This episode teaches data flow modeling as an essential privacy engineering skill, because the CIPT exam repeatedly relies on your ability to reason about where data comes from, where it goes, and what transformations and disclosures occur along the way. We define a data flow as the movement of data through collection points, processing services, storage systems, and external recipients, including the identifiers that allow linking and the metadata that can become sensitive through inference. You will learn how to model flows in a structured way using spoken steps: identify the source, list the data elements, name the purpose, identify each processing step, identify storage and retention, and list every disclosure path to internal teams and third parties. We also cover how to use data flows to find privacy risks such as overcollection, unexpected sharing, weak access points, and retention drift, and how to use the model as the backbone for DPIAs, notices, vendor reviews, and incident response. Troubleshooting includes dealing with incomplete knowledge, shadow integrations, and systems where data is duplicated across logs and analytics pipelines. By the end, you will be able to answer exam questions by grounding your reasoning in clear, end-to-end flows that support defensible control choices. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.