About This Architecture

TriSec DevSecOps and AI/ML Security Lab on GCP demonstrates a phased, production-hardened Kubernetes environment built with infrastructure-as-code and GitOps principles. The architecture progresses from Workload Identity Federation and Terraform remote state through a private VPC with regional GKE, security services including Secret Manager and Cloud KMS, and policy enforcement via OPA Gatekeeper and Binary Authorization. Multi-namespace deployments—Juice Shop, AI Goat, ML Service, and observability stacks—showcase real-world security patterns with RBAC, NetworkPolicies, and Falco runtime monitoring. This lab design solves the challenge of learning GCP security best practices in a controlled, repeatable environment without long-term cost burden. Fork this diagram on Diagrams.so to customize phases, add additional workloads, or adapt the VPC CIDR ranges and node pool configurations for your own labs. The phased approach allows teams to build incrementally, validating each security layer before moving to the next.

People also ask

How do I build a secure, phased Kubernetes lab on GCP with Workload Identity, OPA Gatekeeper, and GitOps?

TriSec lab on GCP uses eight phased stages: Workload Identity Federation for keyless authentication, a private VPC with regional GKE and Shielded Nodes, Secret Manager and Cloud KMS for secrets, OPA Gatekeeper and Binary Authorization for policy enforcement, and Argo CD for GitOps deployments. Multi-namespace workloads (Juice Shop, AI Goat, ML Service) and observability (Prometheus, Grafana, Falco