Falco or Tetragon: when each one fits your runtime security plan
A team that has runtime security on its shortlist must first decide whether it needs alert-first detection or in-kernel enforcement, because the two tools answer different questions. Falco fits when the goal is rule-based detection and alerting across hosts, containers and cloud audit sources, and when the alerts must land in an existing SIEM pipeline. Its documentation describes detection and alerting, with alerts forwarded to other components to act on, and it does not describe Falco itself blocking activity. Falco's plugins extend event sources beyond system calls to Kubernetes audit logs, AWS CloudTrail and Okta, which matters if identity and cloud control plane events belong in the same investigation. Output can go to standard output, a file, syslog, a program or an HTTP(S) endpoint, and Falcosidekick forwards events onward, so a team with a log pipeline already in place can usually connect Falco without replacing it.
Tetragon fits when the workloads are Kubernetes-native and the team wants policy applied in the kernel to specific workloads. Its TracingPolicy custom resource traces kprobes, tracepoints and uprobes and can act on a match, for example by killing a process before it completes a system call. Tetragon is Kubernetes-aware, so it can target namespaces and pods, and it runs as part of a Cilium-based stack. Confirm whether your clusters already run Cilium or plan to, because the Tetragon path assumes that dependence. Writing TracingPolicies requires knowledge of the Linux kernel and containers, and the Tetragon documentation warns about issues such as TOCTOU bugs, so budget engineering time for policy authoring and testing on non-production nodes before enforcement is enabled. Four decision drivers should settle the choice. First, decide whether you need to block or only alert, since enforcement in Tetragon changes the failure mode of a bad policy. Second, assess rule authoring skills, because Falco rules are the lighter learning curve for most security teams while TracingPolicies demand kernel-level expertise. Third, check Cilium dependence against your network architecture. Fourth, measure overhead on your own nodes under production-like event rates, since the Tetragon documentation says in-kernel filtering reduces overhead for high-frequency events but does not give a figure you can plan against.
Which scenarios favor Falco and which favor Tetragon
This table maps common operating scenarios to the platform that fits better and the condition that decides it. Read each row against the documented difference: Falco detects and alerts in user space, while Tetragon can filter and act on events inside the kernel through TracingPolicy resources.
| Scenario | Better fit | Condition that decides it |
|---|---|---|
| Alert-only detection feeding an existing SIEM | Falco | Falco forwards events to outputs such as files, syslog or HTTP(S) endpoints, so it suits a team whose response already lives in a SIEM or SOAR. Confirm your SIEM can ingest Falco JSON output. |
| Kubernetes enforcement that must stop a process | Tetragon | Tetragon can block or kill a process in the kernel before a system call completes. Falco's documentation describes alerting and forwarding for action, not blocking, so choose Tetragon only if you will write and test enforcement policies. |
| Non-Kubernetes Linux hosts | Falco | Falco runs across hosts, containers and cloud environments. Tetragon is Kubernetes-aware and its policies are most naturally applied to workloads, so confirm its host-only coverage for your use case before choosing it. |
| Clusters already running Cilium | Tetragon | Tetragon is maintained within the Cilium project and fits clusters that already depend on Cilium networking. Without Cilium, Tetragon adds a new component to operate and the advantage narrows. |
| Teams without deep kernel expertise | Falco | Falco ships with a rule language and a default modern eBPF driver. The Tetragon documentation warns that tracing policies require knowledge of the Linux kernel and containers, such as TOCTOU issues, so a small team should budget time for that learning. |
| Teams that need cloud audit sources alongside runtime events | Falco | Falco plugins document sources such as Kubernetes audit logs, AWS CloudTrail and Okta. Tetragon's documented focus is kernel event observability, so confirm any cloud audit ingestion before relying on it. |
| High-frequency events where overhead is the main concern | Tetragon | The documentation says in-kernel filtering reduces overhead for high-frequency events. Measure both tools on your own nodes and workload, because the benefit depends on event volume and rule count. |
How Falco's rule engine and Tetragon's kernel policies are built
Falco and Tetragon both watch kernel activity with eBPF, but they place their logic in different spots. Falco evaluates rules in a user-space engine and sends alerts out, while Tetragon compiles policy into the kernel and can act on matches. The cards below cover where each piece runs and what that placement means for the team that operates it.
