ARMO vs Sysdig: Which Tool Actually Turns Detection Into Prevention

ARMO and Sysdig will both catch the container escape, the reverse shell, the privilege escalation in your cluster. They are two eBPF platforms built on CNCF foundations, Kubescape for ARMO and Falco for Sysdig, and on detection they are real peers.

So the question is not who spots the attack. It is what each tool does next. ARMO turns what it sees into prevention you can enforce: a generated seccomp profile, a network policy, a fix it has checked will not break the workload. Sysdig sees the same attack and explains it, but stops there. That is where ARMO wins.

ARMO vs Sysdig at a glance

The table reads ARMO across the board, but the honest version is more specific than that: Sysdig is a true peer on detection and broader on multi-cloud posture, and ARMO wins by turning detection into generated prevention. The three dimensions that decide are problem-solution fit, architecture fit, and operability. The rest resolve in a sentence.

Where ARMO and Sysdig actually diverge

Start with what the two share, because it is substantial. Both run on eBPF. Both are anchored in a CNCF project, Kubescape for ARMO and Falco for Sysdig. Both detect container escape, reverse shells, and privilege escalation at runtime, both build behavioral baselines, and both reference application-layer activity. Sysdig generates Kubernetes network policies the same way ARMO does. None of that is an ARMO differentiator.

The divergence is not in what each platform can see. It is in what each platform produces from what it sees. Sysdig assembles a strong picture across the infrastructure and explains it, increasingly with an AI analyst on top. ARMO reconstructs the same incident as a single correlated attack story that includes the application call stack and the API calls involved, the kind of evidence ARMO’s cloud application detection and response capability is built to produce. That application-layer visibility reaches into encrypted traffic: ARMO inspects TLS and SSL application traffic at L7 through eBPF uprobes, with no sidecar and no proxy. More importantly, the behavioral profile ARMO builds during observation, what ARMO calls Application Profile DNA (APD™), does not stay a detection artifact. It becomes the input to enforcement. APD™ learns each workload’s normal behavior and generates the least-privilege network policies and seccomp profiles from it. That is the split this comparison runs on, and it shows up in three places that matter to a buyer: prevention, remediation, and cost.

Can it turn what it observes into prevention?

Both platforms generate Kubernetes network policies from observed traffic, so that is shared ground, not an edge. The edge is one layer deeper.

ARMO generates seccomp profiles from observed behavior, not just network policies. After watching a workload run, the platform produces a syscall-level profile scoped to what that workload actually does, then deploys it in audit mode before graduating it to enforcement. Sysdig does not claim seccomp profile generation on its product pages. That gap matters because seccomp is where you shrink a workload’s actual kernel attack surface, and writing those profiles by hand is the work almost nobody does. For ARMO, observation is not the deliverable; the generated control is, an approach detailed in how ARMO’s behavioral baselines turn into Kubernetes hardening.

Prevention does not stop at runtime controls. It also runs at the gate: ARMO blocks non-compliant workloads at deploy time through native Kubernetes Validating Admission Policies, and is the creator of the open-source CEL Admission Library, a Kubescape project. The same open-source lineage behind detection also backs deploy-time enforcement, so a workload that violates policy can be stopped before it ever runs rather than only flagged once it does.

This is the practical version of the question a buyer should ask in a proof of concept: when I deploy in observe mode and let it watch a workload for a week, does the platform hand me the enforcement policy derived from what it saw, or does it hand me a dashboard and leave the policy authoring to me. The behavioral profile is the substrate, and the prevention falls out of it. That is the part of the architecture which limits Sysdig, and it is the cleanest reason the two are not interchangeable.

Will its recommended fix break my workload?

Sysdig has guided remediation, and its Sage analyst surfaces and prioritizes fixes. That is real. The ARMO distinction is narrower than “we remediate and they don’t,” and it holds.

ARMO does not just recommend a fix. It validates that the fix is safe to apply against the workload’s observed runtime behavior before you ship it. When the platform suggests tightening a network policy or removing a permission, it checks that change against what the workload actually does in production, so the recommendation comes with a verdict on whether it will break normal operation. That is the difference between guided remediation and remediation you can apply on a Friday afternoon. The fix is derived from behavior and tested against behavior.

For a buyer, the eval question is concrete: does the platform tell me which fix is safe to ship based on what the workload actually does, or does it tell me what is wrong and leave the blast-radius judgment to me. A guided fix still requires a human to reason about whether enforcing it will take down a service. A fix verified against observed behavior removes most of that reasoning, which is the entire point of running a behavioral platform. Sysdig’s pages describe guided and prioritized remediation. The safety check against observed behavior is the piece ARMO claims and Sysdig does not.

What does it cost my cluster at runtime?

Footprint is the dimension platform teams feel every day, and it is where ARMO’s architecture pays off. ARMO’s eBPF sensors run at roughly 1 to 2.5% CPU and 1% memory with no sidecars and no code changes, which keeps the platform usable on latency-sensitive workloads where a heavier agent gets rejected. Reviewers frequently flag a heavier agent and a steeper onboarding for Sysdig, a real operational cost even when detection quality is high.

On licensing, Sysdig has per-host pricing scales in a way you can forecast. ARMO’s advantage is low-friction entry: a self-service startup plan for teams running up to 25 worker nodes with community support, a free two-week trial to prove value before committing, an air-gapped deployment option for regulated environments, and a lean footprint that lowers the operational cost of running the platform rather than just the license cost of buying it.

Detection you can’t turn into prevention is half a platform

The honest summary of ARMO versus Sysdig is not that one tool sees what the other misses. These are two strong eBPF detectors built on CNCF foundations, and on a fair reading they detect the same attacks. The decision turns on what happens after detection. Sysdig gives you a strong picture and an analyst to interpret it.

ARMO gives you the picture and then generates the seccomp profile, writes the network policy, and verifies the fix against how the workload actually behaves. A runtime platform that detects but cannot turn detection into enforcement is doing half the job, and the half it skips is the half that changes your risk. You do not have to take that on faith. ARMO’s Cloud Threat Readiness Lab (CTRL) injects real attack behavior into your own cluster so you can watch the platform detect the attack and hand back the generated prevention before you commit. See ARMO close that loop on your own cluster with a short technical demo.

Frequently asked questions

How do I get the enforcement policy generated from what the tool observed?

Deploy ARMO in observe mode and let it build a behavioral baseline of the workload over a representative window, typically a week. The platform then generates a seccomp profile and Kubernetes network policy from that baseline. Deploy the generated controls in audit mode first, compare expected against actual, then graduate to enforcement once the baseline has caught the legitimate edge cases.

How do I verify a remediation will not take down production?

Require that the platform validates the proposed fix against the workload’s observed runtime behavior, not against static configuration. A fix tested only against declared config can still break a service that behaves differently in production. ARMO checks the change against what the workload actually does before recommending it, which is the safety signal you want before enforcing anything on a live system.

Can ARMO see inside encrypted traffic?

Yes. ARMO’s eBPF uprobes inspect TLS and SSL application traffic at L7 with no sidecar and no proxy, so you get application-layer visibility into encrypted calls without standing up a decryption tier. That is the same L7 signal that feeds the correlated attack story, which is why the application call stack shows up in the incident rather than just the network metadata.