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Static image scanning answers “what is inside this container artifact, and is it acceptable to deploy?” Runtime monitoring answers “what is this workload doing on a live host or cluster, and does that behavior look dangerous?” Image scanners inspect layers, operating-system and language packages, software bills of materials (SBOMs), and policy thresholds before or outside execution. Runtime tools observe processes, system calls, files, networks, privileges, and orchestration events while containers run.
Neither control subsumes the other. Scan every build and registry artifact, use a risk-based gate before deployment, and add runtime detection wherever workload behavior, privilege, or exposure warrants it.
Two controls, two security questions
Container security is often described as if one scanner should find every problem. In practice, the controls operate at different points in the software lifecycle and collect different evidence.
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Static scanning examines an image or registry artifact without starting the workload. It unpacks image layers, identifies operating-system packages, language-specific dependencies and, where supported, non-packaged software, then matches those components against vulnerability advisories and policy rules. The output commonly includes an SBOM and a report that can be attached to a build, pull request or registry record.
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The central question is: Should this exact artifact be promoted or deployed? A finding can usually be remediated by updating a base image, upgrading a dependency, removing an unnecessary package or changing a build process.
Runtime container security monitoring
Runtime monitoring watches a workload after it starts. It consumes Linux kernel events and, depending on the product, plugin streams from container runtimes and orchestration systems. Rules evaluate events such as process creation, shell execution, system calls, file access, network connections, privilege changes and namespace operations. Kubernetes and container metadata can be attached so an alert identifies the pod, namespace, image, node and workload.
The central question is: Is this running workload behaving in a way that should trigger investigation or enforcement? A response may include alerting, investigation, isolation or a runtime policy action.
What static image scanning catches
Known vulnerabilities in operating-system packages
Scanners compare package names and versions in an image with published vulnerability advisories. This can expose an outdated system library, shell, compression utility, crypto package or other component inherited from a base image. The result is tied to the package and version present when the artifact was scanned; it is not a prediction of every way the software could be attacked.
Language and application dependencies
Supported scanners inspect dependency manifests and installed packages for ecosystems such as JavaScript, Python, Java, Go, Ruby and similar languages. A vulnerable library can be identified even when it is not an operating-system package. Whether a dependency is found depends on the scanner’s ecosystem support and how the application was built, including vendored or bundled code.
Non-packaged software and Kubernetes components
Some scanners also identify software installed outside the operating system package manager and can scan Kubernetes targets or configuration. Trivy documents coverage for operating-system packages, language-specific packages, non-packaged software, container images and Kubernetes components. Coverage varies by target and scanner version, so teams should verify which analyzers are enabled in their pipeline.
SBOM and component inventory
An SBOM records the components discovered in an image. Inventory is useful even when no currently known vulnerability is present: incident responders can search deployed artifacts for a newly disclosed issue, and compliance teams can show which packages entered a release. Docker Scout analyzes image contents, generates a detailed package and vulnerability report, and can provide remediation suggestions.
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Policy violations before deployment
CI and registry workflows can reject an image when it exceeds a severity threshold, contains a prohibited package, uses an unapproved base image or fails an SBOM requirement. A policy should define what happens for exploitable versus non-exploitable findings, exceptions, and vulnerabilities with no available fix. A blanket “zero findings” rule often blocks useful releases over low-risk or unreachable issues; a risk-based rule is more sustainable.
What runtime monitoring catches
Unexpected process and shell execution
A production web service that suddenly launches a shell, package manager, scripting interpreter or encoded command creates a behavioral signal even if the image was clean at build time. Runtime rules can flag parent-child process relationships, unusual binaries, execution from temporary directories and commands that are rare for that workload.
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Privilege escalation and privileged operation
Monitoring can detect attempts to change identity, acquire capabilities, use set-user-ID binaries, access host resources or run in a privileged container. The event stream can reveal a process trying to escape its intended privilege boundary, while Kubernetes context shows which service account and pod made the attempt.
Suspicious system calls and namespace changes
Unexpected calls affecting mounts, namespaces, capabilities or kernel interfaces can indicate exploitation or container-escape activity. Runtime tools do not need to know that a package is vulnerable to flag a process performing a dangerous operation; they evaluate the observed event and its context.
Sensitive file reads and mutations
Rules can alert when a workload reads credential files, container runtime sockets, host-mounted paths or other sensitive locations. They can also detect unexpected writes, deletion of binaries, modification of configuration and execution of a file created during the session.
Unexpected network connections
Live monitoring can identify a process making an unusual outbound connection, listening on an unexpected port or contacting an address that is inconsistent with its normal role. The alert can include process, container, pod, namespace and node metadata. This is evidence about current behavior, not a complete inventory of every network path the image could use.