Falco's user-space rule engine
Falco reads kernel events through its driver, then evaluates them against rules in a user-space engine and sends alerts to outputs such as standard output, a file, syslog, a program or an HTTP(S) endpoint. Detection logic is therefore written as YAML rules that anyone on the team can read and review. Check how many events your nodes generate at peak, because the engine must process every event that matches a rule's event types before it can alert.
Falco's modern eBPF and kernel module drivers
Falco documents two kernel drivers. The modern eBPF probe, built on the CO-RE paradigm, is the default, and a kernel module remains available. Driver choice affects which kernels and managed Kubernetes nodes you can support, so test the chosen driver on each node pool before rollout. Keep the driver in your change record, since upgrading the node kernel can change which option is safe.
Falco plugins beyond system calls
Falco plugins add event sources and fields beyond system calls. The documented sources include Kubernetes audit logs, AWS CloudTrail and Okta, so one rule set can cover node behavior and control-plane or identity activity. Each plugin is a separate component to version, configure and test. Confirm which plugin versions your Falco build supports before you write rules that depend on their fields.
Tetragon's in-kernel TracingPolicy
Tetragon applies policy inside the kernel, so filtering happens before events reach a user-space agent, which the documentation says reduces overhead for high-frequency events. A TracingPolicy is a Kubernetes custom resource that attaches kprobes, tracepoints or uprobes and can filter on function arguments and return values. Writing these policies requires kernel and container knowledge, and the documentation warns about pitfalls such as TOCTOU bugs.
Tetragon's Kubernetes awareness
Tetragon understands identities such as namespaces and pods, so a policy can target a specific workload instead of every process on a node. This matters when you want enforcement scoped to one application team's namespace. Cilium dependence is the trade-off to review: confirm your cluster networking and admission setup before you adopt Tetragon as a standalone control.
Enforcement and day-two ownership
Falco's documentation describes alerting, with forwarding to other components to take action, and does not describe Falco itself blocking activity. Tetragon can kill a process when its privileges change before the system call completes. Blocking in the kernel means a faulty policy can stop legitimate workloads, so ask who owns policy review, staged rollout and rollback before you enable enforcement.
Capability differences between Falco and Tetragon
This table compares how Falco and Tetragon detect, author policy, and fit into Kubernetes and Cilium environments. Read each row against your own requirement: if you need alerts that feed existing pipelines, Falco's model is the starting point, and if you need the kernel to act on a match, Tetragon's model applies, with the kernel-knowledge caveats noted below.
| Capability | Falco | Tetragon |
|---|---|---|
| Primary behavior | Detects and alerts on abnormal runtime behavior; the documentation describes forwarding alerts to other components to act, and does not describe Falco blocking activity | Filters, blocks and reacts to events in the kernel; the documentation gives the example of killing a process when its privileges change before the system call completes |
| Where policy runs | Rule evaluation in a user-space engine fed by kernel events from the eBPF probe or kernel module | Policy and filtering applied inside the kernel, which the documentation says reduces overhead for high-frequency events |
| Authoring model | Custom rules that match events and emit alerts to standard output, a file, syslog, a program or an HTTP(S) endpoint | TracingPolicy custom resources that trace kprobes, tracepoints and uprobes and optionally act on matches; also loadable with the tetra CLI or at startup outside Kubernetes |
| Authoring skill required | Rule syntax and runtime behavior knowledge; confirm the rule set your team can maintain against your workloads | Documentation warns that writing tracing policies requires Linux kernel and container knowledge to avoid issues such as TOCTOU bugs |
| Event sources beyond system calls | Plugins add sources such as Kubernetes audit logs, AWS CloudTrail and Okta | Hooks kernel functions and filters on arguments and return values; confirm any non-kernel source before relying on it |
| Kubernetes identity awareness | Kubernetes context is available to rules; confirm the metadata fields your rules need in your deployment | Understands identities such as namespaces and pods so detection can target individual workloads |
| Cilium dependence | Runs independently of Cilium; confirm the kernel and driver requirements for your nodes | Part of the Cilium project; confirm how its components are deployed in clusters that do not run Cilium networking |
| Licensing | Apache-2.0; CNCF graduated on February 29, 2024 | Apache-2.0 |
| Latest supplied release | Falco 0.45.1, published October 19, 2026 | Tetragon 1.7.1, published August 25, 2026 |
Licensing and editions: both projects are Apache-2.0
Falco and Tetragon are both published under Apache-2.0 in their GitHub repositories, so the license does not separate them on cost. The code, the rule and policy files you write, and the packaged releases can be used, modified and redistributed under the same permissive terms. Your legal team should still confirm what Apache-2.0 means for your internal redistribution, container images and any product you build on top of either tool.
