Consent vs. Legitimate Interest for AI Which Works Where (and why)?

Artificial Intelligence runs on data, lots of data. But what happens when that data includes personal information? Regulators worldwide have made it clear: you need a lawful basis to process personal data, even when training AI models. This has set up a dilemma between two key options under GDPR: consent or legitimate interest. A recent example put this debate in the spotlight. In May 2025, Meta announced it would train its AI on Europeans public Facebook and Instagram data without asking users first, relying on “legitimate interest” instead. The backlash was immediate, privacy advocates filed complaints across 11 countries, arguing Meta should have obtained explicit opt-in consent rather than just offering an opt-out. Clearly, even tech giants are struggling with the question: Consent vs. Legitimate Interest: which works where, and why?

What is consent?

Under GDPR, consent means the individual has given you explicit permission to use their personal data for a specific purpose. It is the gold standard of user control, the person actively agrees (e.g., ticks an opt-in box or clicks “Allow”) after being fully informed about what you will do with their data. Importantly, consent must be freely given, specific, informed, and unambiguous, or it is not valid. In simple terms, the user should understand what they are agreeing to and make a genuine choice (no pre-ticked boxes, no sneaky tactics). And once given, consent is never forever; people can withdraw it at any time, and you must stop that data use if they do.

Why do Privacy Professionals Favor Consent?

It puts individuals in the driver’s seat. For Data Privacy Managers, consent feels safe because it is explicit proof that the person is okay with the processing. If challenged, you can show a record of John Doe clicking “Yes, use my data to improve AI services”. That transparency builds trust and reduces legal risk: using data without asking, only to have users object later, can lead to complaints or fines. Indeed, regulators often call consent the “safer choice”, relying on the wrong basis (when you should have gotten consent) could invalidate your processing and bring serious consequences.

The Downside of Consent for AI

While consent is ideal in theory, in practice, it can be extremely cumbersome for certain AI applications. Imagine trying to get millions of people’s permission to scrape the open web for training data, which is practically impossible. Or consider an AI system that learns from historical records: tracking down every individual to sign a consent form would halt the project. Even when you can ask, many might ignore the request or say no, potentially skewing your dataset. And if someone who consented later revokes it, you face the headache of extracting their data from your AI model or retraining it. In short, a consent-only approach can make data processing hugely burdensome for controllers. It is no surprise that AI Developers and Engineers often find that consent “does not scale” well for big data needs.

GDPR recognizes that in some cases, an organization may have a valid legitimate interest to use personal data without asking each individual for consent. In fact, legitimate interest is known as “the most flexible” legal basis under GDPR. It lets you process data if doing so is genuinely necessary for a legitimate purpose of your company (or a third party), provided you are not overriding the individuals’ rights and freedoms. Unlike consent, you do not need to obtain a direct “yes” from users, but you do need to meet a high bar behind the scenes. The UK’s ICO (Information Commissioner’s Office) and other regulators insist on a three-part test for legitimate interest:

• Purpose Test: Do you have a clear, specific, legitimate purpose for this data use?
• Necessity Test: Is the processing necessary for that purpose, or could you achieve it in a less intrusive way?
• Balancing Test: Do your interests outweigh the individual’s privacy rights? In other words, would the average person consider this use acceptable, and are any potential impacts on them minimized?

Only if all three answers line up in your favor can you confidently rely on legitimate interests. It is not a free pass; it is a careful balancing act. Essentially, the burden is on you to prove that using the data without consent is fair and reasonable.

Why Consider Legitimate Interest in AI?

For many businesses, legitimate interest is the go-to basis if it applies, with consent used only if it does not. This is especially true in AI development, where getting consent from everyone in a large dataset may be impractical. Legitimate interest can cover data uses that are part of your ordinary business operations and beneficial to you (and even to users) in a specific way. For example, if an AI security system analyzes network logs to detect cyber threats, you likely have a strong legitimate interest in protecting your infrastructure, and users benefit from a safer service. In fact, the European Data Protection Board (EDPB) explicitly noted that using AI to improve cybersecurity or detect fraud can fall under legitimate interests “but only if the processing is shown to be strictly necessary and the balancing of rights is respected.” In other words, some AI uses (like fighting spam or enhancing safety) are broadly expected and welcomed, making legitimate interest a natural fit.

