Tetragon has moved from interesting demo to production-ready runtime security tool over the last eighteen months, and the 1.4 release in February 2026 cleaned up most of the remaining rough edges around policy authoring. Teams that previously stitched together Falco, auditd, and a handful of homegrown eBPF probes are consolidating on Tetragon because it covers process, network, and file events with a single agent and a coherent policy model. The catch is that getting it right requires more than a Helm install.
This post walks through what we learned deploying Tetragon across a 4,200-node Kubernetes fleet, including the calls we got wrong on the first pass and the patterns that scaled. The emphasis is operational rather than introductory, so we assume you already know what eBPF is and why kernel-level observability matters.
How does Tetragon compare to Falco in practice?
Falco and Tetragon overlap but are not interchangeable. Falco's rule engine is more mature for high-level detections and integrates cleanly with existing SIEM pipelines through the Falcosidekick layer. Tetragon's strength is granular kernel hooks and the ability to enforce, not just detect, by killing offending processes or blocking syscalls inline. We run both: Falco for the broad detection ruleset where its community library saves effort, and Tetragon for the targeted enforcement policies where we need kernel-level certainty and sub-millisecond reaction times.
The performance profile differs as well. Tetragon's overhead on our nodes measured at 0.7% CPU at steady state with about 40 active TracingPolicies, compared to 1.4% for Falco doing comparable work. The difference is largest on nodes with high process churn, where Falco's libbpf-based collector and ring buffer drain show up more clearly. Neither is expensive in absolute terms, but if you are running both on the same nodes you should budget for the combined overhead and watch for ring buffer drops during traffic spikes.
What does a useful TracingPolicy look like?
A TracingPolicy is a YAML document that selects kernel functions to hook and defines what to do when those hooks fire. The most useful early policies for us were ones that caught the classics: unexpected execve calls from container processes, writes to sensitive paths like /etc/shadow or /root/.ssh, and outbound network connections to known C2 IP ranges. Each policy averaged about 60 lines of YAML once we factored in selectors, filters, and the action specifications.
The mistake we made initially was writing policies that were too broad. A policy that fires on every execve generates a flood of events even at modest cluster sizes, and the noise drowns out the signal. The fix was to layer selectors aggressively, filtering by container image label, namespace, and parent process. A well-scoped policy generates maybe a dozen events per day across the fleet, almost all of which are real signal. A poorly scoped policy generates millions and gets disabled within a week.
How do you handle policy distribution at scale?
Tetragon policies are Kubernetes custom resources, which means you can manage them with the same GitOps tooling you use for everything else. We use Argo CD with a dedicated repository for security policies, organized by detection category and severity. Each policy goes through a pull request review by the security team before merging, and the deployment uses canary rollout to a small subset of nodes before fleet-wide propagation. A bad policy can crash kernel hooks and degrade node performance, so the rollout discipline matters.
We also built a small validation pipeline that runs new policies against a synthetic event corpus before they reach production. The corpus is a few thousand recorded process and network events from staging clusters, and the validation checks that the new policy fires on the expected events and stays quiet on the rest. This caught about 30% of the policy bugs that would otherwise have shipped to production, and it is the single highest-leverage piece of tooling we built around Tetragon.
What about events egress and SIEM integration?
Tetragon emits events as JSON over a Unix socket or stdout, which is convenient but not directly useful for SIEM ingestion at scale. We run the Tetragon export sidecar to ship events to a Vector pipeline, which deduplicates, enriches with Kubernetes metadata, and forwards to our Elastic-based detection stack. The end-to-end latency from kernel event to SIEM alert is about 4 seconds on a healthy day, which is fast enough for most detection use cases.
The volume reality check: across 4,200 nodes with our current policy set, we ingest roughly 180 GB of Tetragon events per day after deduplication. That is manageable, but it would have been much larger without the aggressive filtering at the Vector layer. Plan for storage and ingestion costs up front, because the temptation to log everything and figure it out later is expensive.
Where does enforcement actually pay off?
Enforcement, as opposed to detection, is where Tetragon earns its keep relative to logging-only tools. Our highest-value enforcement policies are the ones that kill processes attempting to load kernel modules from inside containers, block writes to immutable file paths, and prevent outbound connections to non-allowlisted external IPs from production workloads. Each of these has caught real incidents in the past quarter, and the kernel-level enforcement means the malicious action is prevented rather than logged after the fact.
The risk is that an aggressive enforcement policy can break legitimate workloads. We require every enforcement policy to spend at least two weeks in detect-only mode in staging before flipping to enforce, and we require sign-off from the affected service team. This slows rollout but eliminates the political problem of security tools that block production traffic without warning.
How Safeguard Helps
Safeguard ingests Tetragon events alongside SBOMs and CVE data, so you can correlate runtime activity with the underlying package risk. Griffin AI surfaces the cases where a runtime event maps to a known CVE in the same image, prioritizing investigation. Reachability analysis runs against the loaded code paths, so a runtime alert on a vulnerable function is flagged as a high-confidence exploitation candidate rather than incidental noise. Policy gates can require Tetragon coverage on any image promoted to production, ensuring you do not lose runtime visibility on new workloads. Zero-CVE base images reduce the surface area Tetragon has to monitor in the first place, and TPRM data helps you assess whether upstream suppliers ship workloads that comply with your runtime policy baseline.