- Falco: Licensed under Apache-2.0 according to the falcosecurity/falco repository, with version 0.45.1 published on October 19, 2026.
- Tetragon: Licensed under Apache-2.0 according to the cilium/tetragon repository, with version 1.7.1 published on August 25, 2026.
- Falco governance: Falco became a CNCF graduated project on February 29, 2024, which describes project maturity, not a commercial support agreement.
- Shared cost position: Neither license introduces a per-node or per-event fee, so differences in budget come from staff time, cluster capacity and any support you purchase.
Confirm the exact license file and release tag for the version you plan to deploy before you record the decision.
The supplied sources do not establish whether either project has a paid edition, a commercial distribution or an enterprise feature set. That gap matters for procurement because a team could assume an open project has no paid tier and miss a commercial option, or assume a paid tier exists and budget for it without need. Treat both as open questions until the maintainers, the CNCF project or a commercial vendor confirms the answer in writing.
- Falco edition status: The Falco documentation describes the project as open source, but the supplied material does not list a paid Falco edition or its contents.
- Tetragon edition status: The supplied material does not describe a paid Tetragon edition or enterprise tier.
- Third-party offerings: Ask whether any vendor sells Falco or Tetragon support, hardening or managed operation, and what that vendor's license terms cover.
- Feature parity: If a paid tier appears later, confirm which detections, policies, integrations and support windows it adds.
Ask each vendor for a written statement of editions, feature lists and support terms, and keep that statement with the procurement record.
Open-source licensing says nothing about who answers your production incident. Under both projects, you choose between community channels, a commercial vendor agreement, or an implementation partner who covers operations. DeployOpen plans, implements, integrates, hardens, upgrades and supports self-hosted open-source platforms inside your own infrastructure, and most engagements are fixed-scope with scope and deliverables agreed in writing before work starts. Ongoing support, upgrades and continuous testing are available as retainers.
- Community support: Costs no license fee but depends on your team's capacity to triage issues, read release notes and test upgrades.
- Vendor support: Ask whether a commercial provider offers response targets, named contacts and patch commitments, and get those commitments in the contract.
- Implementation partner: A fixed-scope engagement covers the work agreed in writing, such as rule and policy design, Kubernetes integration and alert routing.
- Retainer: Ongoing support, upgrades and continuous testing run under a separate agreement, so define the review cadence and escalation path up front.
Ask any provider, including DeployOpen, for the written scope, deliverables and service terms before you assume a support level.
Total cost of ownership for either tool is mostly people and infrastructure. One-time work includes design, initial deployment, integration with your alerting pipeline and the first set of rules or policies. Recurring work includes upgrades, rule and policy tuning, reviewing alerts, testing new kernel and Kubernetes versions, and keeping event destinations running. Cluster capacity and the systems that receive events also add cost, and those costs depend on your event volume and destination choices.
- One-time costs: Architecture, deployment, rule or policy authoring and the first round of testing on your nodes.
- Recurring costs: Upgrades, alert review, policy maintenance and support retainers if you buy them.
- Cost drivers: Event volume, the number of nodes, the number of rules or policies and the destinations that receive alerts.
- Quote inputs: Ask for the scope, the environments covered, the Kubernetes distributions involved, the upgrade cadence and the handover format.
When comparing quotes, confirm whether each one includes testing on your node operating systems and kernels, since that work often drives the largest share of effort.
Moving from Falco rules to Tetragon policies, or running both
This sequence shows a team which Falco detections carry over to Tetragon, which must be rewritten, and how alert routing stays intact during the change. The output is a tested, reviewed set of TracingPolicy objects and a clear decision on where each alert lands.
Inventory Falco rules and alert destinations
The platform or security engineer who owns Falco exports the active rules files, the custom rules, the enabled plugins and the output channels, including Falcosidekick and any SIEM forwarder. Each rule is recorded with its purpose, the workloads it covers and the team that receives its alerts. The step is done when every rule has an owner and every alert destination is listed in one shared inventory.
Sort detections by intent and enforcement need
The same engineer groups the inventory into three buckets: detections that only need visibility, detections where a block would be useful, and detections that depend on Falco plugin sources such as Kubernetes audit logs, CloudTrail or Okta. Tetragon's enforcement applies to kernel events on Kubernetes workloads, so plugin-based detections usually stay with Falco or move to the SIEM. The step is done when each rule sits in one bucket with a short reason written next to it.
