Commercial vs Open Source AI Attack Detection Tools: A Buyer’s Guide

If you’re weighing open source against commercial tools for detecting attacks on your AI agents, you’re probably trying to answer a single question. Can we build this ourselves, or should we buy it?

It’s a fair question, and the existing content on it isn’t much help. Most comparisons line up tools side by side and tally features. That tells you which tool is better at one slice of the problem. It doesn’t tell you whether you have a working detection program.

Our perspective on this comes from an unusual position. We built Kubescape, the open-source Kubernetes security project that now runs across more than 100,000 organizations. Our commercial platform sits on top of that same foundation. The build-versus-buy line is not theoretical for us. We run both sides of it.

The more useful way to think about it is as a question of layers. AI agent detection runs as a stack. Telemetry sits at the bottom, then per-agent baselines, then correlation, then triage, then response. Open source genuinely covers some of those layers as well as anything you can buy. The cost shows up at the layers above that, and it shows up as engineering time, not licensing. There is a specific layer where most teams’ open-source plans quietly turn into a standing engineering program. Naming that layer is most of the decision.

By the end of this guide, you will have a way to walk your own detection stack from the bottom up. You will see exactly where your team’s capacity runs out. That point is your build-versus-buy line.

Open source vs commercial isn’t a tool choice — it’s a decision about which layers you staff

A feature matrix is the wrong tool for this decision. The instinct is to list candidates down one axis and capabilities across the other — but no single tool, open source or commercial, occupies more than a slice of what a working detection program needs. A comparison between two tools that each cover one layer of a multi-layer problem tells you which slice is better, not whether you have a program.

The more useful artifact is a line, not a matrix. Picture the detection stack vertically: runtime telemetry, per-agent baselines, cross-layer correlation, triage, response. Now draw a horizontal line across it. Below the line is what you build and operate yourself; above it is what you buy. Where that line falls — not which vendor sits above or below it — is the decision.

The reason this reframing matters is cost. The cost of the bottom layer and the cost of the top layer are not the same kind of cost, and they do not scale the same way. Treating them as a single “tool budget” is how teams end up surprised six months in. The rest of this guide walks the stack from the bottom up and fills in that line.

The telemetry layer is the commodity — and that’s the right place for open source

Start at the bottom, because the bottom layer is where open source is not just adequate but correct. Runtime telemetry — the kernel-level record of what every process actually did, enriched with container and Kubernetes context — is a commodity. eBPF-based tooling such as Falco and Tetragon collects it at low overhead and high fidelity, OpenTelemetry standardizes how it moves, and framework SDK callbacks expose the application-layer events that sit alongside it. This layer is mature, well-understood, and broadly interchangeable.

Our own history is the strongest evidence for treating telemetry as a commodity. We built and open-sourced Kubescape — now running across more than 100,000 organizations and 50,000-plus deployments — precisely because posture and scanning were always going to commoditize, and the right response to a commoditizing capability is to make it ubiquitous, transparent, and free. The commercial platform was built on top of that open-source foundation, not instead of it.

That history points at a pattern security teams tend to follow on their own. There is a pyramid of needs in security: you cover the basics first — posture, scanning, telemetry — and only then climb to the layer where attacks are actually detected and stopped. Open source is how most teams cover the base of that pyramid, and that is exactly what it should be used for. The mistake is not using open source at the telemetry layer. The mistake is assuming the telemetry layer is the whole pyramid.

Above telemetry, the build cost migrates from a license to a team

The moment you climb above telemetry, the nature of the cost changes. The license stays free; the work does not. Each layer above the sensor is something an open-source plan asks you to assemble, and assembly is engineering time.

The first layer up is per-agent baselines — a model of what normal looks like for each individual agent, so that deviation means something. This is where open source hits its hardest wall, and the reason is structural. Open-source runtime tools ship with rules written for deterministic workloads: a web server makes a predictable set of syscalls, so a rule that fires on the unexpected ones works well. An AI agent is non-deterministic by design. It generates code, calls tools in orders no one scripted, and varies its behavior with every prompt. A generic rule tuned for stable workloads either floods the queue with false positives or gets loosened until it catches nothing. Producing a baseline that tolerates legitimate variation while still flagging an attack is not a configuration task — it is a data-engineering project you now own, per agent, and it is the single most underestimated line item in a build decision. This is why a “normal” for a non-deterministic agent behaves so differently from a “normal” for a container, a distinction we’ve examined in our work on why AI-specific detection differs from container security.

