Fairwinds Polaris vs Falco

Fairwinds Polaris is an open source policy engine for validating and remediating Kubernetes resource configuration. It includes more than 30 built-in policies, and teams can add their own using JSON Schema. Checks cover security practices such as least privilege, privilege escalation, root execution, and host IPC or PID settings, along with resource requests and limits, readiness and liveness probes, and image tags. Polaris can run as a dashboard, an admission controller, or a command-line tool for checking local YAML files in CI/CD. Its validating webhook rejects workloads that trigger danger-level checks; warning-level findings pass and appear in the dashboard or webhook logs. A mutating webhook or command-line run can automatically remediate supported issues, including missing limits and probes and some unsafe security settings. The dashboard can audit local files or connect to a live cluster, and can be installed with kubectl or Helm. The validating webhook requires a valid TLS certificate. Polaris is free and runs on Linux, macOS, Windows, web, and self-hosted environments.

Falco is a free, open source runtime security project for Linux and self-hosted environments. It monitors hosts, containers, Kubernetes, and cloud environments for abnormal behavior, potential threats, and compliance violations in real time. Events are compared with built-in detection rules, and users can create rules for their own environments. Sources include Linux kernel system calls, Kubernetes audit logs, cloud events such as AWS CloudTrail, and events from GitHub and Okta. For kernel event monitoring, Falco uses an eBPF driver by default and offers a kernel module for wider compatibility. Installation options cover Linux hosts, Docker containers, and Kubernetes, including official Helm charts; supported architectures include x86_64 and ARM64. Falcosidekick can forward events to more than 50 systems, including email, chat, message queues, serverless functions, and databases. Falco is hosted by the CNCF as a graduated project. Detection depends on having appropriate rules in place, while resource use varies with server load and workload. The project recommends testing performance in the target environment.