Context from containers and Kubernetes
Falco describes its model as near-real-time threat detection for cloud, container and Kubernetes workloads using runtime insights. It observes kernel and plugin events, applies rules and enriches alerts with container and Kubernetes context. The practical value is attribution: responders can move from “a process opened a sensitive file” to the specific workload, image, namespace and host involved.
Where each control is blind
What static scanning cannot see
- A process exploiting a vulnerability after deployment.
- A payload downloaded at runtime and executed from a writable directory.
- An unexpected shell, child process or command launched by an attacker.
- Live network connections, data movement or access to a newly mounted secret.
- Behavior made possible by runtime configuration, credentials or a compromised dependency fetched after startup.
A clean scan is evidence about the artifact at scan time. It does not prove that the deployed process will behave safely.
What runtime monitoring cannot replace
- An image and package inventory for vulnerability management.
- Advisory matching that identifies known vulnerable versions.
- A pre-deployment gate that blocks unacceptable artifacts.
- Evidence that a fixed base image or dependency was actually shipped.
A runtime alert may expose suspicious behavior without identifying every vulnerable package in the image. Runtime coverage also depends on the events collected, the operating system, kernel integration, enabled plugins and the quality of detection rules.
Static scanning and runtime monitoring compared
| Comparison | Static image scanning | Runtime monitoring |
|---|---|---|
| Timing | During a build, in CI, or when an artifact enters a registry | While a container or workload is executing on a host or cluster |
| Primary evidence | Image layers, package metadata, SBOM entries and vulnerability advisories | System calls, process activity, file and network events, privileges and orchestration metadata |
| Detection type | Known component vulnerabilities and policy violations | Behavioral anomalies, dangerous actions and rule matches |
| Typical response | Rebuild, upgrade, remove a component, approve an exception or block promotion | Alert, investigate, isolate a workload, terminate activity or enforce a runtime policy |
| Best location | Developer workflow, CI/CD pipeline and image registry | Deployed hosts, Kubernetes nodes and runtime event collectors |
| Main operational concerns | Package and language coverage, false positives, exception handling and policy thresholds | Kernel or eBPF requirements, rule tuning, event volume, privileges and response capacity |
| What a “clean” result means | No matching findings under the scanner’s databases and policies at scan time | No rule matched the events observed during the monitoring window |
How common tool categories fit
Image analysis platforms
Docker Scout is an example of an image-focused workflow. Its documented capabilities include image analysis, SBOM generation, continuously recalculated vulnerability status, remediation guidance, registry and CI integrations, and policy evaluation. The relevant output is a decision about an artifact and the work needed to make it acceptable.
Open-source vulnerability scanners
Trivy is an example of a scanner that can inspect operating-system packages, language dependencies, non-packaged software, container images and Kubernetes targets. Teams commonly run it in CI, against a local image, or against registry content. The scanner supplies component and advisory evidence; it does not observe a process after deployment.
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Falco is an example of a runtime monitoring and alerting engine. Its rules evaluate kernel and plugin event streams and add container and Kubernetes metadata. It belongs on the deployed side of the control boundary, where it can see what processes and workloads actually do.
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1. Scan at build time
- Build the image from a maintained, approved base.
- Generate an SBOM and scan operating-system, language and supported non-packaged components.
- Record the image digest, scanner version, database timestamp and policy result with the build.
- Fail the build or require an explicit exception when the risk policy is exceeded.
2. Scan the registry artifact
Scan the immutable artifact that will be deployed, not only an intermediate local image. Registry scanning catches drift between build and promotion workflows and gives operators a searchable inventory. Recalculate status when advisories change so an image can become high risk even though its bytes did not change.
3. Set a risk-based gate
Define separate treatment for critical or actively exploitable findings, vulnerabilities with an available fix, issues in reachable application code and findings in unused tooling. Specify an owner and expiration date for every exception. The gate should block deployment when the organization cannot accept the documented risk, not merely when a scanner prints a nonzero count.
4. Deploy with least privilege
Runtime monitoring is more effective when workloads already have constrained capabilities, read-only filesystems where possible, non-root identities, restricted host mounts and narrow network permissions. These settings reduce the damage available to an attacker and make anomalous behavior easier to distinguish from normal activity.
5. Enable runtime event collection
Install the runtime sensor or event source on every relevant host or cluster node, confirm the required kernel or eBPF support, and attach container and Kubernetes metadata. Start with rules for shell execution, sensitive file access, privilege changes, namespace operations, runtime-socket access and unexpected network behavior.
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6. Tune rules against real workload behavior
Document expected processes, files, connections and administrative actions for each workload class. Suppress only known-safe events, and make exceptions narrow by namespace, image, process, user or time window. Review rule changes as code so a broad suppression cannot silently remove protection for unrelated workloads.
