Data classification, governance and protection in the world of AI

During this Beltug session, we combined practical experience and regulatory insights to explore how to strengthen data governance, improve data quality, and prevent data loss. Britenet started us off with a look at the growing challenges of managing consent, data classification and information protection across increasingly interconnected data ecosystems. Then the Data Protection Authority explored the challenges organisations face to balance innovation, compliance and risk management in the age of AI.

When consent moves faster than data: exponential complexities in multi-modal sensitive data processing

For companies made up of multiple distinct business units, such as insurers, keeping customer data available, accurate and compliant across systems can be especially challenging when those systems were never designed to work together. With each additional data source, complexity doesn’t just increase, it multiplies, as consent and visibility rules must hold at every intersection. We explored the issue of tracking consent across time, scope and audience, and what that means for data-platform design in the era of cloud and AI.

Piotr Wegner, Solutions Director, Britenet

When AI meets personal data: why accuracy, quality, classification and control matter more than ever

Using personal data in processing activities for training, development or deployment of AI systems and models requires strict data control. Accuracy, appropriateness, high data quality and robust data classification are all essential to satisfy GDPR principles and AI Act data requirements. This session gave us a look at what may go wrong in practice (data leaks, biases, etc.), backed by concrete examples from recent headlines in the press, European DPA decisions and landmark European Court of Justice rulings.

Mihaela Varnav, Legal Advisor, Belgian Data Protection Authority