Data collection
The majority of SIEM systems deploy collection agents on user devices, servers, network hardware, or other security systems like firewalls and antivirus, or they employ the syslog forwarding, SNMP, or WMI protocols to gather data.
Data storage
As a result, just a portion of the log data was kept. Modern data lake technologies like Amazon S3 or Hadoop, which enable practically infinite storage scalability at a minimal cost, are the foundation upon which next-generation SIEMs are created. This enables the retention and analysis of all log data across a wider range of platforms and systems.
Policies and rules
They can then establish guidelines and cutoff points to specify what kind of anomaly qualifies as a security event. More and more, SIEMs use automated behavioral profiling and machine learning to automatically detect anomalies and dynamically construct rules on the data to find security events that need to be looked into.
Data consolidation and correlation
An incorrect password attempt on an enterprise portal and a connection being denied by a firewall can both be related to error messages on servers. Security events are created from many data points and given to analysts via dashboards or notifications. The ability of next-generation SIEMs to identify "real" security events that require attention is advancing.