The next layer up is correlation — connecting a signal on one layer to a signal on another so that five disconnected alerts become one attack story. eBPF tooling sees the kernel events; it does not, on its own, assemble them into a causal chain, which is where kernel-level visibility reaches its ceiling. Building correlation in-house means standing up the join logic, the timeline reconstruction, and the entity resolution that ties a prompt to a tool call to a credential read to an egress — and keeping all of it working as agents change.

Above correlation sit triage and response: deciding whether an assembled chain is an analyst’s problem or an automated containment, and acting on it. In an open-source program these are not features you toggle; they are workflows you design, wire into your SIEM and on-call rotation, and maintain. None of this is impossible. All of it is headcount. The build-versus-buy line is really the line between the layers your team can engineer and the layers it cannot.

And it never stops — “normal” keeps moving for non-deterministic agents

The upper layers, once built, are not done. They run on a current picture of “normal” — and that picture does not hold still. This is the run cost, and it is almost never in the original estimate.

Agent behavior drifts for legitimate reasons. A model gets updated. A prompt template changes. A new tool is added to the agent’s repertoire. Each of those shifts what normal looks like, and a baseline that does not move with it starts generating noise or, worse, goes quiet on real deviation. Maintaining baselines for non-deterministic agents is therefore continuous: every model update is a re-baselining event, every drift signal has to be classified as expected evolution or genuine compromise, and the rules above the baseline need re-tuning as the agent’s repertoire grows. We’ve written separately about distinguishing legitimate evolution from compromise in exactly this setting.

Securing an agent differs from securing a container in one compressed sentence: a container’s normal is mostly fixed; an agent’s is a moving target by design. An open-source program inherits that maintenance burden as standing engineering load — someone owns baseline upkeep, someone is on call when the rules misfire, and that someone is on your payroll.

Buying changes the shape of this cost, not just its size. A platform that keeps a per-deployment behavioral baseline current absorbs the re-baselining and drift-classification work an open-source program leaves on your team’s desk. The capability is the same either way — a current model of each agent’s behavior. The question is whose engineers keep it current.

Your build-vs-buy line falls at the first layer you can’t staff

Now the line can be drawn, and it is drawn by your team’s capacity, not by the tools’ brochures. Walk the stack from the bottom and ask, at each layer, a single question: can we build this and keep it running ourselves? The first layer where the honest answer is no is where your build-versus-buy line falls.

Read the stack as a cost table. At the telemetry layer the license cost is zero and the engineering cost is low — deploy a sensor, ship the data. This layer is almost always below the line; build it on open source. At the baseline layer the license cost is still zero but the engineering cost jumps, because you are now running a per-agent data project with continuous upkeep. At the correlation layer the engineering cost jumps again — join logic and timeline reconstruction that has to survive constant change. At triage and response the cost is organizational as much as technical. The pattern is consistent: as you climb, the dollar figure on the license stays flat at zero while the headcount figure rises, and somewhere on that climb the headcount figure exceeds what your team can carry. That crossing point is the line.

What you’re buying isn’t a sensor. The bottom layer is a commodity you can get for free; paying for it buys little. What a commercial platform sells is the program above the commodity — baselines kept current, alerts assembled into one attack story instead of five, and the triage that rides on top.

So the operative diagnostic is a question you can ask in a planning meeting: at which layer does our open-source plan stop being a tool we configure and start being a program we run? Below that layer, open source is the right answer and you should use it without apology. At and above it, you are choosing between hiring to build the program and buying it — and naming the layer is what turns that into a decision instead of a surprise.

Who should build, who should buy

The line falls in different places for different teams, and three patterns cover most of them.

