CTEM and exposure-management tools work the asset graph, identity graph, and CVE feeds your other tools already produce. That synthesis is useful, and it does not generate new data. Spektion adds runtime evidence those models have never had: execution state, privilege, network exposure, and blast radius. You feed your exposure program ground truth instead of assumptions, prioritize on what is exploitable here, and gain mitigation options when no patch exists.
Continuous Threat Exposure Management (CTEM) and exposure-management tools (such as Zafran and XM Cyber) make sense of data your other tools already produce: asset graphs, identity relationships, configurations, CVE feeds. That synthesis is useful, and it does not generate new data. Spektion runs a lightweight endpoint sensor that adds the data CTEM has never had, then uses it to deliver new data and new outcomes.
New data
First-party runtime observation of the endpoint: execution state, privilege level, network exposure, and blast radius. CTEM tools consume CVE and configuration data from your other tools. They do not observe runtime behavior. That gap is the input Spektion fills.
New outcomes
Visibility into exposures no asset graph contains (pre-CVE weaknesses, vulnerable components embedded inside vendor applications, stored secrets, ungoverned AI agents). Prioritization grounded in what is exploitable here, not what is theoretically reachable. Mitigation options (detective controls and OS configurations) when no patch is on the way.
CTEM computes what an attacker could reach. Spektion observes what is exploitable in reality. The modeled view on the left, runtime evidence on the right.
CTEM and attack-path tools answer a valuable question: if an attacker landed here, where could they go? They build a graph of assets, configurations, identities, and known vulnerabilities, then compute routes through it. The data they reason over comes from tools you already operate: CMDBs, scanners, identity providers, SIEMs. That synthesis is useful, and it is bounded by what those upstream tools produce. None of them observe what software actually does on the endpoint at execution time. Spektion does, and that runtime evidence is what changes which paths the model treats as real.
It does not know whether that component actually executes, the privilege it runs at in practice, or whether the behavior the exploit depends on ever occurs. The model is only as good as its assumptions about runtime, and it does not observe runtime.
The sensor watches how software behaves on the endpoint. That is why the two approaches fit together: the model gets sharper when its assumptions are replaced with evidence.
…you need to reason about identity and configuration exposure, or run a Gartner-style continuous exposure program at the architecture level. That modeling has real value.
…when you want to feed a CTEM program ground-truth runtime data, or when you need coverage of exposures that no asset graph contains. In many environments, the answer is both, with Spektion supplying the runtime layer.
The model gets sharper when its assumptions become evidence. Spektion's runtime evidence validates or prunes the modeled paths, replacing theoretical reachability with observed exploitability.
Spektion gives us uniquely valuable insights into software risk that were previously invisible to us. We can now take proactive action to address vulnerabilities before they become problems, significantly strengthening our security posture.
Spektion delivers Runtime Exposure Management, which feeds a CTEM program rather than replacing the framework. CTEM is a continuous process for assessing attack surface; Spektion provides the runtime behavioral evidence that process needs to prioritize on reality rather than on modeled assumptions.
Those tools model theoretical attack paths across an asset and configuration graph built from data your other tools already produce. Spektion generates new data by observing software behavior at runtime, and uses that data to determine what is exploitable in your environment. The approaches are often complementary, with Spektion supplying the ground-truth runtime input that makes the models more accurate.
Yes. In many accounts they complement each other. Spektion's runtime evidence validates or prunes the paths a CTEM tool models, replacing reachability assumptions with observed exploitability.
A condition Spektion has observed on the endpoint: software that is executing, at a given privilege, reachable over the network, with a measured post-exploitation blast radius. That is different from a path a model computes as theoretically reachable.
The sensor is lightweight: under 2% CPU overhead and no measurable performance impact. It observes passively, with no code injection or process modification, and runs on Windows, macOS, and Linux across endpoints and servers.
First runtime data lands within minutes of deploying the sensor. A typical proof-of-value runs three weeks across 50 to 500 endpoints: deploy in week one, review findings in week two, then quantify what the runtime evidence changes against your CTEM program in week three.
Bring a slice of your environment to a demo and see exploitability measured, not simulated, with a lightweight sensor that runs alongside your CTEM program.