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UID:pretalx-bsidescharm2026-LCLMRP@pretalx.com
DTSTART;TZID=EST:20260426T100000
DTEND;TZID=EST:20260426T105000
DESCRIPTION:Startups ship fast\, often faster than their security practices
  can keep up. As someone who's built and secured platforms at growth-stage
  companies\, I've watched teams accumulate risk while chasing product-mark
 et fit. Then they add AI workloads\, and the attack surface explodes.\n\nT
 his talk bridges two worlds: the pragmatic security challenges of scaling 
 startups and the technical reality of securing AI workloads in Kubernetes.
 \n\nWe'll cover common failure modes: identity sprawl\, over permissioned 
 service accounts\, implicit trust between services and how security practi
 tioners can enable velocity instead of blocking it.\n\nThen we'll dive int
 o service mesh patterns for AI workloads:\n- Identity-first security with 
 mTLS and SPIFFE\n- East-west traffic controls and fine-grained authorizati
 on\n- Model access isolation and prompt protection\n- Observability for de
 tecting AI service abuse\n\nAll examples come from production Kubernetes e
 nvironments. Attendees will leave with patterns they can implement
DTSTAMP:20260417T061119Z
LOCATION:Track 1
SUMMARY:Securing AI Workloads in Kubernetes: Lessons from Scaling Startups 
 - Chris Maenner
URL:https://pretalx.com/bsidescharm2026/talk/LCLMRP/
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