Map candidate detections to TracingPolicy objects
A platform engineer with kernel and container knowledge translates each kernel-level candidate into a TracingPolicy that hooks the relevant kprobes, tracepoints or uprobes. The Tetragon documentation warns that this work requires Linux kernel and container knowledge to avoid problems such as TOCTOU bugs, so the security team reviews each mapping alongside the engineer. Rule logic does not carry over, so every policy is written again and marked with the Falco rule it replaces. The step is done when each candidate has a reviewed policy draft or a recorded decision to keep it in Falco.
Test enforcement in a non-production cluster
The platform team loads the draft policies into a non-production Kubernetes cluster, either as TracingPolicy resources or with the tetra CLI, and replays the workload behavior each detection targets. The team checks that observation-only policies fire on the expected events and that any enforcement action affects only the intended processes. Overhead is measured on representative nodes under the same load as production, since the Tetragon documentation's reduced-overhead claim should be confirmed in your environment. The step is done when test results, including false positives and any unintended blocks, are documented and approved.
Decide which alerts stay in Falcosidekick or the SIEM
The security lead and the SIEM owner decide, per detection, whether the alert remains in Falco, moves to Tetragon output, or is consumed by the SIEM from either source. Falcosidekick routing and SIEM ingestion rules are updated to match, and any plugin-based detections keep their existing path. Where Wazuh or another SIEM reads Falco JSON output from a file, that path is confirmed in writing. The step is done when every alert destination has one named source and one named receiving team.
Run both tools in parallel before cutover
During an agreed observation window, Falco and Tetragon run side by side on the non-production cluster, and the team compares alerts for the same activity. Duplicate alerts are tracked, and gaps where one tool misses an event are investigated before any change reaches production. The window length should be set in the written scope rather than by a fixed rule. The step is done when the comparison shows each retained detection firing as expected and each gap has a documented cause.
Retire duplicate alerts and document the result
Once the parallel run is accepted, the team removes the retired Falco rules or disables their outputs, keeps the rule files under version control for rollback, and updates runbooks to name the new source for each alert. Duplicate paths are closed so each event produces one alert in the SIEM. The step is done when the inventory, policy set, routing configuration and runbooks match, and the change record is signed off by the owners.
What drives the running cost of each runtime security tool
Falco and Tetragon cost less in licensing than in engineering time: both are Apache-2.0, so the running cost comes from the nodes they monitor, the people who write and maintain their policies, and the systems that receive their events. The cards below cover what to measure, what skills to staff, and what to request in a written quote. No prices are supplied for either tool or for DeployOpen's work, so every figure must come from your own tests and vendor or partner quotes.
Measure overhead on your nodes
Overhead depends on the event rate of your workloads, the number of rules or policies loaded, and the kernel driver in use. Falco's default modern eBPF probe is one variable, and Tetragon's documentation says in-kernel filtering reduces overhead for high-frequency events, but neither claim replaces a measurement. Run both tools on a staging cluster that mirrors production syscall volume, capture CPU and memory per node under peak load, and record how many events each tool emits. Ask any partner to include this test in the written scope.
Kernel and Cilium expertise
Tetragon's documentation warns that writing tracing policies requires knowledge of the Linux kernel and containers to avoid issues such as TOCTOU bugs. Tetragon is also Kubernetes-aware and is commonly deployed alongside Cilium, so teams without Cilium already in place take on a network stack change as well as a security tool. Falco needs kernel and driver knowledge to choose and troubleshoot its probe, but its rule language is easier to adopt. Budget for a senior kernel-literate engineer for Tetragon, and confirm which skills a quote assumes.
Rule and policy maintenance
Falco rules are written in its rule language and tuned against your baseline to limit false positives. Tetragon TracingPolicy resources hook kernel functions and can act on matches, so a faulty policy can block legitimate processes. Both need version control, peer review and a test suite that replays known-good and known-bad events. Each Falco or Tetragon upgrade can change field names or default rules, so plan recurring review time for every release. Ask who owns policy changes after handover.
Alert pipeline and SIEM costs
Falco delivers alerts to standard output, files, syslog, programs, HTTP(S) endpoints and, through Falcosidekick, many downstream channels. Each route adds storage, indexing and analyst time. Wazuh publishes a pattern in which Falco writes JSON to a file that a Wazuh agent reads, which means the SIEM license or hosting cost scales with event volume. Tetragon's enforcement decisions also produce events that must be stored for audit. Estimate daily event volume from your load test, then price the receiving platform against that figure.
