AI Platform & AI Security
We secure Kubeflow platforms and build the Kubernetes they run on
Putting a model into production means someone has to decide who can reach the training data and what the model is allowed to answer. Both decisions have been ours. That was a proprietary LLM infrastructure on Kubeflow, secured across the ML lifecycle: access governance, training-data protection, pipeline integrity, and controls on model artifacts and the inference API.
The platform underneath is Max’s half. He builds the Kubernetes those workloads land on, bare metal or EKS, GKE, AKS, and the CKA is his.
The controls around it are Volodymyr’s. He decides who reaches the training data, what the inference API is allowed to return, and what the pipeline is permitted to pull in, and he holds the KCSA and the Cilium Certified Associate. If you already have a model in production and nobody owns those answers, that is the engagement.
The exchange is plain. You give us read access to the cluster and the pipeline, plus two hours with whoever owns the model; you get back a written finding on training-data access, artifact integrity, and what the inference endpoint will answer, with a named owner and a date against each item.
We have not built an ML platform. Nobody here has trained a model or taken a training pipeline to production, and a team that needs the platform itself built should hire people who have already shipped one. We would be learning on their money.