Wazuh vs Falco for container and Kubernetes security

Wazuh vs Falco for Kubernetes security: which one your team needs first

If your platform or security team must detect suspicious behavior inside containers and Kubernetes, the first question is whether you need a runtime detection engine or a full SIEM. Choose Falco when the need is syscall-level runtime detection on clusters and you already have a place to send alerts. Falco is an Apache-2.0 CNCF graduated project that uses the modern eBPF probe by default and detects abnormal behavior at the kernel event level. It detects and alerts; the documentation describes forwarding alerts to other components to act, not Falco blocking activity. Choose Wazuh when you need a SIEM with agents, rules, dashboards, vulnerability detection and compliance views across hosts and Docker. Wazuh's GPL-2.0 server and agents, with its indexer and dashboard, cover that scope in one platform.

The decision turns on three questions. First, does a SIEM backend already exist? If your organization runs a log platform, Falco events can feed it through Falcosidekick, which forwards Falco events to many destinations. If not, Wazuh brings its own indexer and dashboard, and standing up that stack is a larger operational commitment. Second, who triages alerts? A Falco rule set on a cluster produces events that someone must review, tune and route, so confirm that a named team owns that work before you enable it. Third, does host coverage beyond the cluster matter? If nodes, VMs and workstations also need file integrity monitoring and vulnerability detection, Wazuh's agents cover those areas, which Falco does not address.

The two tools are often not either-or. Wazuh documents an integration in which Falco on a K3s cluster, using the modern eBPF driver, writes JSON events to a file that a Wazuh agent reads, so Wazuh rules can alert on them. Ask your team to confirm which Falco driver and Wazuh version your cluster distribution supports, and to test the rule path end to end with a benign trigger before relying on it. Review the Wazuh blog post on cloud native security with Wazuh and Falco for the documented setup, and check current Falco and Wazuh release notes before you pin versions.

Which platform fits which Kubernetes and container scenario

This table maps common container and Kubernetes situations to the platform that fits each one better, with the reason and the condition under which the other platform wins. Read each row against your own cluster layout, team size and reporting obligations, since the two tools overlap only partly.

Scenario	Better fit and reason	When the other platform wins
A single K3s cluster that needs runtime alerts on suspicious container behavior	Falco, because it is built for runtime detection on hosts, containers and Kubernetes with the modern eBPF driver used by default	Wazuh wins if the cluster sits alongside many non-container hosts and you need one console for all of them
A mixed fleet of Linux hosts and Docker hosts that must report to one SIEM	Wazuh, because its agents collect host data and Docker monitoring feeds one server, indexer and dashboard	Falco wins if you only need syscall-level detection on container nodes and already have a SIEM that ingests its alerts
An audit-driven team that needs compliance dashboards for PCI DSS, GDPR, HIPAA or NIST 800-53	Wazuh, because its dashboard ships prebuilt compliance views for those frameworks	Falco wins only if your evidence needs are limited to runtime alert records and you will build the reporting yourself
A Kubernetes team with no SOC and no one watching alerts around the clock	Falco with Falcosidekick routing alerts to the channels your team already reads, since it runs as a lighter detection layer	Wazuh wins if you need a central place to store, search and triage alerts with a dashboard, which requires operating the indexer and server
A team that already runs a SIEM and wants container runtime events in it	Falco, which writes events to standard output, a file, syslog or an HTTP(S) endpoint that your existing SIEM can ingest	Wazuh wins if you want to replace the existing SIEM rather than extend it, since Wazuh brings its own rules, indexer and dashboard
A cluster that must alert on events that an agent cannot see, such as Kubernetes audit logs	Falco, whose plugins document Kubernetes audit log sources in addition to system calls	Wazuh wins when the same audit data is needed for host-level correlation across many endpoints outside the cluster
A team that needs to block activity automatically, not only alert	Neither platform is documented here as blocking activity, so confirm an enforcement design with each vendor and plan for a separate response tool	Choose the platform that integrates with the response tool you already run, since alerting alone does not stop a workload
A team that runs Falco on K3s and wants its events in Wazuh rules	Both together, using the documented pattern in which Falco writes JSON events to a file that a Wazuh agent reads	Either alone is enough when you do not need Wazuh rules applied to Falco output or a single case view for both