Support options and upgrade effort
Falco is a CNCF graduated project, which matters for governance, but community support does not guarantee response times. Tetragon is maintained within the Cilium project, so its upgrades can be coupled to Cilium releases on your cluster. Neither tool is supplied with a vendor SLA in this comparison. Ask whether support is community only or commercial, what the upgrade path is for each component, and whether DeployOpen's retainers would cover testing of new releases in your environment. Retainers and fixed-scope work are quoted from written scope, not from a price list.
Quote items to request
A written quote should separate one-time work from recurring work. One-time items include architecture design, policy and rule authoring, load testing and cluster integration. Recurring items include monitoring of alert volume, policy review after each release, upgrade testing and on-call response. Ask for the assumed node count, event rate, kernel versions, Cilium version, SIEM destination and the hours allocated to each role. A quote that does not state these assumptions cannot be compared fairly with another.
Where Falco and Tetragon implementations usually run long
Falco projects tend to run long in two places: choosing and validating the kernel driver, and tuning rules until the alert stream is worth reading. Falco documents two drivers, the modern eBPF probe, which is used by default, and a kernel module. Before any rule work starts, the client team should confirm which driver its nodes can load, since managed Kubernetes node pools often restrict kernel modules and the choice then narrows. Test the chosen driver on each node pool, including the kernel versions your operating system images actually run, rather than assuming one lab result carries over. Noisy default rules are the second drain. Every rule that fires on expected admin activity, backup jobs or build agents costs triage time, so plan a tuning cycle with the security operations owners who will receive the alerts, and agree on who approves rule changes. Tetragon projects run long for different reasons because the policy itself acts in the kernel. A TracingPolicy that hooks the wrong function, or filters on arguments incorrectly, can either miss activity or block legitimate workloads, so every policy needs testing in a non-production cluster before enforcement is enabled. The Tetragon documentation warns that writing tracing policies requires knowledge of the Linux kernel and containers to avoid issues such as TOCTOU bugs, where the value checked differs from the value the kernel later uses. Budget for kernel-literate review of each policy, and ask the vendor or DeployOpen to show how the team will verify that a blocking action fires only on the intended condition. Cilium integration is the third source of delay. Tetragon is tied to a Cilium-based cluster deployment, so teams not already running Cilium should treat that as a platform change, not a sidecar addition. Confirm the Cilium version, the Helm values and the namespace and pod identity labels the policies will depend on before writing any policy. Alert routing applies to both tools: decide where Falco events go through Falcosidekick or the Wazuh file integration, and where Tetragon events are stored and reviewed. A written scope should name the people who own rule tuning, policy approval and on-call response, because unclear ownership is where these engagements most often stall.
Questions buyers ask before choosing Falco or Tetragon
Can Falco block activity or only alert on it?
Falco detects and alerts. The Falco documentation describes alerts forwarded to other components to take action and does not describe Falco itself blocking activity, so if you need a process stopped before it completes a system call, Falco alone will not do it. Falco fits when your response is a ticket, a page, a SIEM ingest or an automated workflow triggered by an alert. Tetragon can filter, block and react in the kernel, which suits enforcement requirements. Confirm the exact response path you need in a proof of concept, because the answer depends on whether your playbooks can tolerate detection latency.
Does Tetragon require Cilium to run?
Tetragon is licensed under Apache-2.0 and is Kubernetes-aware, but this comparison does not establish that it must run on Cilium. Check the current Tetragon installation documentation and your cluster networking before you commit, because your CNI choice may decide how Tetragon is deployed and supported. If your cluster already runs Cilium, the integration path is the obvious starting point. If it does not, changing the CNI is a separate project with its own migration effort and risk, and it should be scoped separately from the runtime security decision.
How do we measure overhead before choosing either tool?
Measure on your own nodes with your own workloads. The Tetragon documentation says in-kernel filtering reduces overhead for high-frequency events compared with a user-space agent, but it does not give a general figure, and overhead depends on event rate, number and complexity of rules or policies, kernel version and node size. Run each candidate under a representative load, record CPU and memory on the nodes, and measure event volume reaching your alerting pipeline. For Falco, also test which driver your nodes support, since the modern eBPF probe is the default. Ask your team to repeat the test at peak traffic, not only at idle.
What drives the cost of running Falco or Tetragon?
