Google has open-sourced k8s-aibom for Kubernetes clusters, a tool designed to identify unregistered AI workloads and generate machine learning bills of materials. The software runs as an unprivileged Kubernetes controller and observes live cluster activity rather than scanning software artifacts before deployment. It continuously monitors cluster resources and container environments to detect AI runtimes and related components that may have been deployed without formal registration. According to Google, k8s-aibom watches Kubernetes resources including Deployments, StatefulSets, DaemonSets, Jobs, and KServe resources, then uses pattern matching across container images, environment variables, and command-line arguments to identify software such as vLLM, Triton Inference Server, Text Generation Inference, Ollama, LangChain, AutoGen, CrewAI, Milvus, Qdrant, and pgvector. Using that data, the controller creates Machine Learning Bill of Materials documents based on the OWASP CycloneDX 1.6 standard. The documents can be attached to an in-cluster custom resource or sent to external destinations including Google Cloud Storage buckets and webhook endpoints. Google positions the project as a complement to existing AI software inventory and security products. The controller is intended to give teams a view of active workloads without requiring sidecars, privileged DaemonSets, kernel-level access, or manual changes to pod specifications. A central feature is a three-tier classification model for discovered assets, with the first tier, Declared, covering cases where a model