Toward Zero Trust with AI
summary
Cloud-native Zero Trust is framed as practical steps not theory: mTLS via Istio for identity, Kyverno for policy governance and mutation, and K8sGPT for a two-phase analysis (hard-coded checks then LLM expansion) that converts findings into kubectl commands and Kyverno policies. CNCF research estimates misconfiguration costs ~$8,000 per cluster, so the demo ties metrics, Envoy/metrics and Kyverno reports into K8sGPT to produce suggested fixes and enable automated remediation; initial integration took about four to five hours.
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chapters (16)
- 0:00Introduction
- 1:06Defects cost money
- 2:24Zero trust
- 4:56Core principles
- 5:49Trustless ver verification
- 7:24Service segmentation
- 9:02Governance
- 11:49Lease Privilege
- 13:23The Blank Page Problem
- 15:09The Demo
- 17:23What is kgpt
- 19:49What are we bringing together
- 20:39Does it work
- 24:09It can be done
- 26:45Any questions
- 33:35Can we generate a better solution
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