Before any document is sent to a third-party artificial intelligence service, a single measure takes precedence over all others: pseudonymize the identifying data on the client side, then encrypt the content with keys that you control (BYOK approach). Without this step, you expose your organization to an irreversible leak, because the cloud provider can technically access the transmitted data, index it or use it to improve its models.
Three priority actions to implement immediately:
- Pseudonymize before sending: replace names, contract numbers, SIREN, HR data and any identifiable personal information with neutral identifiers, while retaining the correspondence table on your infrastructure.
- Sign a DPA in compliance with Article 28 GDPR: contractually require a ban on using your data to train models, a certified deletion clause and a right to audit.
- Prioritize a sovereign architecture or on-site deployment for the most sensitive data; only use Enterprise APIs with training deactivation option when written contractual evidence is obtained.
Table of contents
- What legal obligations are imposed on you in France for cloud AI?
- Pseudonymization or anonymization: what to choose for your AI processing?
- Which architectures guarantee control of your data compared to a third-party LLM?
- How to evaluate an AI cloud pseudonymization provider?
- What implementation plan should be planned in the company?
- What AI-specific risks should you anticipate?
- Safe-doc: real-time pseudonymization, zero storage, GDPR compliance
- Key Points
- What legal teams and CIOs often underestimate
- Official sources and references to learn more
What legal obligations are imposed on you in France for cloud AI?
The CNIL and ANSSI explicitly recommend to avoid consumer AI tools to process sensitive internal data. This is not a warning in principle: it is a formal recommendation which engages the responsibility of the data controller in the event of an incident.
Legal responsibility remains that of the data controller: delegating hosting to a third party does not exempt you from the obligation to carry out an AIPD and formalize the contractual clauses. Entrusting data to a cloud provider does not transfer GDPR responsibility - it remains entirely yours.
CNIL recommends carrying out an AIPD (Data Protection Impact Analysis, or DPIA) when an AI system processes personal data in volume or involves automated decision-making. This analysis must map the risks, define the technical and organizational measures adopted, and be documented before going into production.
ANSSI, for its part, recommends favoring qualified SecNumCloud providers and architectures limiting exposure to extraterritorial laws. The American Cloud Act of 2018 may force a provider under American law to deliver data hosted in Europe to American authorities. The physical location of the servers is therefore not enough: the legal nationality of the service provider is just as important. For a complete guide to AI contractual obligations, the practical implications of article 28 are detailed sector by sector.
Pseudonymization or anonymization: what to choose for your AI treatments?
The distinction is legally structuring. Anonymized data no longer allows, by any reasonably available means, to re-identify the person concerned: it falls outside the scope of the GDPR, but often loses any business usefulness. Pseudonymized data remains technically reidentifiable via a protected correspondence table: it remains subject to the GDPR, but allows analytical processing, enrichments and controlled restitution.
For AI uses in business, complete anonymization is rarely practical. Analyzing a contract, processing an HR file or carrying out M&A due diligence requires maintaining the semantic coherence of the document. Pseudonymization is therefore the appropriate compromise: it reduces the risk of exposure while preserving the usefulness of the treatment.
For it to be legally effective, three technical conditions must be met:
- The correspondence table must be encrypted with a recognized algorithm and stored separately from the pseudonymized data.
- Key management must remain under the exclusive control of the data controller (BYOK principle).
- Each access to the correspondence table must be logged and auditable.
Pro tip: Refer to Safe-doc pseudonymization / anonymization comparison to identify the appropriate level of protection for each document type before configuring your AI pipeline.
Which architectures guarantee control of your data compared to a third-party LLM?
The recommended secure flow follows four sequential steps: raw data extraction, pseudonymization in a local gateway (or client-side installed agent), client-side encryption with your own keys, and then minimal sending to the LLM API with the Enterprise Disable Training option enabled.
| Architectural pattern | Level of sovereignty | Typical use case |
|---|---|---|
| On-site deployment (Mistral, LLaMA) | Maximum | Ultra-sensitive data, health or defense sector |
| SecNumCloud sovereign cloud (Scaleway, OVHcloud, Outscale) | High | SMEs and ETIs subject to strict GDPR |
| Enterprise API + BYOK + pseudonymization | Moderate | General productivity with reinforced controls |
| Public API without controls | Low | Avoid for any sensitive document |
The zero-storage pattern deserves particular attention: processing takes place in memory or in continuous flow, without persistent indexing at the provider. Coupled with a storage-less architecture by design, this pattern eliminates the risk of reusing data after the session.
For RAG (recovery augmented generation) architectures, the vector base must be hosted in the European Union, the embeddings encrypted, and the prompts injected only from a controlled layer which has previously pseudonymized the document fragments.
Pro tip: The best operational practice is to implement pseudonymization in a local gateway that processes and encrypts the data before any exchange with an external API, then log each transaction for audit.
How to evaluate an AI cloud pseudonymization provider?
Before signing with a service provider or deploying an internal solution, systematically check these criteria:
1. DPA article 28 signable: the contract must explicitly prohibit the use of data to train models, set a zero or minimum retention period, and provide a certificate of deletion.
2. Incident notification within 24 hours: requiring a notification SLA lower than the legal deadline of 72 hours (article 33 GDPR) leaves you room to prepare your own notification to the CNIL.
3. BYOK and client-side encryption: the CNIL identifies client-side encryption as the most protective approach for privacy in the cloud.
4. Zero storage by design: verify that the architecture does not retain any data after processing, not just by policy, but structurally.
