1. Mapping AI Data Lifecycles
5 lessonsLearn to trace data from user input through transformation, storage, model interaction, and deletion in AI systems.
2. Privacy Risk Identification in AI Architectures
5 lessonsRecognize where AI systems create unique privacy vulnerabilities and how to assess their severity.
3. Designing Data Controls for AI Products
6 lessonsApply practical privacy controls at each stage of the AI data lifecycle.
4. Retrieval and Context Privacy
4 lessonsManage privacy risks specific to retrieval-augmented generation and context injection patterns.
5. Privacy Documentation and Communication
5 lessonsTranslate technical data flows into clear privacy explanations for stakeholders, buyers, and users.
6. Monitoring and Auditing AI Privacy
4 lessonsImplement ongoing monitoring to detect privacy drift as AI systems change over time.
7. Vendor Evaluation and Third-Party Risk
5 lessonsAssess AI vendors and service providers by analyzing their data handling practices and contractual commitments.
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