they overlap whenever AI processes personal data.
- GDPR
- Main purpose: Protection of personal data and individual rights.
- Key question: Does the system process personal data?
- AI Act
- Main purpose: Regulation of AI systems by risk level.
- Key question: Is the system prohibited, high-risk, subject to transparency obligations, or minimal risk?
- Data protection authorities
- Main purpose: Practical guidance, oversight and impact assessments.
- Key question: Does the processing require a DPIA or additional safeguards?
The AI Act, which entered into force in August 2024, applies in stages: its transparency obligations became applicable in August 2026, while the strictest rules for high-risk systems will not take effect until December 2027, or August 2028 for systems embedded in products already subject to regulation. A company should therefore place each AI system within this timeline before drawing definitive conclusions about its obligations.
The European Commission notes that certain breaches of the AI Act can result in fines of up to 35 million euros or 7% of worldwide annual turnover. The GDPR, for its part, provides for fines of up to 20 million euros or 4% of worldwide annual turnover.
Questions to ask before using an AI tool
Before deploying an artificial intelligence solution, it is essential to start from actual use cases. A company does not assess a tool that rephrases public content in the same way as an assistant that analyses client contracts, an HR system, or a platform capable of querying internal documents.
- What data is processed? Personal data, sensitive data, confidential documents, HR data, client information or trade secrets.
- Why is it processed? Research, summarisation, classification, analysis, decision support, text generation or process automation.
- Where is it hosted? In Switzerland, the European Union or a third country, with or without transfer safeguards.
- Who can access it? Internal users, the provider, subcontractors, support teams or technical vendors.
- Is the data used to train the model? This question must be clearly addressed in the contractual and technical terms.
- Is the output verifiable? A generated answer must be capable of being checked, especially when it influences a legal or operational decision.
For legal departments looking to connect these questions to business use cases, the AI for business page details the use cases specific to in-house teams.
GDPR principles to apply to artificial intelligence
The principles of the GDPR remain the same, but applying them becomes more delicate when the tool relies on complex models. An AI project should therefore be documented from the outset, not only once it goes into production.
- Purpose limitation: precisely define the purpose of the processing and avoid unplanned secondary uses.
- Legal basis: identify the applicable ground: contract, legal obligation, legitimate interest, consent or another basis provided by law.
- Data minimisation: limit the data used to what is genuinely necessary.
- Transparency: explain to individuals how their data is used and what consequences this may have.
- Security: protect data through appropriate technical and organisational measures.
- Individual rights: provide for access, rectification, objection, erasure and challenges to automated decisions.
- Privacy by design: embed confidentiality and security requirements from the earliest stages of the project.
The DPIA: an essential tool for high-risk projects
A data protection impact assessment, or DPIA, makes it possible to evaluate the risks a processing activity poses to individuals' rights and freedoms. The CNIL notes that it is mandatory whenever a processing activity is likely to create a high risk. AI projects can quickly fall into this category, particularly when they involve profiling, large volumes of data, sensitive data or automated decision-making.
A useful DPIA is not just a form to fill in. It must describe the processing, explain why it is necessary, identify the risks, provide for mitigation measures and organise ongoing monitoring. If the residual risk remains high, prior consultation with the competent authority may be required.
Generative AI: the sensitive issue of confidential data
Generative AI raises particular risks: hallucinations, lack of traceability, prompt reuse, exposure of confidential documents, disclosure of sensitive information, or difficulty erasing certain data. Within an organisation, these risks go beyond the privacy policy. They touch on governance, vendor contracts, internal rules and team training.
Legal departments must therefore decide which data can be used, which data must remain excluded, which tools are authorised and which outputs must be reviewed by a professional. This discipline matters even more in regulated sectors, or when documents contain client, HR, medical, financial or strategic data.
The Silex security page details Silex's approach: Swiss hosting, confidentiality, encryption, zero training on client data, and infrastructure designed for legal professionals.
Building AI governance that actually works
Compliance should not be treated as a formality bolted on at the end of a project. It should help the company know which tools it uses, which risks it accepts and which rules it imposes on its teams.
- Map the tools: official solutions, business uses, one-off trials, extensions and embedded assistants.
- Classify the use cases: research, drafting, document analysis, decision-making, HR, customer support or compliance.
- Frame vendor relationships: hosting, subcontracting, security, audit rights, reversibility and no training on entrusted data.
- Set internal rules: prohibited data, authorised tools, human validation, retention and incident procedures.
- Train teams: confidentiality, model limitations, hallucinations, output verification and good prompting practices.
- Document the choices: register, DPIA, risk assessments, tests, trade-offs and evidence of information provided.
Legal teams can rely on legal analysis to compare requirements, prepare an internal memo, review a vendor's clauses or scope an AI project.
How Silex helps legal professionals
Silex helps lawyers, in-house counsel and legal departments work faster on legal texts, recommendations, vendor contracts, internal policies and compliance documents. The platform does not replace legal analysis: it makes research easier, structures sources and helps prepare a verifiable working basis.
To discover the features, visit the Silex product page. To evaluate the tool on a use case related to the GDPR, the AI Act or data governance, you can book a demo.
If you are torn between a general-purpose tool and a specialised legal platform, the ChatGPT vs Silex comparison explains why sources, method and security matter as much as a fluent answer.
FAQ: AI, GDPR and data protection
Does the GDPR apply to all AI tools?
The GDPR applies as soon as an AI tool processes personal data. If no person can be identified directly or indirectly, the GDPR may not apply, but other obligations, particularly under the AI Act, may still be relevant.
What is the difference between the 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. In many projects, the two frameworks must be applied together.
Is a DPIA necessary for an AI project?
It is necessary whenever the processing presents a high risk to individuals' rights and freedoms. This is often the case with profiling, sensitive data, large-scale processing or automated decision-making.
Can confidential documents be sent to a general-purpose AI tool?
This should be avoided unless the environment is contractually and technically controlled. Legal, HR, financial or client documents may contain personal data, trade secrets or information subject to confidentiality obligations.
Is Silex suitable for AI, GDPR and compliance topics?
Yes. Silex helps legal professionals research, analyse and structure answers on legal texts, sources, contracts and compliance documents, within an environment designed for legal requirements.
