Panel: How Privacy Engineers Can Shape the Coming Wave of AI Governance
summary
Panel argues privacy engineers should operationalize AI governance now: use data catalog tagging for model governance and extend cookie-banner disclosure workflows into model cards and provenance logs; apply differential privacy in fine-tuning (not initial training) and experiment with synthetic data for training; keep a risk register, run continuous privacy reviews, and require exit plans when adopting new tooling. Practical wins are concrete controls and lineage, not higher-level promises.
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chapters (6)
- 0:00Introduction
- 4:19Privacy and AI risks
- 13:30Privacy by design for AI
- 19:30Automating AI governance
- 30:43Balancing AI data needs
- 32:09Q&A
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