7. Connect alerts to response
An alert without an owner becomes noise. Define who investigates, what evidence is preserved, and when to isolate a pod, cordon a node, revoke credentials, block egress or redeploy from a known-good digest. Correlate the runtime event with the image SBOM and deployment record: the two evidence sets answer different questions but become more useful together.
Concrete scenarios and the right control
Outdated library inherited from a base image
Static scanning identifies the package version and advisory before release. The fix is normally a base-image update or package upgrade followed by a new scan. Runtime monitoring might see exploitation attempts, but it cannot inventory every vulnerable package or prove that a patched artifact was built.
Compromised application downloads a second-stage payload
The original image may contain no known vulnerable component. After deployment, the process downloads and executes a new file. Runtime rules can detect the unusual network connection, file creation and execution chain; static scanning of the original artifact cannot see those later events.
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Attacker launches a shell in a web container
The image can pass vulnerability policy while a compromised request causes a shell to run. Runtime monitoring can flag the shell, its parent process and subsequent commands. The image scan remains useful for finding vulnerable dependencies and preserving the artifact inventory.
Container accesses the container-runtime socket
A runtime rule can identify the file access and associate it with a pod and service account. Static scanning may show that the client utility exists in the image, but it cannot tell whether the socket was opened in production or whether an escape attempt followed.
New advisory appears after deployment
Continuous or scheduled image analysis can recalculate the vulnerability status of existing registry artifacts and identify affected digests. Runtime monitoring continues to watch behavior but will not automatically enumerate every newly vulnerable package. The operational response is to prioritize rebuilds and redeployments while using runtime controls to detect suspicious activity during the transition.
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Small teams and simple registries
Start with an automated image scan on every build and a registry scan on every promoted digest. Add runtime monitoring to production workloads that process untrusted input, hold sensitive data or run with elevated privileges. Keep the initial rule set small enough for the team to investigate alerts promptly.
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Use centralized SBOM and vulnerability policy for all namespaces, then deploy runtime sensors across worker nodes. Require Kubernetes-aware metadata, ownership labels and a documented escalation path. Separate platform-wide rules from application-specific rules so a change for one service does not weaken another.
High-privilege or internet-facing workloads
Prioritize runtime detection for workloads with host mounts, privileged containers, broad capabilities, administrative interfaces or direct internet exposure. Pair alerts with rapid isolation and credential rotation. Static gates still apply because runtime visibility is not a substitute for reducing the attack surface in the artifact itself.
Restricted or specialized operating systems
Verify kernel, eBPF, container-runtime and plugin compatibility before promising runtime coverage. Verify scanner support for the operating-system distribution, package manager, language ecosystem and image format. “Supported” should mean tested in your exact node and build configuration, not merely listed on a feature page.
Common mistakes and how to correct them
- “The image has no critical findings, so runtime monitoring is unnecessary.” A scan says nothing about a shell launched later, a stolen credential or a downloaded payload. Add behavioral detection where the workload risk justifies it.
- “Runtime alerts tell us which packages are vulnerable.” Event rules show behavior, not a complete SBOM. Keep package scanning and advisory management in the supply-chain workflow.
- Scanning only the developer’s local image. Scan the immutable registry digest that production will pull and retain its result.
- Blocking on every finding. Use severity, exploitability, reachability, fix availability and exposure to define policy; document time-limited exceptions.
- Deploying default runtime rules without tuning. Baseline normal behavior, assign alert ownership and measure investigation capacity before expanding coverage.
- Ignoring metadata quality. Without image digests, namespace, pod, node and service-account context, responders spend time identifying the affected workload instead of containing it.
- Assuming one sensor covers every node. Check kernel and runtime compatibility, event-source health and gaps created by unsupported hosts or ephemeral workloads.
Decision checklist
Use the following questions to design the control set:
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- Which operating-system, language and non-packaged components must be inventoried?
- Where will the policy gate run: pull request, CI pipeline, registry promotion or all three?
- How quickly can the organization rebuild and redeploy after a new advisory?
- Which workloads process untrusted input, access secrets or run with elevated privileges?
- Do the nodes support the required kernel, eBPF and container-runtime integrations?
- Who investigates an alert, and what isolation actions are authorized?
- How will exceptions, rule suppressions and scanner databases be reviewed and expired?
- Can the team correlate an alert to an image digest, SBOM, deployment and owner?
Bottom line for tool selection
Choose an image scanner when the immediate need is component inventory, SBOM evidence, advisory matching or a pre-deployment policy decision. Choose runtime monitoring when you need visibility into process, system-call, file, network, privilege or namespace behavior on live workloads. In a mature container program, these are complementary layers: static controls reduce known risk before release, while runtime controls detect what actually happens after deployment.
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