Exploring the use of DeltaStream to manage data security while enabling machine learning teams to access necessary information, the text describes how distributed data meshes can empower application development but pose challenges for non-product teams requiring anonymous data. It explains how teams can securely capture and denormalize user data for model training, using a financial institution's example where a machine learning team accesses anonymized payment data to detect fraud. The process involves utilizing Apache Kafka and DeltaStream to create streams that merge payment information with location data while protecting personally identifiable information. The text highlights real-time data processing and the benefits it brings to model training, allowing for quicker adaptation and improved business outcomes, and encourages readers to explore DeltaStream for processing and securing streaming data.