Build is the right call for teams with genuine detection-engineering capacity and a small, stable set of agents. If you employ engineers who already write and maintain detection content, and you are running a handful of agents whose behavior does not change weekly, the upper layers are within reach — and open source all the way up is not a compromise, it is a sound use of a capable team. The cost is real but bounded, and you keep full control of the stack.

Buy is the right call when agent count is scaling faster than security headcount. The build cost is roughly fixed, but the maintenance cost scales with the number of agents whose baselines must stay current. A team going from five agents to fifty without going from five engineers to fifty will cross its build-versus-buy line whether it planned to or not; buying the upper layers is what keeps detection coverage from degrading as the fleet grows. Buying has its own price — a recurring subscription and less direct control of the stack — but for a team whose agent count is outrunning its hiring plan, that is the cheaper trade than a detection program that quietly stops keeping up.

The third pattern is the common one, and it is a blend rather than a binary. Run open source at the telemetry layer — the commodity, where it belongs — and put a commercial correlation and baseline layer on top of it. This keeps the free, interchangeable part free and interchangeable, and buys only the layers that are genuinely hard to build and maintain. Most teams that think they are choosing between open source and commercial are actually choosing where, on the stack, to switch from one to the other. It is the architecture we run ourselves — Kubescape at the foundation, with the correlation and baseline layers built on top of it.

The decision is staffing, not licensing

The commercial-versus-open-source question for AI agent attack detection is not really about software licenses. It is about staffing — about which layers of the detection stack your team can build and keep running, and which layers it makes more sense to buy as finished work. Open source is the right tool at the telemetry layer for almost everyone, and a sound choice further up for teams with the engineering depth to maintain what they assemble. The cost it carries is not on the invoice; it is on the org chart.

The practical next step is to draw your own line. Walk your detection stack from telemetry upward, and at each layer ask whether your team can build it and keep it current as your agents change. The first layer where the answer is no is the layer to buy. That answer lives on your org chart, not in a feature comparison.

If you want to see what the upper layers look like when someone else runs them, that is the shape of ARMO’s platform for cloud-native security for AI workloads — telemetry through response, with the baselines and correlation maintained for you.

FAQ

Can I build AI agent attack detection entirely on open source? Through the telemetry layer, yes, and well — Falco, Tetragon, and OpenTelemetry give you high-fidelity runtime signal at low overhead. The build begins above telemetry: the per-agent baselines, cross-layer correlation, and triage that turn that signal into an actionable detection are not things you configure, they are things you engineer and maintain. A fully open-source program is achievable, but it is a detection-engineering project, not an install.

What does Falco or Tetragon actually cover for AI agents? Both instrument at the kernel and process layer, so they see syscalls, process trees, file access, and network activity with strong fidelity. What they do not see on their own is the application-layer context — the prompt that triggered an action, the tool-call sequence, the agent’s decision path — or the correlation that ties those events into a single chain. They are an excellent telemetry foundation and a partial answer to one or two layers of the stack, not a full detection program.

How many engineers does an open-source detection program take to run? There is no fixed number, because the cost scales with agent count, not with the install. The headline cost is not the initial deployment, which is fast — it is the standing maintenance: keeping per-agent baselines current as models and prompts change, re-tuning rules as agents gain new tools, and being on call when correlation logic misfires. Budget for ongoing detection-engineering capacity rather than a one-time integration, because the run cost is where open-source detection programs are usually under-resourced.

When does commercial tooling become worth the cost? The clearest trigger is agent count scaling faster than security headcount. Build cost is roughly fixed, but baseline-maintenance cost rises with the number of agents you run, so a growing fleet crosses the build-versus-buy line on the maintenance side first. When keeping baselines and correlation current would require hiring you are not going to do, buying those upper layers as a maintained service is what holds coverage steady.

Can I combine open source and commercial tools? Yes, and it is the most common real-world configuration. Run open-source telemetry at the bottom of the stack — it is a commodity, and interchangeable — and put a commercial correlation and baseline layer on top of it. This keeps the free part free while buying only the layers that are expensive to build and maintain, which for most teams is the economically rational place to draw the line.