Data privacy in the era of cloud native app
summary
Manual inventorying fails at scale: a customer with 500 engineers / 5,000 applications spends 80 hours interviewing teams and covers only 20% of the scope after two years. The proposed fix is code-driven automation that uses static analysis + machine learning to catalog components, detect personal data flows, map gaps against policies, and trigger risk workflows without touching production databases. Mentions existing tools (spreadsheets, Backstage, Jira, OneTrust, piCapture) but argues they don’t replace automated code analysis for continuous privacy-by-design.
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chapters (12)
- 0:00Intro
- 1:40Three key concepts
- 3:08The 7 principles
- 3:59Data privacy today
- 7:27Engineer organization
- 8:47Private systems
- 11:51The borrower
- 13:53Mapping data flow
- 15:54Audit controls
- 17:36Automation
- 19:16Security
- 20:58Questions
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