Using AI Responsibly with Personal Data

Artificial intelligence is no longer a distant prospect for most organizations. It drafts content, flags unusual activity, ranks applications, and supports teams handling large volumes of work. When AI processes personal data, and especially when its output affects people's education, employment, or access to services, the familiar data protection responsibilities still apply. Fairness, accountability, and the ability to explain a decision do not disappear because a machine was involved. What changes is how organizations meet them.

Where AI Touches Personal Data

AI now appears at many stages of everyday processing:

• Drafting and summarizing documents, communications, and other content.
• Detecting anomalies, such as unusual login behavior, potential fraud, or irregular patterns in submitted work.
• Scoring and ranking, for example screening job applications or evaluating tests.

Two principles hold across all of these uses. First, the tool may speed up the process, but responsibility for the result stays with the organization. If AI-drafted content is wrong or unfair, the organization answers for it. Second, an AI flag is a signal, not a conclusion. It should prompt a human review, never an automatic finding against a person.

The Main Risks

Data exposure. Organizations need clear answers to basic questions: where does the data go, who can access it, and is it used to train the provider's model? The European Data Protection Board (EDPB) has made clear in Opinion 28/2024 that AI models trained on personal data are not automatically anonymous. The bar for treating a model as anonymous is high.

Inaccuracy. AI can produce information that sounds convincing but is wrong. The GDPR's accuracy principle (Article 5(1)(d)) still applies, and the fact that a system generated an error does not shift responsibility away from the organization using it.

Bias. AI systems learn from existing data, which may contain bias. This can affect how content is generated, how people are scored, or which behavior is flagged as unusual, with a real risk of unequal outcomes for different groups.

Opacity. People may need to know what role AI played in a decision about them and how to challenge it. Under GDPR Article 22, individuals have specific rights where decisions with legal or similarly significant effects are based solely on automated processing, including the right to obtain human intervention, to express their point of view, and to contest the decision.

Overreliance. The more useful and efficient a system becomes, the easier it is to stop questioning its output. A tool designed to support people can quietly turn into a decision-maker that receives too little scrutiny.

Using AI Responsibly

1. Assess before deployment. Identify who could be affected, what data the system will process, and what safeguards are needed. Where processing is likely to result in a high risk to individuals, a Data Protection Impact Assessment (DPIA) under GDPR Article 35 is required. If key questions about data flows cannot be answered, the system is not ready to use.

2. Keep people accountable. Important decisions should remain with people who have the authority and the information to question unusual results, catch errors, and consider circumstances a system may not understand. For high-risk AI systems, the EU AI Act also sets out explicit human oversight requirements (Article 14).

3. Choose vendors carefully. Outsourcing the technology does not outsource accountability. Contracts with AI providers should address security, data location, access, the right to audit, and whether data may be used for model training, alongside the processor requirements in GDPR Article 28.

4. Be open about AI use. People should be told when AI is used and, where relevant, how it affects them. Since 2 August 2026, Article 50 of the AI Act has required transparency in specific situations, such as when people interact directly with an AI system or are exposed to emotion recognition. The European Commission has published guidelines on these obligations.

Conclusion

AI brings risks that data protection law takes seriously: exposure of personal data, confident but inaccurate output, hidden bias, opaque decisions, and the temptation to stop checking a dependable tool. In each case, the organization remains responsible for the outcome. Assessing risks before deployment, knowing where data goes, keeping people in charge of decisions that matter, choosing vendors with care, and being transparent about AI use allow organizations to innovate while keeping the trust of the people whose data they process.