Umang Kedia
Principal Cloud Developer, HPE
Salt Lake City, Utah, United States
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Umang Kedia is a Principal Cloud Developer at HPE's Office of the CTO, where he works on innovations in AI, security, and hybrid cloud. He holds CKAD, AWS Solutions Architect Associate, and AWS Solutions Architect Professional certifications. Umang has deep hands-on experience with Kubernetes, cloud-native security, and agentic AI frameworks. He is passionate about building secure, scalable systems and sharing practical solutions with the community.
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Your Pods Are Leaking: Bidirectional DLP Sidecars for Kubernetes
What happens when a misconfigured pod sends customer SSNs to an external API? Or when a compromised container exfiltrates secrets? Perimeter DLP tools can't see inside your pods, and service meshes handle routing - not content inspection.
This talk shows a practical approach to pod-level data leak prevention using ingress and egress sidecar containers. Using regex-based pattern matching deployed via Kubernetes ConfigMaps, the sidecars detect and block sensitive data - PII, credentials, even SQL injection attempts — in real time with under 5ms overhead and zero app code changes. The architecture is also extensible to ML-based detection for identifying patterns that regex alone cannot catch.
Attendees will see a live demo of an egress sidecar catching a credit card number in an outbound API call, learn how to dynamically update DLP policies without pod restarts, and understand how this lightweight approach compares to service meshes for content-aware security.
GitOps for RAG: A Kubernetes Operator That Self-Heals Your Knowledge Base
Your RAG knowledge base is silently rotting. Embedding models change, source documents go stale, chunks corrupt — and retrieval quality degrades with no alert, no detection, no self-healing. Meanwhile, Kubernetes can autoscale your pods on CPU, but has no idea that one "heavy" analytical query costs 50x more than a simple lookup.
This talk introduces a Kubernetes operator and CRD that manages RAG platforms the way we manage databases — declaratively. Change your embedding model or chunk size in YAML, and the operator performs a zero-downtime shadow-collection swap. It also classifies queries by semantic complexity and scales each pipeline component independently: a heavy query scales up LLM inference without touching the embedding service.
Attendees will see how a single CRD unifies knowledge base health and query-aware scaling into one closed-loop controller, no multi-tool glue, no propagation delay.
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