ARMO vs Sweet Security: Why the Cluster Beats the Gateway for Runtime Security

The real question when you compare two runtime security platforms is not which one detects more. It is where each one enforces. That single architectural decision, whether security acts at a gateway in front of your workloads or inside the cluster on what those workloads actually do, cascades into every capability difference that follows.

Sweet Security is a credible peer. It brands itself a cloud application detection and response leader, ships a full runtime platform, and built its AI security around an AI gateway and an MCP gateway. ARMO takes a different position. It runs inside the cluster, watches the workload’s real behavior at the kernel, and generates the Kubernetes hardening that behavior implies.

For teams whose center of gravity is Kubernetes runtime, ARMO is the stronger choice. It enforces least privilege on the workload’s actual execution, generates the network and system-call policies a gateway cannot, and is built on an open-source project you can audit. Sweet covers the prompt stream well. The rest of this comparison is about why the cluster, not the gateway, is where runtime enforcement belongs.

Where does each platform enforce least privilege, at the gateway or in the cluster?

Enforcement has to sit somewhere. It can sit at a gateway, a checkpoint the agent’s traffic passes through on its way to a model or a tool, or it can sit inside the cluster, on the system calls and tool calls the workload actually issues. That placement is the decision that matters, because it determines what enforcement can see and what it can stop.

ARMO enforces inside the cluster. Its eBPF sensor runs in the Linux kernel and watches what each workload does: the files it opens, the processes it spawns, the network connections it makes, the system calls it issues. Enforcement acts on that real behavior. Because the restrictions live in kernel space, they sit outside the workload’s own control. An agent whose logic is hijacked by a malicious prompt can be steered into new behavior, but it cannot lift a kernel-level restriction on what it is allowed to do. This matters most for the AI agents now arriving in production clusters, where cloud-native security for AI workloads has to constrain what an agent does, not only what it is asked to do.

ARMO arrives at those restrictions through the observe-to-enforce model. You start in visibility mode and let the platform watch a workload operate for a defined period. It learns the workload’s normal behavior and builds a behavioral baseline ARMO calls Application Profile DNA (APD™), then proposes policies that match what it actually saw. You run those policies in audit mode first, where the platform logs what would have been blocked without blocking anything, so you can catch the once-a-quarter job or the month-end reconciliation that a guessed policy would have broken. Only once the audit logs are clean do you promote to active enforcement. The move from watching to blocking is a configuration change on the same sensor, not a second tool to deploy and keep in sync. None of this requires changing application code, and the sensor runs at between 1% and 2.5% CPU and around 1% memory, low enough to leave on in production.

Sweet enforces at the gateway, and it does that part well. Its AI gateway analyzes prompts and blocks malicious operations, and its MCP gateway applies operation-level enforcement on agent tool use. Sweet’s own framing is that intent happens inside the MCP, so that is where it places the control point. For prompt injection and for governing the operations that route through the gateway, this is real protection, and it is worth conceding plainly. If your primary need is to intercept the prompt stream, Sweet does it.

The limit is structural. A gateway governs what passes through it. The behavioral consequence of an attack executes in the cluster, on the workload: the file an agent reads, the process it launches, the egress it opens, whether or not any of it traversed the gateway. That is the layer ARMO enforces on. The two are not mutually exclusive, and a gateway in front of an agent and kernel-level enforcement on the agent answer different questions. But for a team buying Kubernetes runtime security, the decisive layer is the one where the workload actually runs.

Which platform writes your Kubernetes guardrails from what it observes?

Watching a workload is useful only if you can turn what you saw into something that constrains it. This is where the in-cluster position pays off in a way a gateway cannot match, because the same Application Profile DNA (APD™) that drives enforcement also becomes Kubernetes-native hardening you can apply directly.

Does it generate enforceable Kubernetes NetworkPolicies from observed runtime behavior, so least-privilege segmentation comes from evidence rather than hand-written YAML? ARMO does. It watches the connections a workload actually makes and produces NetworkPolicies scoped to exactly those connections. Sweet does not claim NetworkPolicy generation on its product pages. Generating network policy from observed behavior is uncommon across runtime tools, and generating seccomp profiles from observed system calls appears to be ARMO’s alone. From the calls a workload actually issues, ARMO produces a seccomp profile that blocks the hundreds of system calls it never makes, delivered as a Kubernetes custom resource. A gateway enforces the traffic that passes through it. It does not emit a NetworkPolicy or a seccomp profile, because it never observed behavior at the kernel, which is where those artifacts come from.

The effect of this is measurable, and it is the difference between a policy someone guessed and a policy the cluster proved. In a representative environment running 120 microservices across 8 namespaces, the observe-then-enforce loop took the average number of services each pod could reach from 87 down to 6.2, lifted the share of workloads under enforced network policies from 10% to 80%, and cut service accounts holding unused cluster-wide permissions from 28% of the fleet to 2.5%. Containers running without a seccomp profile dropped from 90% to 15%, generated from real system-call data rather than written by hand. The number that matters most is the last one: zero production incidents caused by an enforced policy in the first quarter, because every policy was validated in audit mode before it ever blocked anything.