How Wazuh and Falco are built and what that means for operations

Wazuh and Falco solve different parts of the same problem. Wazuh is a multi-component platform that collects, analyzes and stores alerts, while Falco is a runtime detection agent that emits events and leaves storage and response to other tools. The cards below cover what each piece is and what it means for the team that has to run it.

Wazuh's four components

Wazuh runs as an indexer, a server, a dashboard and agents on endpoints. The indexer stores alerts and is an open source fork of OpenSearch, so the team operates a search cluster alongside the Wazuh server and dashboard. Before committing, confirm who will size, patch and back up the indexer, since storage and cluster health usually drive day-two effort more than the rules do.

Wazuh agents on hosts and containers

Wazuh agents are installed on endpoints and read host logs, file integrity, inventory and Docker activity. Agents run on Linux, Windows, macOS, Solaris, AIX and HP-UX, so mixed estates can share one pipeline. Operationally, the team must deploy, version and configure every agent, and confirm that the container hosts it monitors are covered by its agent rollout.

Falco's kernel drivers

Falco reads system calls through a modern eBPF probe, which is the default, or through a kernel module. The eBPF probe avoids building a kernel module for each host kernel, which affects how easily the team can patch nodes. Before rollout, test the chosen driver on every node image and kernel version in the cluster, because driver support determines whether events are captured at all.

Falco plugins for Kubernetes audit logs

Falco plugins extend detection beyond system calls to event sources such as Kubernetes audit logs, AWS CloudTrail and Okta. Kubernetes audit events show API-level actions like privileged pod creation or secret access, which kernel events alone miss. The team must enable audit logging on the API server and route it to Falco, a step that belongs to the cluster platform owner and should be agreed before detection work begins.

Falco alerts, not blocking

Falco delivers real-time alerts to standard output, a file, syslog, a program or an HTTP(S) endpoint. Falcosidekick forwards those events to many downstream applications and channels. The Falco documentation describes alerting and forwarding for other components to act on and does not describe Falco itself blocking activity, so a response workflow, such as a ticket or an orchestrated pod action, must be designed and owned separately.

Wazuh reading Falco events on K3s

Wazuh documents an integration in which Falco on a K3s cluster, using the modern eBPF driver, writes JSON events to a file that a Wazuh agent reads, letting Wazuh rules alert on them. This keeps Falco as the detection source and Wazuh as the correlation and dashboard layer. Ask who maintains the file path, log rotation and agent placement on each node, since a broken file handoff silently stops alerts.

Capability comparison: detection depth, coverage and operating model

This table compares what Wazuh and Falco each see on hosts, containers and Kubernetes, how they are licensed and operated, and how they respond to what they find. Read each row as a question to answer for your own cluster, since the two tools overlap only partly. The main takeaway is that Falco is the narrower runtime detector, while Wazuh is the broader platform that can ingest Falco events alongside host telemetry.

