For years, the development of Artificial Intelligence systems in Europe has been dominated by a single, overriding question regarding data: "Did we get consent?" While essential, a consent-only approach is often impractical for the large-scale datasets required to train effective AI models. Now, a pivotal set of recommendations from France's influential data protection authority, the CNIL, has provided much-needed clarity, confirming a more flexible yet rigorous path forward for innovators. As the CNIL often sets the tone for data protection interpretation across the Union, this French-specific guidance offers a critical blueprint for any organization developing AI for the EU market.

In line with the European Data Protection Board (EDPB), the CNIL states that developing AI systems does not necessarily require consent. "Legitimate Interest" (LI), one of the six legal bases under GDPR, is a valid legal foundation for developing AI systems, provided that strong, demonstrable safeguards are in place. This guidance doesn't create a loophole; it establishes a stringent, three-part test that every organization must pass if it wishes to rely on LI for AI development.

The Three-Part Test for Legitimate Interest: A Deeper Dive

To depend on Legitimate Interest as your legal basis, you must satisfy three cumulative conditions. This isn't a checklist to be rushed; it's a framework for deep, documented analysis and risk assessment, often forming a key part of a Data Protection Impact Assessment (DPIA).

• 1. The Purpose Test: Is Your Interest Genuinely Legitimate?

  First, the interest your organization is pursuing must be lawful, specific, real, and present. Vague, hypothetical goals like "providing new services" are insufficient. Your purpose must be clearly defined. The CNIL provides examples of interests that are considered legitimate a priori: scientific research, improving existing services, fraud detection, and even commercial interests, as long as they are not contrary to law. However, this test also serves as a critical gatekeeper. For example, since the Digital Services Act (DSA) prohibits profiling minors for targeted ads, developing an AI for this purpose would fail the test, as the underlying interest is not lawful from the outset. Your interest must align with the broader legal framework, not just GDPR.

• 2. The Necessity Test: Is This Processing Truly Required?

  Next, you must prove that processing personal data is necessary to achieve your stated interest. This involves two key considerations. First, you must exhaust less intrusive alternatives. Can the same goal be achieved with fully anonymized or synthetic data? If so, processing personal data is not necessary. Second, this test is intrinsically linked to the principles of data minimization and proportionality. You must justify the volume and nature of the data you are using. This means you can't collect everything just in case; you must demonstrate why *this specific data* is required and how you are taking into account technological developments that may allow for training effective models with less data.

• 3. The Balancing Test: Do Individual Rights Outweigh Your Interest?

  This is the most critical and context-dependent condition. The processing must not disproportionately infringe on the fundamental rights and freedoms of the individuals whose data is being used. This requires a documented balancing act, weighing your interests against potential harms. Key factors to consider include:
  • The benefits of the AI system: The greater the societal value—such as improved healthcare accessibility, significant scientific breakthroughs, or robust fraud prevention—the more weight your interest carries.
  • The reasonable expectations of individuals: People should not be surprised by the processing. The CNIL is clear: using private chatbot conversations to train a new AI model would likely exceed reasonable expectations, requiring explicit consent. In contrast, using strongly pseudonymized public forum data to improve a summarization tool, with clear user controls and an easy opt-out, might be considered acceptable. For web scraping, if a site uses `robots.txt` to prohibit harvesting, ignoring this signal means you cannot meet the reasonable expectations of users.
  • The spectrum of risks to individuals: You must assess all potential harms: risks to privacy (e.g., data regurgitation), security (e.g., model inversion attacks), reputation (e.g., generation of false information), and ethics (e.g., discrimination, bias). The indiscriminate scraping of sensitive images from across the web for a generative AI model, without robust safeguards, would clearly fail this balancing test.

Practical Implications: Transparency and Safeguards are Non-Negotiable

The CNIL's recommendations demand a new level of transparency from AI developers. Even if the precise final use of a general-purpose AI model is not yet known, the organization must clearly state the objectives of its development—whether it's commercial, scientific, internal, or external. This information must be brought to the attention of individuals as part of your GDPR transparency obligations.

Furthermore, the guidance highlights the need for robust safeguards as part of the balancing act. These are not optional extras; they are required to tip the scales in favor of your legitimate interest. These can include:

• Technical Measures: Promptly anonymizing or pseudonymizing collected data, using synthetic data where possible, and implementing advanced measures to limit data memorization and regurgitation in generative models (e.g., differential privacy, model regularization).
• Organizational Measures: Providing users with a clear, simple, and discretionary way to object to the processing of their data (the right to object), especially in cases of web scraping. For high-risk projects, establishing an ethics committee is recommended.
• Legal Measures: Contractually prohibiting unlawful or unethical uses of your AI system when it is shared with third parties, and creating clear terms of service that outline these restrictions.

Conclusion: A Path Forward Based on Responsibility and a French Precedent

The CNIL has provided a clear, albeit challenging, roadmap that is likely to influence data protection authorities across the EU. Legitimate Interest offers a viable alternative to consent for AI development, but it is not an easy way out. It demands a mature, responsible, and meticulously documented approach to data protection. Organizations must now move beyond simply asking "can we use this data?" to rigorously justifying "should we use this data, and have we done everything possible to protect the people it belongs to?"

This French guidance is a call for a deeper integration of data ethics and privacy-by-design principles into the very core of AI development. The companies that thrive in this new regulatory landscape will be those that embrace this responsibility, not as a compliance burden, but as a cornerstone of building trustworthy and sustainable AI.