What Straiker's verdict decides.
Two products can both render a verdict at the moment an agent acts and still be answering different questions. Straiker asks whether the interaction in front of it is an attack. That is a genuinely hard question, they answer it well, and it is not the same question as whether this agent was allowed to do this on behalf of that person.
Three modules over one detection engine
The product is organised as three modules mapped to the agent lifecycle, all served by the same detection engine. They are worth scoring separately, because a buyer evaluating runtime governance is often shown the red teaming.
- Discovera continuous inventory of agents, the MCP servers, tools and skills they connect to, and what each one can reach. It runs posture assessment over that inventory — over-permissioned agents, risky integrations, misconfigurations — and covers builder platforms and coding agents alike, including agents nobody registered.
- Ascendautomated adversarial testing. Purpose-built offensive models ingest the app's context, run reconnaissance across MCP servers, tools and infrastructure, then execute live attacks, with hooks into the build pipeline and findings mapped to the public control frameworks.
- Defendruntime protection. It inspects every prompt, reasoning step and tool call, maintains state across a whole conversation, and blocks injection, exfiltration, tool manipulation and agent hijacking inline. A separate control takes a compromised agent fully offline: revoke its tools, freeze its memory, suspend the session, or halt a fleet.
Where Straiker is strongest
Three of the sixteen rows below go to Straiker and four more are level — the most either count has run on a comparison we publish. They follow from a real difference in where each product starts: theirs is the attack, ours is the action.
- Adversarial testing before an agent shipsthis row goes to them outright and it is not close. Continuous automated red teaming against the agent's own tools, MCP servers and workflows, wired into the build pipeline so a prompt change or a new configuration triggers a fresh assessment. We govern agents once they are running; we do not attack them beforehand, and the table scores us accordingly.
- Detecting the attack inside the contentdirect and indirect prompt injection, across text, code, images, audio and uploaded files, on detection models trained for the job rather than general-purpose judges. We govern the action a compromised agent attempts rather than inspecting the prompt that induced it. Both stop the same incident; only one of them reads the payload, and that row is theirs.
- Where you can buy itboth modules are transactable through a major cloud marketplace against committed spend, and they hold a named partner designation with a frontier model provider. We are a focused platform rather than a marketplace presence, and that row is scored at a 2 for us on every comparison we publish.
- Level, not lostfour rows come out even, and they matter. Audit evidence is generated per decision and streamed to the SIEM. Agents built on anyone's stack are covered, because Straiker builds no agents. And regulated data leaving inside an agent action is caught semantically, across connectors — a genuine strength of theirs that stands beside ours rather than below it.
A trace records a user. It does not carry an owner.
Every integration mode collects the same raw material: traces. An interactive session's trace names the user who started it, and that is real attribution as far as it goes. It goes as far as the session.
An accountable owner is a different object. It is durable, it belongs to the agent rather than to a session, and it survives the case that matters most — the long-running agent that acts at three in the morning with nobody in front of it. A trace of that run has a model, a tool chain and a result. There is no user in it to name, because there was no user. The published data model records what an agent is connected to and what it can reach; it does not record who answers for it.
This is why every row in the identity group goes one way while the runtime group is close. Accountability is not a stricter grade of logging. It is a different thing to hold, and it has to be held before the action, not reconstructed after it.
Malicious and permitted are different questions
Consider an agent that reads a customer record and posts it to an internal channel. Nothing is injected, no exfiltration signature fires, the tool call is well formed and the content is unremarkable. A threat verdict returns clean, correctly. The question that has not been asked is whether the person this agent acts for is entitled to that record.
Straiker does surface over-permissioned agents — that is posture work in the discovery module, assessed against the inventory and reported to a security team. It is the right control in the wrong tense for this case: it tells you the permission is too broad, some time after the action already used it.
Agen evaluates entitlement in the moment, against the human behind the agent, with five verdict types rather than allow and deny — including step-up authentication and a human approval step. Those two rows in the table are not scored against a missing feature; they are scored against a model that has no principal to evaluate.
Where the enforcement point sits
Five integration modes are documented, and the differences between them do real work in the table. An SDK exports traces from first-party agents. An eBPF sensor captures cloud and Kubernetes workloads with no instrumentation. A gateway mode routes requests through for evaluation, positioned for simpler applications. Those three are inline.
Two are not. Proxy mode ingests proxy logs for third-party SaaS, and the browser mode is documented as lightweight monitoring that collects input and output from web agents. That mix is exactly why the moment-of-action row scores a 4 rather than a 5, and why endpoint and browser enforcement score where they do: the architecture explicitly ships no thick client, and the enforcement point is a sensor, a hook or a gateway rather than the device the work happens on.
Detection itself runs in their cloud. Traces and proxy logs are routed there for analysis, which is a sound design and a real constraint for a buyer with residency requirements. No self-hosted or in-region detection option is published, which is the whole of the deployment row.
What is not published
Seven of the rows we score were dropped rather than published, because no primary source supports them in either direction. Two of those are the pricing rows: there is no pricing page, no pricing unit and no tiers anywhere on the site, so the cost of governing a hundred agents cannot be modelled before a sales conversation. That is a normal enterprise motion and it is not a criticism — but it means a row that usually publishes on every comparison could not publish here.
One more row was dropped on fairness rather than evidence. Connector and tool breadth measures a broker, and Straiker brokers nothing — it observes the connectors an agent already has. Scoring that row would have measured our architecture rather than a capability theirs was built to have.
