Governance Guide
AI Data Minimization Guide (2026) - Privacy by Design
Data minimization reduces privacy risk: collect only necessary data, limit purpose, minimize retention. This guide covers engineering controls for privacy by design.
Direct answer
Data minimization reduces privacy risk: collect only necessary data, limit purpose, minimize retention. This guide covers engineering controls for privacy by design.
Fast path
- Limit collection: only collect data required for specific AI purpose, no extra fields.
- Constrain purpose: each data element used only for declared purpose, no repurposing.
- Minimize retention: prompt/output logs deleted after processing, no indefinite storage.
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Implementation Steps
- Limit collection: only collect data required for specific AI purpose, no extra fields.
- Constrain purpose: each data element used only for declared purpose, no repurposing.
- Minimize retention: prompt/output logs deleted after processing, no indefinite storage.
- Implement controls: schema validation rejects extra fields, purpose tags, automated deletion.
Frequently Asked Questions
What is data minimization for AI?
AI data minimization: collect minimum data needed for AI task (not full user profiles), use data only for declared purpose (no training on user data without consent), delete prompts/outputs after use (no indefinite retention), and avoid storing sensitive data in logs.
How to implement purpose limitation in AI?
AI purpose limitation: tag each data element with allowed purpose (inference, training, analytics), validate data access against purpose tags, prevent cross-purpose data sharing, and audit purpose compliance. Example: prompts tagged 'inference' cannot be used for 'training'.
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