ARMO does this natively. It 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. CrowdStrike provides Kubernetes posture and a validating admission controller through Falcon KAC, but this depth of Kubernetes-native RBAC and blast-radius analysis is more limited on an endpoint-rooted platform.

ARMO prioritizes on loaded-in-memory reachability, so you see the CVEs that are actually executed in production, cutting noise by 90%+, and cross-references that runtime evidence with EPSS and CISA KEV. CrowdStrike prioritizes through exploit-prediction (ExPRT.AI), a probabilistic model of how likely a CVE is to be weaponized. Both reduce noise, but they answer different questions: reachability gives you an observed answer about your live Kubernetes workload, where exploit-prediction gives you a likelihood. For an in-cluster decision, the observed answer is the more defensible one.

ARMO generates workload-specific network policies 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. CrowdStrike detects and responds to Kubernetes threats, but automated generation of network policies and seccomp profiles from observed behavior is more limited.

ARMO detects and responds to SQL injection, command injection, SSRF, and LFI/RFI across the app-to-cloud stack, with response actions including Kill, Stop, Pause, and Soft Quarantine, all as part of the full ADR + CDR + KDR + EDR chain. CrowdStrike has strong runtime and agentic investigation through Charlotte AI, but L7 application-layer attack detection of this depth is more limited on a platform whose runtime detection is endpoint and XDR-centric.

ARMO offers a self-service path with no sales call: a two-week free trial of the platform, plus a self-service Startup plan for up to 25 worker nodes with a limited feature set and community support. The Startup plan is an entry point for smaller footprints, not the full platform, so you can validate ARMO on your own cluster before you talk to anyone. CrowdStrike evaluations are typically sales-led, so if a hands-on, no-commitment proof on your own environment matters early, that is a practical difference.

Yes. Through native Kubernetes Validating Admission Policies and the open-source CEL Admission Library, ARMO blocks policy-violating workloads, such as containers running as root, before they are admitted to the cluster. Because the library re-implements Kubescape controls, the same open-source checks that score your posture also enforce it at deploy time. CrowdStrike offers admission control via Falcon KAC; ARMO’s runs on native Kubernetes VAP and the open-source CEL Admission Library.

No sidecars. ARMO runs as a single eBPF-based node agent, typically 1-2.5% CPU and around 1% memory, with no proxies and no per-pod injection. That keeps the runtime footprint light even on dense clusters, which matters when you are weighing another agent against an endpoint platform you already run.

ARMO is Kubernetes-first today, securing clusters wherever they run across AWS, Azure, GCP, Oracle Cloud, VMware Tanzu, and on-premises. Broader multi-cloud posture beyond Kubernetes is on the 2026 roadmap. If your priority is depth on Kubernetes runtime and posture rather than wide multi-cloud CSPM breadth, that scope is deliberate, not a gap.