The Questions Practitioner Should Ask.
Our Answers.

The AI features in your SIEM, EDR, and SOAR generate recommendations and then wait. A human still has to read the suggestion, gather context, query the data, and make the call. The AI accelerates the analyst. The analyst still does the work.

Crogl operates differently. The moment an alert enters your environment, or a threat advisory lands, Crogl agents conduct the full investigation autonomously. No prompt. No human trigger. Your analysts receive complete, documented findings and make the call.

This is also why Crogl isn't a single model. It's a compound AI system, a knowledge graph, multiple AI models, and agentic orchestration working together. The knowledge graph provides the environmental context that LLMs alone can't supply. The agentic layer executes the workflow. The result elevates analysts to decision makers, not query runners.

Most security automation breaks on anything outside its ruleset. If the playbook doesn't exist, the alert sits. This is the core failure mode of playbook-driven tools, and it's why alert backlogs exist.

Crogl's knowledge graph continuously maps your environment: users, assets, behaviors, relationships, and history. When a new alert arrives that no one has written a playbook for, Crogl reasons from that context. It knows what's normal. It understands what's anomalous. It executes an investigation drawing on security principles, your organization's specific context, and the MITRE ATT&CK framework.

The answer to "what if you've never seen it before?" is: Crogl investigates it anyway.

Crogl deploys entirely within your controlled environment: on-premises, private cloud, or fully air-gapped. No data reaches Crogl's infrastructure. No investigation result, no alert content, no query output transits an external network.

This isn't a compliance checkbox. It's why a U.S. Department of War agency operating in a classified, air-gapped environment with extreme security requirements runs Crogl in production today.

If your organization requires data sovereignty, Crogl is built for it.

Beyond the built-in library, Crogl includes a skill builder so your team can create new skills using the same AI system that runs the platform. Your detection engineers define the workflow. Crogl executes it. Every new skill your team creates makes the system more capable in your specific environment.

No vendor dependency. No waiting for a product roadmap. Your team builds what they need.

There's no schema normalization phase. No playbook authoring sprint. No recoding of detection logic.

Crogl connects to your existing SIEM, EDR, data lakes, and ticketing systems directly, querying them in their native format. The system begins investigating from the moment it's connected. It learns continuously from your team's actions, feedback, and escalation decisions, refining its investigations over time.

The reason most organizations stay on outdated SIEMs is simple: the migration cost is enormous. Every detection use case, every schema mapping, every playbook has to be rebuilt from scratch. For large environments, that's months of work and a window of reduced coverage.

Crogl changes this because your investigation logic lives in Crogl and not in your SIEM. When you migrate, you're moving a data source, not your entire detection capability. Schema mappings and playbook rebuilds aren't required. Coverage continues uninterrupted.