Secure AI platforms on Kubernetes
Protect the AI workloads your business runs on: zero trust for modern platforms, without breaking developer flow.
Focus areas
- Secure AI Platforms
- Protect AI workloads running on Kubernetes
- Zero Trust Architecture
- Reduce lateral movement across modern applications
- Service Mesh Engineering
- Production-ready service mesh architectures that improve both security and reliability
- Production Kubernetes Reviews
- Assess clusters before attackers (or auditors) do
Deliverables
- A cluster assessment mapped to how attackers actually move
- Zero trust architecture with a rollout plan your team can execute
- AI workloads isolated from everything they shouldn't reach
- The signals that catch abuse: cost spikes, access anomalies, denials
- Runbooks and documentation your platform team owns
How it runs
- 01
Understand
Threat model your clusters: what runs where, what talks to what, and what an attacker would do.
- 02
Design
Zero trust architecture fitted to your platform and team, not a reference diagram.
- 03
Implement
Roll out incrementally with your engineers, without breaking production.
- 04
Enable
Observability, runbooks, and the confidence to operate it independently.
FAQ
Why do AI workloads need different Kubernetes security?
Because the assets differ: model IP exposed through inference APIs, sensitive data flowing through prompts, and expensive compute ripe for abuse. One compromised pod on a flat network can mean a five-figure cloud bill overnight, or your model weights extracted query by query.
What's actually inside the engagement?
The implementation work uses the production toolchain: Istio or Cilium service mesh, mTLS workload identity, NetworkPolicy and AuthorizationPolicy, SPIFFE, and Gateway API, chosen for your stack and team, not by preference.
We have NetworkPolicies. Aren't we done?
NetworkPolicy controls which pods can talk (L3/L4). It can't restrict what they say: methods, paths, identities. AI workloads need both layers: an AI service with a prompt injection flaw can otherwise reach your user database with nothing in the path to stop it.
Is this a paper assessment or hands-on?
Hands-on. This practice comes from production platform work and public teaching, including conference talks demonstrating live from-insecure-to-secure cluster builds with kind, Cilium, and Hubble.
Related services
Ready to talk about kubernetes & ai workload security?
A 30-minute intro call: your goals, your environment, and whether this is a fit. No pitch deck, no obligation.