Both projects are open source, so license fees are not the main cost. The recurring costs are engineering time to write and tune rules or TracingPolicy resources, compute on every node the agent runs on, storage and processing for the events you forward, and maintenance through upgrades. Tetragon policy writing requires knowledge of the Linux kernel and containers, which tends to raise staffing cost. Falco's rule authoring is usually less demanding, but alert volume you forward to a SIEM can drive ingest cost. Ask any partner or internal team for a written scope that separates one-time build work from ongoing tuning and upgrade time.
Is it hard to migrate from Falco to Tetragon, or the reverse?
The effort is driven by rule and policy count, not by the switch itself. Falco rules and Tetragon TracingPolicy resources use different models, so existing detections must be rewritten and retested, not copied across. Expect parallel running so your alert baselines can be compared before cutover. Tetragon's kernel-level policies carry more risk during testing because a blocking policy that misfires can disrupt workloads. Plan the migration as a separate scoped project, with a written inventory of current detections, a test environment that matches production kernels, and a rollback path for each policy.
Can our own team run Falco or Tetragon without outside help?
A team with Linux, Kubernetes and security engineering skills can run either tool, but the depth of skill needed differs. Falco is a reasonable starting point for teams that mainly need detection and alert routing. Tetragon needs stronger kernel and container knowledge to write safe policies, and any enforcement policy needs careful testing. Be honest about bench strength: a single engineer maintaining detections alongside other duties often becomes the bottleneck. Outside help is commonly used for initial design, testing, documentation and evidence for audits, while your team keeps day-to-day ownership.
What does DeployOpen do for a Falco or Tetragon deployment?
DeployOpen plans, implements, customizes, integrates, hardens, documents and supports self-hosted open-source platforms inside your own infrastructure, and runs security testing that can cover the runtime tooling and the workloads it watches. Most engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts. DeployOpen does not publish a price list, so a quote follows from the written scope. Ongoing support, upgrades and continuous testing are available as retainers. DeployOpen prepares systems and evidence for compliance assessments but does not issue audit reports or certifications, which independent auditors and certification bodies provide.
How DeployOpen helps you choose and implement runtime security
Choosing between Falco and Tetragon is a decision about detection and enforcement, and it is also a decision about the kernel, the cluster and the people who will write policy. DeployOpen plans, implements, hardens and supports self-hosted open-source platforms inside your own infrastructure, and it runs security testing against the result. The cards below show where that work starts and what it covers for either project.
Scoping the detection or enforcement decision
Start by deciding whether a suspicious kernel event should only raise an alert for an analyst or stop the process before the system call completes. Falco is built around detection and alerting, while Tetragon can filter, block and react in the kernel. Write down which workloads need prevention, which need visibility, and which change owner signs off on enforcement. DeployOpen can turn that decision into a written scope with deliverables and timelines agreed before work begins.
Cluster and platform fit
Tetragon is Kubernetes-aware and works with namespaces and pods, and the documentation ties its approach to eBPF. Falco also runs on hosts, containers and Kubernetes, and it can read sources beyond system calls through plugins. Confirm which clusters, node operating systems and cloud accounts are in scope, and whether Cilium is already part of the network stack. DeployOpen plans the rollout inside your own cloud accounts, data centers or air-gapped networks.
Policy authoring and kernel expertise
Tetragon policies are written as TracingPolicy custom resources that attach kprobes, tracepoints and uprobes, and the documentation warns that writing them requires knowledge of the Linux kernel and containers. Falco rules are easier for SOC analysts to read but still need tuning to reduce noise. Decide who will own each policy set and how changes are tested before they reach production. DeployOpen can customize and document policy sets so your team can maintain them after handover.
Alert routing and response integration
Falco sends alerts to standard output, files, syslog, spawned programs or HTTP(S) endpoints, and Falcosidekick forwards events to many channels. Tetragon's enforcement reduces the need for a separate response step on some events but still produces events for review. Map where each alert lands, who triages it and which actions are automated. DeployOpen integrates these paths with your existing SIEM or case tooling and tests them end to end.
Hardening, testing and ongoing support
Runtime security tools are themselves privileged components that run in the kernel, so their own configuration, access and upgrade path need the same scrutiny as the workloads they protect. Plan for regular upgrades, measured overhead on your nodes, and independent testing of detections and enforcement. DeployOpen provides hardening, security testing and support as fixed-scope work or retainers, and it recommends whichever project fits the agreed scope. DeployOpen is not a vendor partner or reseller for Falco or Tetragon.
