An approach that embeds data protection and user privacy considerations into AI system architecture and processes from the outset.
A proactive methodology that integrates privacy controls - data minimization, pseudonymization, access controls, encryption - directly into system requirements, design, and deployment. It mandates privacy impact reviews at each development phase, defaulting to the most privacy-protective settings, and ensuring that new features cannot be released without meeting privacy criteria.
A health-tech startup architected its patient-risk prediction tool so that all personal identifiers are tokenized on ingestion, with keys stored separately and access audited. Privacy checks are built into the CI/CD pipeline: any code touching PII automatically fails privacy-gate tests unless explicitly approved by the data-protection officer.
