Log Data Analysis:

The core component of RaptorEye SIEM is the collection and normalization of logs, putting them forward in a more analytical manner.
At the point of ingestion, flexible log collecting from on-premises or the cloud guarantees that metadata is automatically gathered, giving you visibility as soon as feasible. Through our innovative Machine Data Intelligence Architecture, RaptorEye SIEM effectively normalizes log data and enriches it to increase searchability and analytics across various log sources.

Log management entails more than just gathering and normalizing logs. Learn exactly what your data means with the help of the RaptorEye SIEM platform. In order to protect your network and automate compliance, threat detection, and response, we specialize in normalizing log and machine data and generating relevant insights in order to saturate security intellectuals more accurately.
Gather data from every gadget, computer, program, and network device you can find. Each log message is categorized and contextually structured by our Machine Data Intelligence (MDI) Fabric.

Do not skip important attack sequences. As time zone, device clock offsets, and collection offsets are automatically corrected by our patented TrueTime architecture. Get the true and exact time of every security incident recorded so far.
With our centralized log management, you can reduce noise and obtain useful insights. RaptorEye SIEM can be installed in a company's data center for centralized log aggregation and event management, even across different systems, in extremely distributed environments. .




Threat detection

RaptorEye SIEM instantly alerts you to important changes made to your files and folders. It executes real-time file integrity monitoring.

File Integrity

RaptorEye SIEM instantly alerts you to important changes made to your files and folders. It executes real-time file integrity monitoring.

Notifications & Alerts

Important components of the RaptorEye SIEM solution are notification and alerting. According to particular data points discovered during the log collecting