Governance

Data Minimisation

Definition

The GDPR principle requiring that only the minimum necessary personal data be collected and processed for a specified purpose. Applied to AI, data minimisation drives techniques like federated learning, synthetic data generation, and differential privacy to train effective models without retaining raw personal data.

Related Terms

GDPR + AI

The intersection of the General Data Protection Regulation with AI systems. Key tensions include the right to explanation for automated decisions (Article 22), data minimisation requirements that limit training data use, and purpose limitation that constrains repurposing of collected data for model training.

Federated Learning

A machine learning approach where models are trained across multiple decentralised devices or servers holding local data, without that data ever leaving its origin. Federated learning is valuable for healthcare, finance, and any sector where GDPR data minimisation principles make centralised training impractical.

Purpose Limitation

The GDPR principle that personal data must be collected for specified, explicit, and legitimate purposes and not further processed in a way incompatible with those purposes. This constrains organisations from repurposing customer data to train internal AI models without fresh consent or a lawful basis.

Related Services

Eu Ai Act Compliance

Knowing the Terms Is Step One. Applying Them Is Step Two.

Book a 30-minute call to discuss how these AI concepts translate to your specific industry and business challenges.