This is the practical case for enforcing where the workload runs. Detection tells you something happened. Generated hardening changes what is allowed to happen next, and it does so from evidence the cluster supplied rather than from a security engineer’s best guess about what an unfamiliar agent should be permitted to do.

Which platform covers the full Kubernetes attack chain, not just slices of it?

Depth on Kubernetes and breadth across the attack chain are where a runtime platform either earns the word native or borrows it. ARMO ships 250+ Kubernetes-native controls and correlates detections across four layers: cloud events, Kubernetes API activity, the container and workload, and the application itself. The result is one connected attack story instead of a stack of separate alerts.

Both platforms tell a cross-stack attack story, and that is worth stating without hedging. Sweet correlates signals across workloads, identities, and applications, ships genuine application-layer detection, and uses patented correlation to assemble incidents. ARMO’s own application-layer detection has concrete footing: it inspects encrypted TLS/SSL traffic through eBPF uprobes, full L7 visibility with no sidecar and no proxy, so its application-level signals come from inside the cluster rather than from traffic routed through a gateway. On the raw runtime numbers, Sweet publishes a higher figure than ARMO, 99% reachability and noise reduction against ARMO’s 90%, and claims function-level reachability where ARMO is partial. If the contest were a single noise-reduction percentage, that point would go to Sweet. The more useful question is what the reachability feeds. ARMO’s function-level depth flows into the attack story and into the generated hardening, so reachability is not a standalone score but an input to enforcement.

Where the chain gets concrete is in least privilege inside the cluster. Can it audit over-permissioned Kubernetes service accounts and flag workloads holding cluster-admin they never use, the permissions that quietly widen blast radius? ARMO analyzes Kubernetes RBAC for over-privilege directly and scopes service accounts to the access they actually exercise. Sweet’s Kubernetes least-privilege analysis is partial and oriented to its identity threat detection. For a team whose blast-radius risk lives in RBAC sprawl and standing permissions, that distinction is the difference between seeing the problem and being able to close it.

Which foundation can you actually audit?

Every security claim a vendor makes rests on a foundation you either can or cannot inspect. ARMO is built on Kubescape, the open-source project it created and contributed to the CNCF, used by more than 50,000 organizations and carrying over 11,000 GitHub stars.

That same open lineage extends to enforcement: ARMO is the creator of the open-source CEL Admission Library, a Kubescape project, and blocks non-compliant workloads at deploy time through native Kubernetes Validating Admission Policies, so a container running as root is stopped before it ever starts rather than caught after the fact. You can read the code, run it yourself, and see exactly how the checks work before you ever take a sales call.

Getting started is self-service: a startup plan covers clusters up to 25 worker nodes with community support, and a free two-week trial runs the full platform before you ever talk to sales. For environments that cannot reach the internet, there is an air-gapped option.

Sweet ships no open-source project, so its detection logic and its enforcement behavior are inspectable only to the degree its product surfaces them. For a security team that wants to verify a tool’s claims rather than take them on faith, an open foundation is a different kind of assurance than a funding round, and it is the kind that survives a code review.

Runtime enforcement belongs in the cluster, not in front of it

Sweet Security and ARMO both detect, both correlate, and both tell an attack story, which is exactly why the decision comes down to architecture rather than feature counts. A gateway in front of your agents catches what passes through it, and that is real and useful.

But the attack executes on the workload, in the cluster, on the system calls and the file access and the network connections an agent actually makes, and that is the layer where least privilege has to be enforced and where the hardening that prevents the next incident is generated.

If your stack runs on Kubernetes, that points to ARMO: in-cluster enforcement on real behavior, the NetworkPolicies and seccomp profiles that shrink your blast radius, and one connected attack story across cloud, Kubernetes, container, and application, all on an open foundation you can audit before you commit a dollar.

You do not have to take that on faith. ARMO’s Cloud Threat Readiness Lab (CTRL) injects real attack behaviors into your cluster so you can watch ARMO detect and respond before you commit. See it run against your own workloads: book a demo and watch ARMO build the live attack story and generate the hardening for your cluster, in minutes rather than a sales cycle.

Frequently asked questions

How do I test in-cluster enforcement without risking production? Deploy ARMO’s eBPF sensor and start in visibility mode, which observes workload behavior without enforcing anything. Let it build a behavioral profile over a defined window, then review the network and system-call policies it proposes. Run those in audit mode, where the platform logs what would have been blocked, and promote to active enforcement only once the audit log is clean. The whole path requires no application code changes.

How do I verify ARMO actually detects attacks? ARMO’s Cloud Threat Readiness Lab (CTRL) injects real attack behaviors into your own cluster so you can watch detection and response happen before you commit to anything. You see the platform surface the attack story and the recommended hardening against live workloads, not a canned demo environment. It turns the question of whether this actually works into something you confirm yourself.

Can ARMO see inside encrypted traffic? Yes. ARMO inspects encrypted TLS/SSL application traffic at L7 through eBPF uprobes, with no sidecar and no proxy in the request path. That means application-layer detection comes from inside the cluster on the workload’s real traffic, rather than depending on traffic being routed through a gateway first.