Comment mener une analyse d’impact sur la protection des données

Start by checking if your AI project could risk individual rights and freedoms. Look at the data's nature, scope, context, and purpose, and think about how it might impact privacy, security, and discrimination. For example, an AI tool in healthcare analyzing patient data without a DPIA might breach patient confidentiality. When in doubt, play it safe and do a DPIA. This step helps you stay compliant and build trust.

Next, give a clear, detailed rundown of your data processing activities. Include the source, type, and amount of personal data, the purpose and legal basis for processing, who the data subjects and recipients are, how long you'll keep the data, and the security measures in place. Also, explain how your AI system works, including the algorithms and models used and how they're trained, tested, and validated. This transparency helps ensure compliance and builds trust.

Evaluate if the data processing is necessary and proportionate for your AI project's goals. Start by defining your objectives clearly. Then, check if there are less intrusive ways to achieve the same results or measures to reduce risks to data subjects. For instance, an AI project analyzing customer behavior might use anonymized data instead of personal data to minimize risks. Ensure your processing aligns with data protection principles like data minimization, accuracy, and transparency. This step helps you stay compliant and protect individual rights.

The fundamentals of an AI risk assessment are not that different from those of a good, old-fashioned cybersecurity risk assessment. Identify and assess potential risks to the rights and freedoms of data subjects from your AI project's data processing. Evaluate both the likelihood and severity of risks, along with their sources and consequences. For instance, consider risks like data breaches, unauthorized access, misuse, bias, discrimination, and loss of control. By understanding these risks, you can implement measures to mitigate them, ensuring your project remains secure and fair while protecting individual rights.

Under GDPR Articles 18 and 22 the restriction of data processing can be requested by data subjects. Additional restrictions related to minors also exists, it would be critical to ensure the project closely investigates these processes restrictions and opt out requirements even if previously provided permission.

Again, the fundamentals in mitigating risk are similar in your AI project and general cybersecurity/privacy projects. Use both technical and organizational measures like encryption, anonymization, pseudonymization, access control, audit logs, consent management, data protection by design and by default, human oversight, and ethical guidelines. For example, apply data protection by design as outlined in the EU AI Act Article 15. Document these measures and their expected effectiveness to ensure transparency and compliance, safeguarding individual rights and enhancing trust in your AI system.