Securing AI Workloads in Kubernetes: Lessons from Scaling Startups

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Presented at BSidesCharm 2026 by

Chris Maenner

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-market fit. Then they add AI workloads, and the attack surface explodes. This talk bridges two worlds: the pragmatic security challenges of scaling startups and the technical reality of securing AI workloads in Kubernetes. We'll cover common failure modes: identity sprawl, over permissioned service accounts, implicit trust between services and how security practitioners can enable velocity instead of blocking it. Then we'll dive into service mesh patterns for AI workloads: - Identity-first security with mTLS and SPIFFE - East-west traffic controls and fine-grained authorization - Model access isolation and prompt protection - Observability for detecting AI service abuse All examples come from production Kubernetes environments. Attendees will leave with patterns they can implement