GenOps: A Governance-Driven Model for Safe
summary
GenOps claims a concrete stack: contextual prompt injection that pulls build logs and incidents, an OPA-enforced blast radius score gating merges, SLO-driven canaries that auto-rollback on metric drift, and a cryptographically signed immutable audit trail with a model registry. Concrete numbers: 55% faster cycle time, 14.4% blocked dangerous actions, and a reported 95% reduction in operational overhead from automated onboarding. The system treats models as service accounts with runtime assurance and closed‑loop learning.
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chapters (12)
- 0:00Introduction and Welcome
- 0:51Speaker's Background and Experience
- 2:09Current Industry Landscape and AI Adoption
- 3:57The Problem with AI in DevOps
- 6:27Proposed Solution: GenOps
- 8:13Four Pillars of AI Governance
- 9:11Contextualization and Memory in AI
- 11:04Strategic Guardrails and Blast Radius
- 12:34Deployment and Runtime Assurance
- 13:31Governance and Audit Trail
- 16:54Summary and Key Takeaways
- 18:30Conclusion and Final Thoughts
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