5. Access logs and audit rights: the logs must be accessible to the data controller and exportable for external audit.
6. Verifiable certifications: ISO 27001 over the entire scope of the service, SecNumCloud for the most sensitive data, HDS if the health sector is concerned.
7. Recent penetration test reports: a pentest less than 12 months old, carried out by an independent third party, is a concrete signal of security maturity.
An NDA alone is not enough. A comprehensive GDPR DPA must include training bans, retention periods, audit rights and deletion certificates - without which contractual protection remains incomplete.
What implementation plan should be planned in the company?
| Phase | Indicative duration | Senior Manager |
|---|---|---|
| Data mapping and AIPD | 2-4 weeks | DPO + DSI |
| Pseudonymization prototype (synthetic data) | 2-4 weeks | DSI + project team |
| Gateway and BYOK integration | 4-8 weeks | CIO + CISO |
| Security tests and compliance validation | 2-4 weeks | CISO + DPO |
| Production and monitoring deployment | Continuous | DSI + DPO |
A complete POC is generally completed in 4 to 8 weeks; full production takes 3 to 6 months depending on the complexity of the IS and sectoral requirements. Fictitious or synthetic data is recommended by the CNIL for testing phases, which avoids exposing real data before the controls are validated.
The main cost items to anticipate: adaptation of the existing IS, deployment of a key management module (HSM or BYOK service), external audits and pentests, and sovereign service licenses or on-site infrastructure for models deployed internally.
What risks specific to AI should you anticipate?
Threats specific to AI systems go beyond traditional vulnerabilities. The CNIL file on the security of AI systems identifies several specific categories of attacks:
- Membership inference: an attacker determines whether a specific record was used to train the model, indirectly revealing sensitive data.
- Model inversion (model inversion): by interrogating the model in a targeted manner, it becomes possible to reconstruct training data.
- Data poisoning: injection of corrupted data to bias the model outputs.
- Exfiltration via responses: a poorly configured model can return fragments of confidential documents in its responses to other users.
The countermeasures to be implemented combine data minimization before sending, robust pseudonymization, filtering of model outputs and “red team” type exercises to test the limits of the system. On an organizational level, mastery of Shadow AI involves training teams in AI security, strict limitation of access rights and monitoring of undeclared uses.
Pro tip: In the healthcare and finance sectors, add watermarking to documents before pseudonymization: in the event of a leak, you can identify the source of the compromised document.
Safe-doc: real-time pseudonymization, zero storage, GDPR compliance
Setting up a secure architecture for third-party cloud AI takes time and resources. Safe-doc meets this need with a directly operational approach: the solution pseudonymizes your sensitive documents in real time, before any sending to ChatGPT, Claude or any other LLM, without ever storing the processed data.
Concretely, Safe-doc is inserted as a transparent layer of protection between your teams and the AI tools they already use. Identifying data is replaced on the fly, keys remain in your control (BYOK), and each transaction is logged for audit. For legal departments, DPOs and HR or M&A teams, Safe-doc offers a page dedicated to DPA and compliance commitments as well as a DPO service page to support compliance. Get in touch for a demonstration or proof of concept tailored to your industry.
Key points
Client-side pseudonymization combined with a signed Article 28 DPA constitutes the minimum basis for securing sensitive documents when using a third-party AI cloud.
| Point | Details |
|---|---|
| Pseudonymization before sending | Replace all client-side identifying data, encrypted lookup table under your control. |
| DPA article 28 mandatory | Training ban, certified deletion, right to audit and incident notification within 24 hours. |
| AIPD before production | Map risks and document measures before any AI deployment on personal data. |
| Zero storage architecture | Favor in-memory processing without persistent indexing by the third-party provider. |
| Safe-doc | Real-time pseudonymization, zero storage by design, BYOK and logging for GDPR compliance. |
What legal teams and CIOs often underestimate
Most organizations approach cloud AI data security as a technical problem to be solved once and for all. This is a framing error. The risk surface evolves with each new model, each update of a supplier's conditions of use, each new business use case. Compliance is not a steady state: it is an ongoing process.
What I recommend to teams just starting out: don't look for the perfect solution before taking action. Start with high-risk processes, those where HR, health or M&A data is already passing through undeclared AI tools. The pseudonymization of these flows as a priority immediately reduces the exposure surface, even if the rest of the IS is not yet secure. Generalizing then is much simpler when a first scope is mastered and documented.
The combination of technical measures and solid contractualization (complete DPA, not just an NDA) is what really reduces the legal surface area in the event of an incident. One without the other leaves blind spots that neither your insurer nor the CNIL will ignore.
Official sources and references to learn more
- Security recommendations for a generative AI system (ANSSI/CNIL): reference guide on confidentiality, integrity and availability controls for AI systems.
- CNIL recommendations for companies considering cloud services: contractual checklist and security requirements for the public cloud.
- Development of AI systems: CNIL recommendations: AIPD framework and obligations of the data controller.
- CNIL file on the security of AI systems: analysis of threats specific to AI (inference, inversion, poisoning).
- Cloud encryption practices (CNIL): recommendations on client-side encryption, BYOK and synthetic data.
- Security and architecture Safe-doc: technical documentation on the zero storage policy and security guarantees.
- GDPR and legal data processing: guide on AIPD, DPAs and sectoral compliance.
- Safe-doc for legal departments: use cases and implementation for legal teams and DPOs.
This article is general information and does not constitute legal advice. For your specific situation, consult your DPO or a specialist data law advisor.