Best Container Security Tools for Cloud-Native SaaS Companies

Container vulnerability management might create the illusion that progress is being made. Even if developers include an updated Dockerfile, the problem will vanish from that particular report and an older image will still be served to customers. For a SaaS company, the right unit of progress is the deployed service, not the number of image vulnerabilities that have been eliminated.

Tools used for securing containers can examine images, detect the most serious issues, and, in some cases, protect workloads that are running. A good choice enables developers to carry out remediation actions while keeping the responsibilities for doing so separate from the operations carried out at runtime. These six options are suitable for various stages of the lifecycle, particularly for teams that have to look after a large number of services without treating each update to the base image as though it were a separate project.

At a glance

1. Aikido Security — SaaS image maintenance

Aikido’s container security tooling scans container images and connects findings with developer remediation. Its container AutoFix workflow can propose changes to Dockerfiles and base images for review, alongside broader code and cloud security capabilities.

It is therefore a good solution in the case where the bottleneck is the need to apply fixes across various service repositories. Consider one ordinary update and another which could cause compatibility issues. Success is defined as having a change that has been reviewed, an artifact that has been rebuilt, and a rollout that has been verified—not merely the automatic opening of a pull request or an optimistic decrease in the number of vulnerabilities.

2. Snyk Container — Existing Snyk users

Snyk Container offers assessment of vulnerabilities in images and advice aimed at developers for remediation, including suggestions for base images. It is useful in the case where SaaS developers are already using Snyk and wish to have container maintenance carried out in the same working environment.

Check the recommendations against the application’s runtime requirements. If a cleaner image results in the service failing, then it is not a better release. Keep record of whether the team are able to understand the change, carry out its tests, and coordinate the rollout across a number of services that share the same base image.

3. Trivy — Custom scanning pipelines

Trivy is an open-source tool which is capable of scanning container images and configurations. It is appropriate for teams who already have an engineering platform into which such scanning can be incorporated.

What it offers is control over the way executions are carried out and over automation. The other aspects of the work stay with the organization—such as the freshness of the vulnerability data, the policy thresholds, suppressions, and the routing of issues. In the case of a SaaS company, it’s worth testing how results are transformed into service-level tasks rather than requesting developers to interpret the isolated command output from each build. Even a good component still requires a maintained operating process.

4. Docker Scout — Docker-centered development

Docker Scout uses insights based on an SBOM to analyze the composition of the image and the related vulnerability information, making it a suitable choice in cases where Docker tooling is already at the heart of the team’s development workflow.

It can be used to compare an image currently in use with one that is being proposed for replacement and to examine what actually changes. This approach is particularly clear advantage when developers need understandable feedback at the level of the image during maintenance. You should verify how that feedback gets passed on to the owner of the production service, since the image that is local to the developer may not be the same as the one that is currently deployed.

5. Sysdig — Production workload context

Sysdig has added to its container and Kubernetes security capabilities runtime context and protection, since it is useful when the SaaS platform team needs to prioritize and investigate issues on the basis of the workloads that have been deployed rather than just the contents of the images.

Get the people who are involved in production into the pilot phase. Check the deployment requirements, the escalation procedures, and the evidence associated with the important alerts. Although runtime context can help with making operational decisions, it does not eliminate the need for a developer process which safely rebuilds and replaces the vulnerable images.

6. Aqua Security — Full workload protection

Aqua’s platform provides capabilities in both artifact assessment and in offering cloud-native runtime protection and should therefore be included on the shortlist when a SaaS company needs policies and controls covering the entire workload lifecycle and not just a vulnerability report with each build.

When testing policy behaviour it is necessary to use realistic exceptions, for example by having a service temporarily need a certain permission or an older component. The decision should specify who is responsible for approving these exceptions and how they expire. Broad coverage is useful provided that the organisation can run it with clear accountability rather than having sophisticated controls set up once and then being forgotten.

Treat image updates as a service maintenance program

When it comes to developer-led remediation, compare Aikido with Snyk; for Docker-centered image feedback, choose Docker Scout; and for a configurable scanning component, use Trivy. Regarding runtime protection, Sysdig and Aqua should be considered if runtime protection is a requirement.

Choose a base image that is used by a number of different services. Calculate the time it takes to determine the owners, suggest an update, test compatibility, deploy the replacements, and check that the old digest is no longer running. Include an exception that has a specific expiry date. Carrying out this exercise shows whether the tool supports a sustainable SaaS maintenance process or if it simply generates another set of image-level findings.