Capability	Wazuh	Falco
License	GPL-2.0 with an OpenSSL linking exception for server and agent; indexer and dashboard under Apache-2.0	Apache-2.0
Runtime visibility	Agent collects logs, file integrity data, inventory and configuration checks from endpoints; syscall-level detection is not the core model	Detects abnormal behavior from kernel events using the modern eBPF probe (default) or a kernel module
Container and Docker monitoring	Docker monitoring is documented; confirm the depth of container-level coverage for your runtime with the vendor	Designed for containers and Kubernetes runtime behavior; confirm driver support for your node OS and kernel
Kubernetes audit log sources	Confirm supported Kubernetes audit ingestion with the vendor for your cluster version	Kubernetes audit logs are a documented plugin source alongside AWS CloudTrail and Okta
Rules and MITRE ATT&CK mapping	Over 3,000 built-in rules and decoders, with alerts mapped to MITRE ATT&CK; custom rules and decoders supported	Custom rules define detections; confirm which MITRE ATT&CK mappings ship with the rule set you use
Vulnerability detection	Correlates agent software inventory with CTI platform or offline vulnerability data	Not a documented function of Falco
Compliance dashboards	Prebuilt views for PCI DSS, GDPR, HIPAA and NIST 800-53	Not a documented function of Falco; use Falcosidekick or a downstream SIEM for compliance reporting
Alert response and blocking	Active response is documented on hosts; confirm its scope inside containers and Kubernetes pods with the vendor	Documents detection and alerting only, forwarded to outputs such as stdout, file, syslog or HTTP(S)
Central server and dashboard	Central server, indexer and dashboard, with server clustering for horizontal scaling	No central server of its own; events go to outputs such as Falcosidekick and the systems you connect

What each license allows and what is not paid for

Wazuh's server and agent are distributed under GPL-2.0 with an OpenSSL linking exception, so you can run, modify and redistribute them under the GPL terms. The indexer and dashboard are licensed under Apache-2.0, which is a different license with its own obligations. The supplied facts do not name a paid Wazuh edition, so the license itself does not gate features in the way a commercial tier would. Wazuh Cloud is available as a managed option, with its own terms and tier pricing that you should review directly on the vendor's site.

Server and agent: GPL-2.0 with the OpenSSL exception governs the components that run on your Kubernetes nodes and collect host data.
Indexer and dashboard: Apache-2.0 covers the storage and interface layers, which are separate from the GPL components.
No named paid edition: The supplied facts do not list a paid Wazuh tier, so confirm whether any commercial features or support plans exist.
Managed option: Wazuh Cloud is a hosted alternative that changes who operates the platform, not the open-source license of the self-hosted code.
Modification duties: If you modify GPL components and distribute them, review the GPL obligations with your legal team.

Ask Wazuh in writing which commercial support, subscription or training options exist and what each includes.

When Falco alone is enough for a Kubernetes team

Falco alone is enough when the detection problem is runtime behavior on nodes and pods, and the team already has somewhere to send alerts. Falco's modern eBPF probe, the default driver, observes system calls, and its custom rules can flag things like a shell spawned inside a container or unexpected file access. If the team wants that syscall-level signal and nothing else, a Falco deployment with Falcosidekick forwarding events to an existing SIEM or log platform covers the core need. Confirm that the receiving platform can ingest Falcosidekick outputs in the format your team chooses before you commit to this design.

The limit to plan for is that Falco documents detection and alerting, not blocking. Its alerts go to standard output, a file, syslog, a spawned program or an HTTP(S) endpoint, and any containment step has to be built in your own response workflow, such as a ticket, a runbook or an automation that cordons a node or kills a pod. Budget for building and testing that workflow, and for the people who own it. Falco can also read Kubernetes audit logs, and its documented plugins cover sources such as AWS CloudTrail and Okta, so API-server activity and cloud control-plane events can sit alongside syscall alerts in the same pipeline.

Falco alone is less likely to suffice if the team also needs agent-based host inventory, file integrity monitoring or vulnerability correlation across its nodes. Those controls are outside Falco's scope, and Wazuh covers them through its agents and the software inventory they collect. Ask the team to write down which of those requirements an auditor or incident responder will expect to see before choosing. If the answer is none, Falco with an existing log platform is a lighter option, and you should still verify how the chosen SIEM handles Falco's JSON events.

Running Wazuh and Falco together: the documented pattern

The documented starting point is a single K3s cluster where Falco runs with the modern eBPF driver and writes JSON events to a file. A Wazuh agent on the same node reads that file, and Wazuh rules turn the events into alerts. The integration is narrow, so the work is mostly configuration and rule review rather than new infrastructure.

