Security Operations
Crogl
Autonomously investigates every SOC alert without playbooks, entirely in your environment.
Crogl Overview
What it does
Crogl is an agentic AI platform for the security operations center (SOC) that autonomously investigates every alert an environment generates, from the routine to the unprecedented, without relying on predefined playbooks. Its core mechanism pairs a Knowledge Engine that continuously learns an organization's security processes with a Knowledge Graph holding live environmental context: entities, relationships, and behaviors. Investigations proceed through contextual reasoning over that graph rather than rule-based automation that stalls on anything outside its ruleset.
How it works
The platform connects directly to SIEM, endpoint detection and response (EDR), data lakes, and ticketing systems, querying each in its native format rather than normalizing telemetry into a separate store. When an alert fires, an orchestration layer plans and runs the investigation: it gathers context from the Knowledge Graph, cross-references data across sources, and a large language model (LLM) reasons over the evidence to reach a verdict. Crogl ships with three skills, Threat Hunting, Alert Investigation, and Report Creation, plus a skill builder, and produces a documented report for every alert while analysts retain the final decision.
Credentials and traction
Crogl runs in production across high-assurance environments where security data cannot leave the perimeter, including air-gapped federal deployments, critical infrastructure such as a major U.S. electric utility, and Fortune 500 financial institutions. A U.S. defense agency operates the platform inside a classified, air-gapped network. It is built for regulated security operations teams that must keep investigation data and AI inference entirely within their own infrastructure.
Key Capabilities
mapped to solution categoriesAutomatically gathers and attaches context — threat intelligence, asset and identity data — to alerts during triage and investigation.
Performs initial triage of incoming alerts automatically, classifying and prioritizing them to cut tier-1 workload before a human touches the queue.
Investigates alerts end-to-end from trigger to verdict and closes them out autonomously, so the full volume of raw alerts gets analyzed without resource-constraint concessions.
Generates investigation summaries and incident reports for analysts and leadership from completed investigation activity.
Reconstructs attack timelines and maps alert activity onto attack paths so scope and impact of an incident are clear.
Recommends the next response actions to take based on investigation findings.
Applies ML classification to incoming alerts to filter false positives, group related events, and route high-confidence detections to analysts, reducing L1 analyst workload.
Assembles chronological attack timelines from raw events across multiple data sources automatically, reducing the time to build an initial incident narrative.
Suggests the next investigative or containment steps for an alert or incident, with the supporting reasoning, so analysts can confirm and act rather than deciding from raw telemetry alone.
Integrations
compatible toolsImplementation & support
Info last updated on August 8, 2026
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