Kubestriker

A Blazing fast Security Auditing tool for Kubernetes

Multi-Cloud Open Source Self Hosted + Cloud Options
Category Container & Kubernetes Security
GitHub Stars 992
Last Commit 3 years ago
This page updated a month ago
Pricing Details Free and open-source
Target Audience DevOps teams, security professionals, and organizations using Kubernetes.

Kubestriker is designed to detect misconfigurations in Kubernetes clusters by providing a comprehensive and platform-agnostic security auditing tool. It is designed to perform in-depth checks on various services and open ports across different Kubernetes environments, including self-hosted, Amazon EKS, Azure AKS, and Google GKE.

The technical architecture of Kubestriker involves a command-line interface and a web application version, both of which can integrate with CI/CD pipelines using tools like Jenkins, Azure Pipelines, and Bamboo. This integration allows for continuous scanning of the infrastructure to identify misconfigurations before they reach production environments. The tool also visualizes attack paths, helping organizations understand potential vulnerabilities and strengthen their IT infrastructure.

Operationally, Kubestriker is optimized for speed and scalability, though its performance may vary depending on the size and complexity of the Kubernetes cluster being audited. It requires Helm 3.0 or later for installation via its official Helm chart, which simplifies the deployment process. However, the tool's effectiveness can be limited by the depth and frequency of its scans, as well as the resources available for continuous monitoring.

From a technical standpoint, Kubestriker's ability to scan multiple platforms and identify a wide range of misconfigurations makes it a valuable asset. However, it may introduce additional overhead in terms of resource usage and potential false positives, which need to be managed carefully. The tool's continuous scanning capability ensures real-time security monitoring, but this also means it can generate a significant amount of data, which must be managed to avoid performance degradation.

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