About this ebook
For data officers, privacy engineers, and compliance leads, AI Data Privacy Standards delivers a decision-focused framework for embedding privacy and security into every stage of AI adoption. This book moves beyond abstract principles to concrete workflows: mapping data flows, conducting algorithmic impact assessments, selecting privacy-enhancing technologies, and structuring governance that holds up under audit. You will learn how to align AI initiatives with GDPR, CCPA, and emerging sector rules, balance model utility against re-identification risk, and build incident response plans tailored to machine learning systems. Each chapter includes checklists, decision trees, and practical exercises, with guidance on verifying current standards and product capabilities. Whether you are deploying predictive models, chatbots, or computer vision, this book gives you the tools to protect data subjects while advancing AI innovation.
For data officers, privacy engineers, and compliance leads, AI Data Privacy Standards delivers a decision-focused framework for embedding privacy and security into every stage of AI adoption. This book moves beyond abstract principles to concrete workflows: mapping data flows, conducting algorith...
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