How product-driven privacy ensures real compliance
summary
Claims that trust fails at scale and regulators demand proof lead to a shift to continuous technical checks: a website scanner that simulates consent across geos, an APK/mobile scanner that lists SDKs, permissions and cross‑border flows, and integrations into tag managers and CDPs to build a dynamic data map and risk dashboard. Results include automated assessments and AI that autopopulates processing descriptions, exposing issues like 76% of top 100 US sites ignoring opt‑outs.
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chapters (21)
- 0:00Introduction to privacy shift
- 1:45Trust-based privacy fails at scale
- 4:38Regulators demand evidence
- 6:43Trust to evidence-based privacy
- 8:29Software core of compliance
- 10:16Dynamic data map with evidence
- 12:02Scanning software lifecycle
- 14:27Kelly Peterson on consumer trust and AI
- 19:26Embedding privacy in engineering
- 20:29Grindr’s privacy program
- 24:25Aaron Weller on trust and controls
- 26:13Manual privacy controls unsustainable
- 28:41Privacy tools beyond spreadsheets
- 31:09Trust through evidence and architecture
- 35:50Prashant Mahajan on regulatory pressure
- 37:39Privacy technology unbundling
- 40:30Website scanning for consent
- 42:58Mobile app scanning and SDK governance
- 45:05Generative AI for privacy automation
- 46:56Integrating marketing stack
- 48:10Closing remarks
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