Transform CyberSec Governance with GenAI
summary
Claims a concrete stack: homomorphic encryption plus trusted execution environments, API gateways, rate limiting, digital signatures and real‑time monitoring to prevent data poisoning and model inversion. Describes detection methods (data validation, continuous retraining, CI/CD vulnerability scans) and an implemented healthcare case: models trained on a large anonymized image corpus using encrypted training and attested execution, improving diagnostic accuracy while preserving HIPAA compliance. Recommends zero‑trust practices, just‑in‑time privilege, automated incident response, and migrating to post‑quantum cryptography and predictive security models.
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chapters (9)
- 0:00intro
- 0:14introduction and agenda overview
- 1:37understanding gen ai in platform engineering
- 3:03security risks in gen ai models
- 6:22security techniques and best practices
- 12:00secure deployment strategies for gen ai
- 15:51case study: applying gen ai in healthcare
- 17:05future trends in gen ai security
- 18:08conclusion and final thoughts
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