Falco driver and output: Confirm Falco is set to the modern eBPF probe, which is the default, and that JSON file output is enabled with a path the agent user can read.
Agent localfile entry: Add the Falco output file as a localfile source in the Wazuh agent configuration so events reach the Wazuh server.
Rule mapping: Review which Falco rule names and priorities Wazuh rules match, so that only meaningful events raise alerts.
Single owner: Assign one team to own both the Falco rule set and the Wazuh decoding and alerting, since a change in either affects the other.
Log growth: Track the size of the Falco file and set rotation, because a busy node can produce a large event stream on a single disk.

Check that Falco rules and Wazuh rules are not both alerting on the same behavior, and confirm the file rotation settings with your own K3s setup before go-live.

What drives the running cost of Wazuh and Falco

Falco's license is not the cost. Falco is licensed under Apache-2.0, so the spend sits in the infrastructure around it and in the people who tune and act on its output. Falco runs on every node it watches, using the modern eBPF probe by default or a kernel module, so the first recurring cost is the compute and memory on each cluster node. Wazuh is also GPL-2.0 for its server and agent, with the indexer and dashboard under Apache-2.0, so its license is not the cost either. Its recurring costs are the indexer storage that holds alerts, the server capacity that analyzes agent and event data, and the hosts those components run on.

Some costs are one-time and some recur. The Falco-to-Wazuh integration, the agent configuration that reads Falco's JSON output, and the custom rules that turn raw events into useful alerts are mostly one-time engineering, although rules keep changing as workloads change. Indexer storage, retention settings, Wazuh and Falco upgrades, rule tuning to cut false positives, and on-call triage recur for as long as the platform runs. Triage is usually the largest line, because every alert a team cannot classify quickly costs analyst time, and noisy runtime rules multiply that cost across clusters.

When you request a quote, ask for each item separately: the Wazuh indexer and server sizing for your endpoint and event volume, the retention period assumed for alerts, the hosts or cloud accounts that will run both platforms, the one-time integration and rule-writing effort, the recurring upgrade and tuning effort, and whether any vendor support contract is included or optional. Ask who holds the on-call rotation and who owns rule changes after handover. Confirm current Wazuh release and retention details with the Wazuh release notes and Falco release details with the Falco releases page before you fix a budget.

Moving from Falco-only to a Wazuh-backed design, or back

This sequence moves detection from Falco alone into a design where Falco events flow into Wazuh (or back out again), leaving one clearly owned alert path per node and a tested record of which detections survived the move.

