Privacy-by-design
Privacy by design is an approach that integrates privacy protections into the architecture, development, and operation of systems from the outset.
Introduction to Generative AI, Second Edition
Privacy by design is a development approach that integrates privacy protections, proactive risk management, and data minimization throughout a technology's lifecycle.
- How it is used
- Guides language-model development by embedding privacy protection, risk management, and data minimization from design through deployment.
- How deep it goes
- Explains lifecycle principles and concrete model-development measures, but does not descend into implementation details.
- Examples
- Uses training-data curation, preprocessing, training, memorization-risk evaluation, privacy documentation, and default privacy settings as examples.
- What you can do after
- Apply privacy checks across a model lifecycle, including data curation, protective techniques, evaluation, documentation, and compliance planning.
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The Art of AI Product Development
Privacy-by-design is an approach that embeds privacy protections into the architecture and operation of systems from the outset.
- How it is used
- Protects nonpublic user data throughout AI development and operation while supporting compliance, trust, and responsible product delivery.
- How deep it goes
- Defines principles and concrete controls, but stops short of detailed implementation for the complete seven-principle framework.
- Examples
- Uses recommendation generation, anonymized model data, redacted sensitive fields, privacy impact assessments, and federated learning as concrete examples.
- What you can do after
- Build privacy controls into AI systems, assess exposure early, minimize data use, and configure protective defaults.
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First-Party Data Activation
A design principle that incorporates privacy protections into systems, processes, and products from their inception rather than adding them afterward.
- How it is used
- Applies privacy across data collection, analytics, activation, product development, and campaigns to build trust.
- How deep it goes
- Embeds privacy decisions, controls, collaboration, roles, and measurable goals, without prescribing detailed technical implementation.
- Examples
- Uses pre-collection questions, consent management platforms, privacy champions, fine-grained access controls, audits, and the Equifax breach.
- What you can do after
- Apply privacy-by-design decisions that reduce unnecessary collection, clarify responsibility, strengthen trust, and support sustainable growth.
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Related concepts
- data minimization strategyuses · Data minimization is the practice of collecting, processing, using, and retaining only the personal data necessary for specified purposes.
- validationuses · The process of checking data, behavior, systems, models, or results against defined criteria to assess their correctness, suitability, or acceptability.
- principlekind of · A principle is a general rule, guideline, or value that guides decisions, behavior, or judgments.
- federated learningused by · Federated learning is a machine-learning approach in which a shared model is trained across decentralized data sources without centrally collecting their raw data.
- conversational aiconfigures · Conversational AI is artificial intelligence that enables software systems to interact with users through natural-language dialogue.
- privacyconfigures · Privacy is the protection and controlled handling of information or activities to prevent inappropriate access, collection, use, or disclosure.
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