Evaluating Vectra, Darktrace or ExtraHop? Compare the architecture first.
NDR products differ in features, but many share one design: sensors in your network, a model tuned to each environment, and analytics that often run in the vendor's cloud. LogLM inverts that design. It is a foundation model pretrained across diverse security telemetry, so your team detects concerning sequences zero-shot in the logs and network data you already collect, inside your own boundary.
Pretrained across diverse environments and telemetry types. 99% zero-shot, with no learning period in yours.
Runs on infrastructure you already operate: your data lake, your Cribl pipeline, your Kubernetes clusters.
Weights, verdicts and learning stay inside your environment: on premises, air-gapped or in your own cloud.
Conventional NDR and LogLM differ in where the intelligence comes from.
The left column describes common NDR architectures as vendors publicly present them. Individual products vary; confirm current capabilities with each vendor.
Measured in production, not in a lab.
Security teams at BNY, Deutsche Telekom, a top-four global bank and the Technology Advancement Center evaluated LogLM on their own telemetry.
Without training on the customer's environment.
Analysts spend their time on incidents, not noise.
Measured at a top-four global bank.
Measured at BNY.
Questions to ask any NDR vendor.
The answers expose the architecture you will live with long after the evaluation ends: where analysis runs, what leaves your environment, and how long before your team can trust a detection.
Vectra publicly describes attack signal intelligence across network, identity and cloud. Ask where the analysis runs, and what telemetry leaves your environment to reach it.
Darktrace publicly describes Self-Learning AI that learns each organization's normal patterns. Ask how long before detections are trusted, and what happens when your environment changes.
ExtraHop publicly describes wire data analysis from network sensors. Ask which physical or virtual sensors are required, and what coverage extends beyond the wire.
Corelight builds on Zeek network evidence. Ask who writes and maintains the detections that run on that evidence.
From historical logs to a proof of value inside your boundary.
Send telemetry
Share historical logs or flow data from one environment. No agents, no disruption.
Review findings
Receive detections mapped to MITRE ATT&CK, with the evidence behind each one.
Prove value in place
Run LogLM and Vigil inside your environment on live telemetry, beside your current detection layer.
See what your current detection layer misses.
Send historical logs or flow telemetry. The team runs LogLM against them and returns findings mapped to MITRE ATT&CK, with the evidence behind each one.