Information theft is a big business and corporate information that features project deals and trade secrets. Hence, information management is a necessary component of any program.
That is where Security Information and Event Management (SIEM) monitoring comes into play.
What is SIEM?
Security Information and Event Management (SIEM) is a technology for cyber security that provides real-time analysis of security alerts generated by hardware as well as network applications.
AI and Ml SIEM monitoring supports earliest threat detection and fastest security incident response through the real-time collection and historical analysis of security events that are compiled from a broad variety of event and contextual data sources. SIEM tools also support compliance reporting and incident investigation via historical data analysis from the sources.
LTS Secure AI and ML SIEM is capable of offering an effective and efficient means to monitor your network round the clock. Continuous monitoring from SIEM as a service includes all devices, servers, applications, users, and infrastructure components.
How SIEM works?
You may spend a lot of money buying a Security Information and Event Management (SIEM) product from your trustworthy SIEM vendors but if you do not follow through and use the SIEM properly, SIEM monitoring functionality and SIEM tools will fail to protect your information.
SIEM as a service monitor at your network through a larger lens than can be provided by a single security control or information source. For example:
SIEM Benefits
The benefits of AI and ML SIEM monitoring are as follows:
This is the most important benefit offered by SIEM as a service tools. It streamlines their compliance reporting and efforts through a centralized logging solution. Any host that needs to have the log of its security events included in the reporting can regularly transfer its log data to a SIEM server.
Some incidents can be detected only by AI and ML SIEM tools. This is because of two reasons. First, many hosts that log security events do not have built-in incident detection capabilities. Hence, they lack the ability to analyze the log entries and there is no guarantee of identifying signs of malicious activity.
The second reason for AI and ML SIEMs’ advanced detection capabilities is that they can correlate events across hosts. By gathering events from hosts across the enterprise, a SIEM tool can see attacks that have different parts seen by different hosts. After that, it can reconstruct the series of events to determine what the nature of the attack was and whether or not it succeeded.
After SIEM tools examine the log data for all events, they can figure out malware infection in the device that due to infection joined a botnet and started attacks against other hosts. They can also stop cyber attacks in progress.
On the detection of any activity involving known malicious hosts, SIEM tools can terminate the connections of those hosts. They can also disrupt interactions between malicious hosts’ and organization‘s hosts in order to prevent an attack before it occurs.
SIEM tools increase the efficiency of incident handling, which in turn saves time and resources for incident handlers. More efficient incident handling speeds up the process of incident containment. Hence, it reduces the amount of damage that many incidents cause. SIEM monitoring improves efficiency by providing a single interface for viewing all the security log data from many hosts.