Privacy by Design
An approach that embeds data protection and user privacy considerations into AI system architecture and processes from the outset.
Definition
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.
Real-World Example
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.
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