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Data Protection in AI: Practical Strategies to Secure Sensitive Data, Manage Risk, and Build Trustworthy Machine Learning Systems-logo

Data Protection in AI: Practical Strategies to Secure Sensitive Data, Manage Risk, and Build Trustworthy Machine Learning Systems

Christopher Hale

Secure sensitive data and master AI risk management without slowing down your machine learning pipelines. Whether you are a tech leader navigating compliance frameworks or an engineer building trustworthy algorithms, this empowering guide delivers the...

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Opening Credits

1/11/2026
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Chapter 1: Why Data Protection in AI Is Different

1/11/2026
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Chapter 2: Mapping the AI Data Lifecycle

1/11/2026
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Chapter 3: Collect Only What You Need Data Minimization in Practice

1/11/2026
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Chapter 4: From Raw to Safer Data Anonymization and Pseudonymization

1/11/2026
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Chapter 5: Guardrails for Prompts, Inputs, and Logs

1/11/2026
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Chapter 6: Controlling Access to Models, Data, and Features

1/11/2026
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Chapter 7: Managing Vendor, Cloud, and Third Party Risks

1/11/2026
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Chapter 8: Monitoring, Testing, and Responding to Data Risks

1/11/2026
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Chapter 9: Privacy by Design for AI Turning Principles into Practice

1/11/2026
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Chapter 10: A Practical Framework for Trustworthy AI Data Protection

1/11/2026
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Closing Credits

1/11/2026