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Container image scanning finds known weaknesses in software before deployment; it cannot see what a process does after launch. To detect runtime threats, combine kernel-level behavior monitoring with Kubernetes controls: Falco can alert on suspicious system calls and container activity, while Cilium Tetragon adds Kubernetes-aware eBPF observability and policy enforcement. Admission controls, RBAC, audit logs, network policies and workload isolation provide preventive and investigative layers around those detectors.
The practical choice depends on the events you need to see, whether you must block activity or only alert, your Linux kernel and container-runtime support, and how much rule tuning your team can operate. No single replacement makes image scanning unnecessary; runtime security covers a different point in the attack lifecycle.
Why image scanning cannot detect every container threat
Image scanners inspect an artifact such as an OCI image, usually by matching package versions and configuration against known vulnerability data. That is valuable before deployment, but the scan is a snapshot. It does not observe a shell started by a compromised application, a process that appears only after a container launches, a stolen credential used against another service, or a payload downloaded at runtime.
Runtime detection watches activity on the running node and in the cluster. It can identify behavior such as an unexpected executable, a process accessing sensitive files, unusual outbound connections, privilege changes or attempts to escape isolation. Those signals may come from a vulnerable component, a supply-chain compromise that has not yet received a vulnerability identifier, a misconfiguration, or a stolen credential. The two controls therefore answer different questions: “What risk is in this artifact?” versus “What is this workload doing now?”
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Runtime controls also have a different evidence model. A scanner reports package and configuration findings tied to an image digest. A runtime sensor records events, identities and context: the process, system call, file or network action, container, pod and namespace involved. That context is what allows an operator to investigate and, where supported, stop the action.
Falco: syscall and event detection with Kubernetes context
Falco describes itself as a real-time threat-detection solution for containers, hosts, Kubernetes and the cloud. Its sensor instruments the Linux kernel through either a kernel module or an eBPF probe and can monitor system calls made within containers. Falco then enriches events with container and Kubernetes attributes, such as the namespace or pod name, so an alert identifies the workload rather than only a node-level process.
What Falco can detect
Falco rules can flag behavior associated with crypto mining, file exfiltration, privilege escalation and rootkit installation. The project supplies rules and allows custom rules, letting a team express expectations such as “this service may not execute a shell,” “this pod must not read a host-sensitive path,” or “a process in this namespace must not make this type of connection.” Rules are event conditions, so their usefulness depends on selecting the right signals and exceptions for each workload.
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Detection versus response
Falco is principally an event detector and alerting engine. It can emit findings to logging, messaging or security-monitoring pipelines, where an incident workflow can quarantine a workload, revoke credentials or investigate the node. Treating every alert as an automatic kill action is risky: legitimate package managers, debuggers, sidecars and maintenance jobs can resemble malicious behavior. Start with alerting, measure recurring benign patterns, and introduce narrowly scoped response automation only after validation.
Operational requirements
Because Falco observes kernel events, verify that the node kernel, architecture and security settings support the selected driver. A kernel-module deployment and an eBPF deployment have different compatibility and maintenance characteristics. Container-runtime and Kubernetes metadata must also be available to the sensor for useful enrichment. Plan upgrades and driver validation as part of node-image maintenance rather than treating the detector as an application installed once.
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Cilium Tetragon: eBPF observability and enforcement
Tetragon is a Kubernetes-aware security observability and runtime-enforcement tool that applies policy and filtering directly with eBPF. It monitors processes, system calls, file activity and network activity in the kernel. Namespace and pod identities let its events and policies refer to Kubernetes workloads, not merely IP addresses or host process IDs.
Broader event correlation
A process event can be correlated with the file it opened, the system call it made and the network destination it contacted. Tetragon’s documentation emphasizes correlating threats with network data to identify the responsible binary. That relationship is useful when an application unexpectedly downloads an executable and immediately connects to an external service: the investigation can follow the process, file and connection as one chain of activity.
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Enforcement at the sensor
Tetragon supports policy enforcement as well as observation. Policies can filter or block selected process, syscall, file or network actions in the kernel. Blocking should be limited to actions whose legitimate use is well understood, such as preventing a production workload from executing a known administrative utility. Use staged rollout, namespace or workload scoping and an explicit exception process so that enforcement does not interrupt normal releases or incident response.
Dependencies and fit
Tetragon requires an eBPF-capable Linux environment and must be deployed with the permissions needed to observe and enforce the selected events. Confirm support for the kernel versions and distributions used by every node pool, including managed Kubernetes pools. Its Kubernetes awareness is strongest when pod and namespace identity data remain available during scheduling, restarts and upgrades.
Preventive and investigative controls that complement runtime sensors
Admission policies
Admission controls act before a workload is accepted by the API server. Kubernetes’ application-security guidance identifies image scanning as only one input and recommends additional policy. A ValidatingAdmissionPolicy can reject a pod that violates organizational requirements, such as running with an unsafe security context, requesting excessive privileges or omitting required labels. Admission cannot see post-launch behavior, so it should be paired with runtime monitoring rather than substituted for it.
