Advanced user and entity behavior analytics that detect anomalies and threats by analyzing patterns, activities, and deviations from normal behavior.
Behavioral analysis is a critical component of modern cybersecurity, enabling the detection of threats that traditional signature-based systems miss. RazorSecurity's behavioral analysis systems continuously monitor user activities, network traffic, and system behaviors to identify anomalies that may indicate security breaches or insider threats.
The platform correlates signals across endpoints, identity systems, and network telemetry to build a unified view of activity. This makes it easier to distinguish benign anomalies from true threats and reduces alert fatigue.
Our systems establish baseline behavioral profiles for users and entities, then continuously monitor for deviations. This enables early detection of compromised accounts, insider threats, and unauthorized access attempts by identifying unusual patterns in login times, locations, data access, and system interactions.
We analyze network traffic patterns to identify anomalies such as data exfiltration, lateral movement, and command-and-control communications. Our systems can detect advanced persistent threats (APTs) that use legitimate protocols and blend in with normal traffic.
Behavioral analysis complements signature-based detection by identifying zero-day attacks and sophisticated threats that haven't been seen before. By focusing on behavior rather than specific attack signatures, we can detect novel attack techniques and adapt to evolving threats.
Risk scoring and contextual enrichment prioritize the most critical events, guiding analysts to the highest impact incidents first.
Our behavioral analysis platform combines machine learning algorithms, statistical analysis, and graph databases to process and correlate vast amounts of behavioral data. We use Apache Spark for real-time stream processing, Elasticsearch for data storage and retrieval, and custom ML models trained on security-specific datasets to achieve high accuracy in threat detection.
A dedicated feature store maintains consistent behavioral signals across models, while privacy-aware data handling ensures sensitive user attributes are protected and access is tightly controlled.