What does an AI SOC need beyond a promising demo? Security teams building their own AI SOC and agentic SOC capabilities find out when a prototype has to run every day. Operationally relevant AI needs uptime, governance, consistency, and auditability. It also needs secrets management, role-based access control, integrations with dozens of tools, maintenance, and security testing. 

This datasheet explains how Crogl, AI for Enterprise Security, delivers those capabilities without a build project. Crogl works interactively or autonomously to triage and investigate alerts, work tickets, run threat hunts, and check compliance. It works directly with non-normalized security data, querying your SIEM, EDR, cloud platforms, and threat intelligence feeds in their native languages, including SPL and KQL. Findings are correlated automatically, with no months-long data engineering project before the first result. 

Every AI decision is auditable, with the reasoning and evidence chain visible to the analyst and the auditor. Crogl handles the investigation. The analyst makes the call. Crogl runs in a customer-managed cloud, on-premises, or fully air-gapped, which makes it a fit for regulated and privacy-constrained environments. The results hold up where requirements are strictest. In an air-gapped, classified Department of War deployment, Crogl investigates more than 1,000 alerts a day across 3 SIEMs and 2 SOARs. A Fortune 500 financial services institution runs cross-data-lake investigations in minutes instead of about an hour. 

The datasheet also covers alert triage with context-aware prioritization, investigation with MITRE ATT&CK alignment, automatic documentation, and integrations including Splunk, CrowdStrike, Databricks, ServiceNow, Jira, and Cribl. Download Crogl at crogl.com/download.