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Claude Prompt to Build a Container Security Scanning Pipeline

Build a CI/CD pipeline that scans container images for CVEs, secrets, misconfigurations, and compliance gaps before they reach production.

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What this prompt does

This prompt makes the AI assemble a complete container security scanning pipeline that runs as part of your CI/CD, stopping unsafe images before they reach [environment]. It walks through eight ordered stages — Dockerfile linting with [dockerfile_linter], dependency CVE scanning of your [package_manager] lockfile via [dep_scanner], image vulnerability scanning with [image_scanner], secret detection, SBOM generation in [sbom_format], an OPA/Gatekeeper runtime policy, a [compliance_framework] report, and an audited suppression file for false positives.

The structure works because it mirrors the real order a security gate executes: cheap static checks first, then dependency and image scans, then policy and reporting. Filling [severity_threshold] controls how aggressive the gate is, [policy_rules] defines what the cluster will refuse to run, and [compliance_framework] aligns the output with the standard your auditors actually use. Because each stage names a concrete tool, the model produces runnable config rather than abstract advice.

When to use it

  • You are shipping container images and want to catch CVEs at build time instead of in production.
  • A leaked API key or credential in an image layer is a risk you need to detect automatically.
  • An auditor or customer requires an SBOM and a [compliance_framework] report for every release.
  • You want a hard gate that fails builds on critical/high vulnerabilities rather than an advisory warning.
  • You need an OPA/Gatekeeper policy to block images that violate [policy_rules] at deploy time.
  • You are standardizing scanning across multiple repos and want one consistent pipeline template.

Example output

Expect a structured pipeline definition for [ci_platform] — typically a YAML workflow with discrete jobs for linting, dependency scanning, image scanning, secret detection, and SBOM generation — plus a separate OPA/Gatekeeper policy manifest, a sample suppression file, and a short description of how the compliance report is produced. Each stage includes the command invocation and the fail condition.

Pro tips

  • Set [severity_threshold] deliberately: blocking on CRITICAL and HIGH is a sane default, but expect to manage the false-positive suppression file actively or the gate becomes noise.
  • Match [package_manager] to every lockfile you actually ship; if you have both pnpm-lock.yaml and requirements.txt, name both so nothing is skipped.
  • Keep the [image_scanner] and [dep_scanner] choices consistent across repos so results are comparable; a single tool reused everywhere beats a different scanner per project.
  • Make [policy_rules] enforce digests and non-root explicitly — those two rules catch the most common deploy-time mistakes.
  • Treat the suppression file as audited code: require a reason and an expiry for every entry, or stale exceptions will quietly reopen the gate.
  • Generate the [sbom_format] artifact even if you do not consume it yet; you will want it the first time a new CVE drops and you need to know which images are affected.

Frequently Asked Questions

Does this pipeline block builds or just warn about vulnerabilities?
The prompt is written to create a hard gate that fails the build on critical and high vulnerabilities, controlled by the `[severity_threshold]` you set. You can soften it to advisory, but the default intent is to stop unsafe images before they reach `[environment]`.
Which scanners does it use for images and dependencies?
It uses whatever you put in `[image_scanner]` and `[dep_scanner]` — the defaults suggest Trivy with Grype as a secondary, but the prompt is tool-agnostic. Name the scanners your team already standardizes on so results stay comparable across repos.
Will it catch secrets accidentally baked into image layers?
Yes, secret detection is one of the eight stages and it scans image layers for embedded credentials, API keys, and certificates. This catches secrets that linting alone would miss, since they often appear only in built layers rather than the Dockerfile.
Can I handle false positives without disabling the whole gate?
The prompt includes an audited suppression file specifically for known false positives, so you suppress individual findings rather than lowering the threshold. Treat each entry as reviewed code with a reason and expiry to avoid stale exceptions reopening the gate.
Engr Mejba Ahmed

Need this built for real?

Engr Mejba Ahmed

AI Developer · Software Engineer

I'm Mejba — I design and ship production AI systems, automations, and full-stack apps. If you want this turned into a working solution for your team, let's talk.

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Engr Mejba Ahmed

Engr Mejba Ahmed

Claude Code Expert · Online

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