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RBAC and service-account boundaries
Use narrowly scoped service accounts and roles. A compromised pod should not automatically be able to list secrets, create workloads, alter role bindings or access unrelated namespaces. Review permissions for both applications and operators, and pay particular attention to who may use interactive execution or read sensitive objects. Restricting these capabilities reduces what an attacker can do after gaining code execution and makes suspicious access easier to interpret.
Kubernetes audit logging
Audit logs record API actions such as creating a pod, reading a secret, changing a role or opening an execution session. They answer questions that kernel sensors cannot: which identity requested an API action, through which API path and with what response. Forward logs to a protected, searchable destination and retain enough history to reconstruct an incident. Correlating an audit event with a Falco or Tetragon process event can distinguish an authorized deployment from an unexpected change.
Network policies and identity-aware networking
Network policies reduce reachable services and limit lateral movement. Cilium’s security model highlights the advantage of basing visibility and control on service, pod and container identity rather than only IP addresses, which can change as pods are rescheduled. Define allowed ingress and egress by workload purpose, namespace and destination. Network policy will not reveal a malicious action that stays within an allowed connection, so keep process and file telemetry for that gap.
Workload isolation and runtime classes
Sensitive workloads may require stronger isolation than ordinary containers. Kubernetes security guidance includes Linux container security and runtime-class choices; an isolated runtime can reduce the impact of a container escape or kernel-facing exploit. Isolation has scheduling, compatibility and operational costs, so apply it to workloads with a clear threat or data boundary and verify that monitoring agents support the chosen runtime.
How the approaches compare
| Approach | Primary coverage | Kubernetes identity | Can enforce? | Main dependencies | Best use |
|---|---|---|---|---|---|
| Falco | Kernel events and system calls, with rules for process, file and other suspicious activity | Yes; events can include container, namespace and pod attributes | Detection and alerting; response is normally handled by downstream automation | Linux kernel, selected driver (kernel module or eBPF), container metadata and rule pipeline | Broad runtime threat detection and adaptable alerting |
| Cilium Tetragon | Processes, syscalls, file access and network activity | Yes; policies and events use namespace and pod identities | Yes; eBPF policy and filtering can block selected actions | eBPF-capable kernel, node permissions, Kubernetes identity data and policy management | Correlated observability with fine-grained runtime enforcement |
| Admission policy | Pod specifications and API requests before launch | Yes; evaluates Kubernetes objects and identities | Yes; reject or deny non-compliant requests | Kubernetes API admission configuration and maintained policy definitions | Preventing unsafe deployments |
| RBAC | Kubernetes API permissions | Yes; user, group and service-account identities | Yes; deny unauthorized API actions | Role and binding design, review and lifecycle management | Limiting blast radius of compromised credentials |
| Audit logging | Kubernetes API activity and administrative actions | Yes; records requesting identities and resources | No; investigative and detection signal | Configured audit policy, protected storage and analysis pipeline | Forensics, accountability and correlation |
| Network policy | Allowed workload communications | Identity quality varies by networking implementation; identity-aware systems avoid IP-only rules | Yes; allow or deny selected traffic | Network-policy-capable CNI and accurate workload selectors | Reducing lateral movement and unintended exposure |
The official project material establishes these capabilities, but it does not provide a controlled, current comparison of detection accuracy, false-positive rates or resource overhead. Select on coverage, enforcement needs, compatibility and operating model rather than an unverified performance ranking.
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A practical layered implementation plan
- Define the threats and assets. List the behaviors that matter for your environment: unexpected shells, access to host paths, credential or secret reads, privilege changes, unsigned binaries, unusual outbound connections, crypto-mining patterns and attempts to alter Kubernetes objects. Separate high-impact actions that might justify blocking from signals that should initially alert.
- Establish preventive Kubernetes policy. Apply least-privilege service accounts and RBAC, require safe pod security settings through admission policy, and restrict who can use interactive execution or access secrets. Record policy exceptions with an owner and expiry date.
- Constrain communications. Start with a map of required service-to-service flows, then implement ingress and egress network policies. Test rollout in observation mode where the networking implementation supports it, because an over-broad deny can break DNS, telemetry or control-plane access.
- Choose a runtime sensor. Choose Falco when flexible syscall/event rules and downstream alerting fit your operating model. Choose Tetragon when eBPF-based process, file and network correlation plus kernel-level enforcement are priorities. Validate kernel, architecture, node-image and CNI compatibility before enabling every node pool.
- Route and enrich events. Send findings to a protected logging or security-monitoring system with timestamps, node, namespace, pod, container, process, image digest and rule or policy name. Keep enough context to connect a runtime event with Kubernetes audit activity and deployment history.
- Tune in stages. Begin with detection-only rules for a representative set of workloads. Record legitimate exceptions by workload identity and operation, not by disabling a broad rule. Review noisy rules, deployment changes and new software versions on a regular schedule.
