- Anomaly Detection
- Behavioral Analysis
- Behavior Analytics
- Cloud
- Compliance
- Critical Infrastructure
- Cybersecurity
- Data Center
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Who is CyberFlow Analytics?
CyberFlow Analytics is a cybersecurity and network analytics company that focuses on machine learning-based monitoring of network traffic and assets for threat detection and operational visibility.
- Network traffic analytics for security monitoring and anomaly detection (security analytics)
- Machine learning-based behavioral analysis of devices and users (behavior analytics)
- Visualization of network flows and asset relationships for Security Operations (SecOps) (security operations tooling)
- Support for monitoring industrial control systems and enterprise OT/IoT networks (OT/IoT security)
- Integration with existing SecOps workflows and tools (security operations)
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More About CyberFlow Analytics
CyberFlow Analytics focuses on applying Machine Learning (ML) and flow analytics to enterprise and industrial network traffic to detect anomalous behavior that can indicate cyber threats, misconfigurations, or policy violations. Its technology is typically deployed in environments where security teams need visibility into east-west traffic, device behavior, and communication patterns across IT, Operational technology (OT), and Internet of Things (IoT) networks.
The company’s offerings are generally positioned in the security analytics and network traffic analysis (NTA) categories. Deployments often System Integration Testing (SIT) alongside firewalls, Security Information and Event Management (SIEM) platforms (security information and event management), and endpoint security tools, supplying enriched context on network flows and behavioral baselines. By analyzing flow-level data rather than only packet payloads, CyberFlow Analytics tools can operate in environments with encrypted traffic while still surfacing anomalies in communication patterns, volumes, and device roles.
CyberFlow Analytics architectures typically ingest flow records and metadata from routers, switches, and other network devices, applying ML models and statistical methods to classify traffic, establish baselines, and score anomalies. These capabilities align with User and Entity Behavior Analytics (UEBA) and Network Behavior Analysis (NBA) approaches, with dashboards and visualizations that allow SecOps center (SOC) teams to investigate alerts, trace lateral movement, and understand relationships between assets.
In industrial and critical infrastructure contexts, CyberFlow Analytics targets monitoring of OT and industrial control system (ICS) networks, where device discovery, protocol visibility, and behavior modeling are important for both security and reliability. By mapping communication patterns among field devices, controllers, and supervisory systems, the platform can highlight unexpected connections or traffic paths that may require investigation by security or engineering teams.
From a directory and taxonomy perspective, CyberFlow Analytics fits into security analytics, network traffic analysis, and OT/IoT security categories. Its core solution areas include network behavior analytics for threat detection, visual correlation of flows and assets for SOC workflows, and support for hybrid infrastructures that span data centers, cloud environments, and industrial networks. The company’s tools are generally used by SecOps teams, network security engineers, and risk or compliance stakeholders who require detailed network-level telemetry integrated into broader enterprise security architectures.
Our description of CyberFlow Analytics. Updated December 2025.
