Building Privacy-Compliant Data Architecture

Compliance requires fundamental architectural decisions that affect all aspects of data storage. Data classification systems must map regulatory categories to technical implementations. Personal data, sensitive personal data, and special categories each require different protection levels and handling procedures. Automated classification helps ensure consistent treatment across systems.

Privacy-enhancing technologies (PETs) provide technical solutions for regulatory requirements. Differential privacy enables analytics while protecting individual privacy. Homomorphic encryption allows computation on encrypted data. Secure multi-party computation enables collaborative analysis without sharing raw data. These technologies, once theoretical, now offer practical solutions for compliance challenges.

Data lineage tracking becomes crucial for demonstrating compliance. Systems must track data origin, transformations, and usage throughout its lifecycle. This tracking enables accurate responses to access requests and supports purpose limitation requirements. Automated lineage tracking reduces manual documentation burden while improving accuracy.