ARMO generates workload-specific NetworkPolicies and seccomp profiles directly from observed eBPF runtime behavior, then lets you graduate them from audit mode into enforcement with no code changes. Because the policy is derived from what the workload actually does, it tightens least privilege without breaking the application. Upwind detects and correlates Kubernetes runtime activity well, but automated generation of NetworkPolicies and seccomp profiles from observed behavior is more limited.

ARMO uses an observe-to-enforce model: it baselines how each workload actually behaves, then graduates that profile from audit mode into in-cluster enforcement per workload, with no code changes. Upwind delivers strong eBPF runtime detection and topology correlation, but its enforcement is still maturing relative to its detection and is less oriented toward progressive in-cluster enforcement derived from observed behavior. If enforcing least privilege on real workload behavior inside the cluster is the goal, that is ARMO’s model by design.

ARMO visualizes cluster RBAC in one interactive view, surfaces over-privileged services, roles, and bindings, and maps those permissions to the real blast radius if a workload or identity is compromised, alongside pod security, network policy, and control-plane exposure. Upwind provides Kubernetes and cloud posture as part of a broader platform, but this depth of Kubernetes-native RBAC and blast-radius analysis is more limited.

ARMO correlates signals across the full ADR + CDR + KDR + EDR chain into one LLM-powered attack story, so an application-layer attack like SQL injection surfaces inside the same narrative as the cloud and Kubernetes events around it, with investigation time down 90%+. Upwind links runtime signals into a cloud-context narrative through topology correlation, which is genuinely useful, but correlation across the full Kubernetes-to-application chain on a single behavioral foundation is more limited.

ARMO offers a self-service path: start free with Kubescape on a test cluster, then deploy the full platform with a single Helm command in under 2 minutes, with a free tier for small clusters and no mandatory professional-services engagement. Upwind evaluations are typically sales-led. If a hands-on, no-commitment proof on your own cluster matters early in the process, that is a practical difference.

ARMO’s in-cluster components are open-source and built on Kubescape, a CNCF project used by 50,000+ organizations with 100,000+ deployments and 11,000+ GitHub stars, so your team can inspect, audit, and verify every component running in the cluster. Upwind is a capable, modern runtime platform, but it is proprietary, with no equivalent open-source CNCF project for community validation. For teams that treat auditability of in-cluster agents as a requirement, that distinction is often the deciding one.

ARMO’s Cloud Threat Readiness Lab (CTRL) injects real attack behaviors directly into your cluster, so you can watch ARMO detect and respond on your own workloads before you commit. Instead of weighing two vendors’ detection claims on paper, you validate ARMO’s runtime detection and response hands-on. That matters in a matchup where both platforms market strong eBPF runtime coverage, because it turns a marketing comparison into something you can see for yourself.