Container image scanning for software decomposition
The ITOM Visibility apps, Discovery and Service Mapping Patterns and Kubernetes Visibility Agent integrate with Aqua Trivy to collect data on container images and OS packages. You can increase your control over container deployment by having visibility to the container components.
Benefits of image scanning
- It helps you identify software installed in containers for regulatory and compliance use cases.
- It helps you adhere to company policies like usage of golden images, outdated software, mandatory labels, or configuration policies.
- It also helps you manage licensed software running in containers.
- You can also get the service context by using tags, and service mesh to understand their impact on your organization.
Image scanning use cases with ITOM Visibility
You can use two ITOM Visibility apps to scan container images, Discovery and Service Mapping Patterns and Kubernetes Visibility Agent. Patterns is a feature set used by Discovery, Cloud Discovery, and Service Mapping. Kubernetes Visibility Agent is a feature of Agent Client Collector. While Kubernetes Visibility Agent (formerly known as CNO-V) is more suitable for Kubernetes and dynamic containerized workloads, pattern-based discovery is more suitable for non-Kubernetes Docker containers.
- Use case # 1
- Once an application has been packaged up in container images, a security professional can scan the base image, as well as the final image, for vulnerabilities, and identify OS packages, software dependencies, and application records. This is specifically for Containerized MSSQL Server.
| Visibility methods | Method characteristics | What's discovered |
|---|---|---|
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Discovery and Service Mapping Patterns and Aqua Trivy:
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Discovered using Discovery and Service Mapping Patterns:
For more information, see:
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Kubernetes Visibility Agent and Aqua Trivy:
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Kubernetes Visibility Agent -based discovery doesn't require credential set up, and no need for MID Server. Access is through ServiceAccount/ClusterRole. The installation is via Helm Chart or Kubernetes YAML file. The discovery is run near real-time. Use Kubernetes Explorer to download SBOM. |
Discovered using Kubernetes Visibility Agent
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- Use case #2
- A compliance officer can generate an SBOM to obtain a detailed list of the dependencies of the container image and to ensure that the software complies with industry regulations.
| Visibility method | Method characteristics |
|---|---|
| Kubernetes pattern or Docker pattern | SBOM creation is part of the container scanning. |
| Kubernetes Visibility Agent | SBOM creation is also a part of the container scanning, but using ACC is best suited for organizations that need flexibility to perform both full and continues discovery. |
- Use case #3
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An engineer found a defect in a custom-built image and needs to find all Kubernetes pods that are running using that image.
| Visibility method | Method characteristics | What's discovered |
|---|---|---|
| Kubernetes pattern | Aqua Trivy container scanning isn’t required. You can identify the pods using Patterns. |
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| Kubernetes pattern with Cloud discovery | Aqua Trivy container scanning isn’t required. You can identify the pods using Patterns. | All the of the above and account or region details |
- Use case #4
- An engineer finds a defect in a custom-built image and needs to find all Docker containers (non Kubernetes) that are running using that image.
| Visibility method | Method characteristics | What's discovered |
|---|---|---|
| Horizontal Discovery of VM running Docker (Docker pattern) | Aqua Trivy container scanning isn’t required. You can identify the pods using Patterns. | See: Docker virtualization |
Image scanning with Discovery and Service Mapping Patterns
Kubernetes and Docker patterns integrate with the Aqua Trivy tool and run scheduled jobs to discover container images and OS packages at fixed intervals of 10 images per minute. During the scan, the pattern indicates the scanning status. The pattern discovers OS packages that are related to an image. Then, it finds the image command attributes like the CI class. Based on the command attributes the pattern creates application records. In addition, the pattern uses enriched scripts to add details to the application records. After that, the pattern maps the relations between the OS packages and the containers.
Part of the data is populated in CMDB tables and part of it in transformation tables (non-CMDB temporary tables). The transformation tables are installed with the pattern. For example, the information you get by scanning includes origin registry, software name, and version.