Inventory existing Falco rules and outputs
The platform or security engineering team lists every custom and default Falco rule in use, the outputs each one feeds (standard output, file, syslog, program or HTTP endpoint, typically through Falcosidekick), and which teams act on each alert. Export the rule files from the cluster or the Helm values and record the Falco version. The step is done when every rule has an owner, a current output and a documented purpose, so later decisions rest on a complete list rather than memory.
Decide the alert destination
Decide whether Wazuh becomes the system of record for Falco events, or whether Falco keeps its existing outputs and Wazuh only receives a copy for correlation with host telemetry. The security lead and the on-call owner make this call and sign it off in writing, because it determines who gets paged and where retention lives. Confirm with your own Wazuh and Falco documentation that the chosen output type is supported in your versions before building anything.
Configure the eBPF driver and file output
On each node, confirm Falco runs with the modern eBPF probe, which is the default driver in the Falco documentation, and configure the file output to write JSON events to a path the Wazuh agent can read. The platform engineer changes the Falco configuration and restarts the service in a staging cluster first. The step closes when Falco events appear in the file after a test syscall-triggering action, and the event fields match what the rule set expects.
Install the Wazuh agent on the node
Install the Wazuh agent on each Kubernetes node that writes Falco output, enroll it with the Wazuh server, and add a localfile entry that points at the Falco JSON file. The agent must have read access to that path without broadening permissions on the Falco service account. Check the agent appears as active on the server and that the log collection entry is picked up in the agent logs.
Port custom rules into Wazuh decoders and rules
Translate the inventory into Wazuh decoders that parse the Falco JSON fields and Wazuh rules that reproduce the intent of each Falco rule. Rule logic and alert intent carry over, but field names, severity mapping and grouping must be rebuilt because Wazuh rules match on decoded fields rather than Falco rule syntax. The detection engineer tests each ported rule against recorded Falco events and documents any detection that cannot be reproduced exactly.
Run both alert paths in parallel
Keep the existing Falco outputs and the new Wazuh alerts active side by side for a period agreed in your change plan. Compare alert volumes, false positives and missed detections for the same test activity, and rebuild any dashboards, notifications and response playbooks in Wazuh that the old path served. Dashboards, response playbooks and notification routing are the parts that must be rebuilt, not copied. The step closes when the team signs off that every critical alert appears on the new path.
Retire duplicate alerting
Once parallel running is signed off, disable the duplicate Falco output or the Wazuh rules that now fire twice, and update the runbook so responders know the single source of alerts. Keep the Falco rule files under version control so a rollback remains possible. The step is complete when one alert path per event type is documented, duplicate notifications stop in the test window, and the change record names the owner.

What implementing each involves and where projects run long

Wazuh implementation starts with the central stack: the Wazuh indexer, the Wazuh server and the Wazuh dashboard, then agent rollout across every host that should report. Sizing the indexer and server is the first decision, and it depends on the endpoint count, the alert volume and how long you keep data. Confirm the sizing guidance for your deployment type in the Wazuh documentation before you pick hosts, and decide early whether you need a single node or a cluster. Agent rollout across Linux, Windows and container hosts is usually the longest step, because each host needs an agent version that matches the server, a group policy and a check that events arrive in the dashboard. For Docker monitoring, plan which container hosts run the agent directly and confirm the Docker monitoring settings for your runtime.

Falco implementation is less about infrastructure and more about decisions. You choose a driver first: the modern eBPF probe is the default, and the kernel module is the alternative, so confirm that your node operating systems and kernel versions support the driver you pick. Then you author or tune rules, add plugins such as Kubernetes audit logs if you need events beyond system calls, and route alerts through Falcosidekick to the channels your responders use. In the Wazuh and Falco pattern, the Falco JSON output file also has to be read by a Wazuh agent on the same node, so the file path, permissions and rotation become part of the rollout.

The usual slow points are the same on both sides. False-positive tuning takes longer than installation, because default rules will fire on legitimate admin and build activity in your clusters, and someone has to decide which exceptions are acceptable and document them. Kernel and node compatibility checks slow projects down when node pools run mixed kernels, and Falco driver support must be verified per node group. The hardest decision is alert ownership: name who receives Falco and Wazuh alerts, who acts on them, and where duplicates are suppressed, before go-live. Ask any implementation partner to write these decisions into the scope, so the handover does not stall on them.

Questions buyers ask before choosing Wazuh or Falco

Does Falco block attacks on our Kubernetes nodes?

No. Falco detects and alerts on abnormal behavior, and its documentation describes forwarding alerts to other components to take action rather than Falco itself blocking activity. If you need a process to be stopped, you need a separate control, such as a policy engine, admission control or an endpoint response tool, and you should confirm what that control can enforce in your cluster. Wazuh's active response can run scripts after an alert, but whether a given response fits your container workloads is a design question to settle during the pilot.

Is Wazuh enough for Kubernetes security on its own?

It depends on what you need to see. Wazuh agents monitor the hosts and report on Docker activity, and its ruleset covers many host-level events, but Falco's eBPF probe is built for runtime syscall detection across hosts, containers and Kubernetes, and it can add Kubernetes audit log and cloud plugin sources. If your main requirement is central log retention, compliance views and host file integrity, Wazuh may be sufficient. If you need container-level syscall detail, plan to add Falco and confirm the coverage you need against both tools' documentation.