Consent vs. Legitimate Interest in AI: Which Works Where (and Why)?

How do you decide between these two legal bases for an AI project? It ultimately boils down to the nature of your data use, the risk to individuals, and the context. Let’s compare scenarios to illustrate which works where:

• When Consent Shines: If your AI use of data is highly intrusive, unexpected, or involves sensitive personal data, leaning on consent is wiser. For example, training an AI on users’ private communications or sensitive health records absolutely should require their explicit consent; it is too personal to assume a free hand. Many authorities say certain profiling or big data uses demand consent because they significantly impact privacy. Also, if individuals would not reasonably anticipate their data being used in a particular AI system, consent is the safer road. The recent complaints against Meta underscore this: scraping millions of social media profiles to train a generative AI was viewed by many as crossing a line that should require opt-in approval. In short, use consent when in doubt or when transparency and user choice are paramount. It might slow down your project (getting all those opt-ins), but it also bulletproofs it against legal challenges.
• When Legitimate Interest Works: If your AI processing is low privacy-impact, beneficial, and there’s no practical way to get consent, legitimate interest might be appropriate. Consider AI systems embedded in cybersecurity, fraud prevention, quality control, or research for the public good. These are cases where the benefit is clear and the privacy intrusion limited, and users generally expect organizations to do this behind the scenes. For example, an AI that monitors network traffic for hacking attempts can likely rely on legitimate interest; users want security, and asking each user “Do you consent to letting us check server logs for attacks?” Would be overkill. Likewise, improving a voice assistant’s language model using anonymized recordings could fall under a legitimate interest if done in a privacy-preserving way. Regulators have acknowledged that “AI models can be properly trained using personal data” under legitimate interest, as long as rigorous necessity and balancing checks are in place. The EDPB’s 2024 Opinion even gave a green light in principle to legitimate interest for AI training and deployment, marking an important step toward legal certainty for companies.

Bottom line: Legitimate interest is fitting where the processing is something users benefit from or expect, and you can demonstrate minimal privacy risk.

• Gray Areas: Tread Carefully: Not everything is black-and-white. Some AI applications sit in a gray zone. For example, using publicly available data to train a broad AI model: the data is public, but do people expect AI companies to harvest and learn from it? Probably not always. In these cases, you must do a careful case-by-case analysis. If you choose legitimate interest, strengthen your measures (more on that next). If you choose consent, be prepared for lower data availability. Always ask: How would I feel if this were my data? If the answer is uneasy, consider asking for consent upfront or not using that data. Remember, if you start with consent and people decline, you cannot then sneakily switch and claim legitimate interest for the same purpose; that’s against the rules. You have to pick the right basis from the start; you can not have it both ways.

How Can You Build Trust in AI While Staying Compliant?

Navigating consent vs. legitimate interest for AI ultimately comes down to striking the right balance. On one side lies the individual’s fundamental right to privacy and control over their data. On the other hand, the drive to innovate and extract meaningful value from data fuels business growth and societal advancement.

GDPR does not prohibit the use of personal data in AI; it simply demands accountability and justification. Use consent when you need a clear “yes” and a high degree of user trust. Use legitimate interest, carefully and responsibly, when consent is not practical, but your purpose is fair, transparent, and proportionate.

Ready to Master AI Governance and Make the Right Decisions?

Understanding when to use consent vs. legitimate interest is not just theoretical; it is a critical skill for modern cybersecurity and AI professionals.

That’s where InfosecTrain’s AIGP (Artificial Intelligence Governance Professional) Training comes in.

With this program, you will learn how to:

• Evaluate the right legal basis for AI data processing
• Conduct AI risk assessments and implement safeguards
• Align AI systems with GDPR, global regulations, and governance frameworks
• Build transparent, accountable, and compliant AI solutions

Do not just build AI; govern it with confidence and clarity.
Do not just meet compliance; lead with trust and responsibility.

Enroll in InfosecTrain’s AIGP Training today and become the professional who can balance innovation with privacy in the age of AI.

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