- Define response playbooks. For a suspected compromise, preserve relevant logs, identify the pod and node, isolate network access, rotate exposed credentials, capture the image digest and deployment metadata, and rebuild from a trusted artifact. If an enforcement policy blocks a legitimate action, provide a documented break-glass path and review the event afterward.
Designing useful Falco rules
A useful rule combines an event with context and an exception. “A shell executed” is rarely sufficient on its own: deployment hooks, diagnostics and maintenance containers may legitimately run shells. Add workload identity, namespace, parent process, user, image or path conditions, then document why an exception exists. Rules for writing to sensitive host locations, loading kernel modules, changing credentials or opening unexpected outbound connections generally carry more investigative value than generic “process started” alerts.
Use severity to prioritize rather than to imply certainty. A high-severity finding should identify the exact process and container, the triggering event, and the fields an analyst needs to verify it. Keep rule changes in version control, test them against representative workloads, and roll back a noisy rule without disabling the entire sensor.
Designing Tetragon policies without breaking workloads
Separate observation policies from enforcement policies. First collect process, syscall, file and network events for normal release, startup, backup and debugging paths. Then select a narrow action for enforcement, scope it to a namespace or labeled workload, and define the expected failure behavior. A policy that blocks every execution of a utility may stop an attacker but can also stop a language runtime, health check or migration job that relies on that utility.
Use the identity context in policy selectors. A pod label or namespace boundary is more stable than a transient IP address, while an image digest can distinguish a changed build of the same application. Revisit selectors when deployments, sidecars or service-account assignments change. Enforcement should be paired with an operator workflow that can explain why an action was denied and how to authorize an emergency exception safely.
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Runtime threat scenarios and the right signal
A compromised web process launches a shell
Image scanning may show that the web image has no known vulnerable package, yet an exploit can still invoke a shell after launch. A Falco process-execution rule or a Tetragon process event can identify the shell, parent process, pod and namespace. RBAC and network policy then limit what the shell can access, while audit logs reveal any Kubernetes API requests made with the pod’s credentials.
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A process reads a sensitive mounted file
The image may be unchanged while an attacker abuses a running process or an over-permissive mount. File-access telemetry can show which binary opened the path and under which workload identity. Admission policy can prevent unsafe mounts in future deployments; runtime policy can alert or block the access when the workload is already running.
A pod begins unexpected outbound communication
Network policy can deny destinations that the service does not require. Tetragon can correlate the connection with the process and binary that opened it, while Falco can alert on the associated event pattern when its rules and metadata provide that context. The combination distinguishes a normal sidecar connection from a newly downloaded executable beaconing externally.
An attacker uses Kubernetes credentials
Kernel sensors may show a process reading a token, but Kubernetes audit logging records the subsequent API request and identity. Least-privilege RBAC limits the available actions; admission policy can block a malicious attempt to create an unsafe workload; and network restrictions can reduce access to the API server or other services. Investigators need all three views to reconstruct the sequence.
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- Collecting everything without a triage plan: High-volume events become background noise. Start with a small set of high-impact behaviors and define who receives each severity.
- Assuming an image scan is a runtime control: A clean scan does not establish that the running process, credentials or network behavior is safe. Keep artifact checks and runtime checks in separate deployment gates.
- Ignoring kernel and node diversity: Driver or eBPF support can differ across distributions, architectures and managed-node pools. Validate coverage after node upgrades and alert when a sensor is absent.
- Using IP-only policy: Pod addresses change. Prefer workload identities, namespaces and stable labels where the networking and runtime tools support them.
- Blocking before tuning: Enforce only after observing normal behavior and documenting exceptions. Keep a break-glass procedure that does not require disabling all security controls.
- Leaving logs unprotected: Attackers who control a workload may try to delete or alter local evidence. Forward events to access-controlled storage and monitor delivery failures.
- Forgetting legitimate operational paths: Debugging, migrations, backups and incident response often require actions that production applications normally do not. Model those workflows explicitly rather than creating permanent broad exemptions.
How to choose an approach
Choose Falco as the first runtime layer when your priority is broad, rule-driven syscall and event detection with Kubernetes enrichment and integration into an existing alerting workflow. Choose Tetragon when you need correlated process, file and network visibility tied to Kubernetes identities and want the option to enforce selected actions in the kernel. In either case, add admission policy, RBAC, audit logging, network policy and suitable isolation according to the sensitivity of the workload.
For a small cluster, begin with one sensor, a narrow rule set and a clear response owner. For a multi-team platform, standardize event fields, policy labels, exception lifetimes and audit retention so that each team does not invent incompatible controls. Reassess coverage after major kernel, CNI, runtime or Kubernetes upgrades.
Runtime monitoring is the answer to the question image scanning cannot answer: what is this container doing while it runs? Falco and Tetragon provide the behavioral view; Kubernetes preventive and investigative controls constrain the blast radius and explain administrative activity. The strongest design uses those layers together, with enforcement introduced deliberately after the organization understands normal workload behavior.
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