Which driver should Falco use on our nodes?

Falco's documentation describes two kernel drivers: the modern eBPF probe, built on the CO-RE paradigm and used by default, and a kernel module. For most teams the modern eBPF probe is the starting point because it is the documented default and avoids loading a kernel module. Confirm that your node operating system kernels support it, and check the Falco documentation for your distribution before rollout. The kernel module is worth evaluating only if your kernels cannot run the eBPF probe, since it adds kernel-level operational risk.

What does running Wazuh and Falco together cost in effort?

Both tools are open source, so the cost is staff time, infrastructure and integration work rather than a license fee. Falco needs its driver configured per node class, rules tuned to your workloads and an output path, such as a file that a Wazuh agent reads. Wazuh needs its indexer, server and dashboard sized for the event volume, plus rules and decoders for Falco's JSON events. Ongoing effort comes from upgrades, rule tuning and alert triage. Ask any implementation partner for a written scope that lists each of these items separately.

How long does a pilot take and what does it need from our team?

Pilot length depends on cluster count, the number of event sources you add, the rules you tune and how many people review alerts. Your team needs cluster access, a test environment that matches production node images, someone who owns the Falco rule set, and someone who owns the Wazuh rules and dashboards. Agree on the pilot's success criteria, such as which detections must fire during test scenarios, before work starts. DeployOpen plans and delivers most engagements as fixed-scope work, so the timeline and deliverables are set in writing before the pilot begins.

Can our own staff run Wazuh and Falco after handover?

Yes, if they have Linux, Kubernetes and detection engineering skills and time to maintain the platform. Falco rules are text files you can version and review, and Wazuh rules and decoders are also editable, but both require testing before changes reach production to avoid noisy or missed alerts. Plan for upgrades: Wazuh releases patches on a regular cadence in the 4.14 line, and Falco publishes its own releases. Check which upgrade and support tasks your team will own and whether a retainer for ongoing support fits your staffing.

Does Wazuh Cloud change the picture for Falco?

Wazuh Cloud changes where the Wazuh platform runs, not what Falco does on your nodes. Falco still runs as a driver and rule engine on your clusters and needs a path to send events, whether that is a file read by a Wazuh agent or another output. Before choosing a managed Wazuh service, confirm how it handles agent data, which regions are available and what access you get, since the managed service's dashboard and API access may differ from a self-hosted deployment. Check your data residency and compliance requirements with the vendor first.

How DeployOpen helps choose, implement and integrate these tools

DeployOpen plans, implements, integrates, documents and supports self-hosted open-source platforms inside your own infrastructure, including the Wazuh agents and Falco runtime sensors discussed here. Engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts.

How we scope

Confirm which Kubernetes distributions, node operating systems and container runtimes the written scope must cover
Define whether Falco events are read through the Wazuh agent or forwarded to another alerting path
Agree in writing which rules, decoders and alert destinations are in scope and who owns tuning

How we build

Deploy Wazuh server, indexer and dashboard in your environment, sized to the agreed endpoint and cluster count
Install Falco with the driver your kernels support, then route its JSON output into a file the Wazuh agent reads
Test detections against agreed attack scenarios and record which events reach Wazuh rules and which stay in Falco

How we hand over

Document architecture, agent placement, file paths, rule mappings and the decisions behind each integration choice
Provide runbooks covering agent upgrades, Falco rule updates and how alerts are triaged end to end
Walk your platform, security and compliance teams through evidence the environment produces for later assessment

How we support

Offer ongoing support, upgrades and continuous testing as a retainer after handover
Review Wazuh and Falco release notes and plan upgrades against your change windows and compatibility checks
Retest detections after Kubernetes or kernel upgrades that could change what Falco and the agent observe