Navigating privacy and compliance while securing gen AI applications
summary
A five‑scope scoping matrix maps consumer apps through enterprise apps to pre‑trained, fine‑tuned, and self‑trained foundation models, and shows most customers sit in scopes two to four with few organizations in scope five. Practical controls include RAG with vector DBs plus role‑based access, Bedrock opt‑out for training, SageMaker Clarify for bias metrics, Macie and S3 discovery, Audit Manager and ISO 42001 guidance. Key warnings: data placed into model weights cannot be selectively deleted, and over 1,000 AI laws across 69 countries create fast‑moving compliance risk.
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chapters (16)
- 0:00Introduction and scoping
- 0:57Consumer app scope
- 2:29Enterprise app scope
- 3:44Pre-trained model scope
- 5:16Customized model scope
- 6:34Self-trained model scope
- 9:30Data privacy considerations
- 13:28Rag and fine-tuning risks
- 18:46Self-building responsibility
- 20:16Legal compliance framework
- 23:13Data privacy in AI law
- 27:50Transparency and audit
- 32:31Human oversight mechanisms
- 36:41Risk-based classification
- 39:14Profiling and data ethics
- 41:17Safety and conclusion
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