SIEM technology collects security-related data from servers, end-user devices, and network equipment. This data is then sent to a central data storage, where it’s organized and archived for compliance and forensic purposes.
SIEM systems also feature event correlation, which sifts through massive amounts of log data to identify patterns that might indicate an attack. This enables enterprises to reduce the mean time to detect (MTTD) and mean time to resolve (MTTR).
Invest in High-End Hardware
Investing in high-end hardware is critical for ensuring the system can quickly analyze and process data. This will prevent the system from bogging down or slowing down the rest of your network and allow you to use its capabilities thoroughly. It is also essential to consider what types of data you want your SIEM architecture system to collect. More data is not always better, so research realistic expectations and leave room for adjusting the configuration as needed.
Another important aspect of scalability is log filtering. Not all security alerts are equal, and analysts must have immediate access to context that will help them understand the significance of an event or threat. SIEM systems help with this by normalizing logs into a standard format and applying filters to the data they store.
It is also necessary for SIEM to provide contextual information about potential threats, including their location on the network and what types of digital assets they are targeting. This will allow security teams to prioritize their response and mitigate cyberattacks in a more targeted manner. SIEM solutions help by correlating different security events and presenting them in dashboards for analysts to review. They are also designed to detect and respond to abnormal behavior, such as failed login attempts and unusual website activity.
Deploy a Hybrid Cloud
SIEM systems require a lot of storage for raw log data, indexes to speed search and reporting, and metadata. The total capacity needed can easily exceed 50 TB daily, especially if compliance requires keeping historical records for a year or more. Adding to this storage requirement is high-performance, low-cost hardware to enable real-time monitoring and analysis. This requires a high throughput SAN, many servers running potentially expensive OSs and databases, and costly software licenses for the BI/reporting tools.
To reduce storage requirements, many SIEM solutions include built-in mechanisms for aggregating and condensing events to produce reports and alerts. In addition, they can be augmented with machine learning capabilities that can help identify anomalies and recognize compromised accounts or ongoing threats. The best way to improve your SIEM’s effectiveness is to ensure its input data is clean and correct. For example, a SIEM will not understand that an error code like 427 means someone downloaded a database unless you educate it.
Also, ensure all your log sources promptly send the information they need to the SIEM and that the SIEM is filtering out the types of events you don’t want it to report. This will help it avoid wasting processing power on irrelevant, low-priority incidents.
Build a Scalable Infrastructure
A SIEM needs a robust and scalable infrastructure to analyze and make sense of log data. This includes hardware, software, and storage for storing log files and a database for processing the logs. Designing composable data systems has also been very helpful with this process, learn more on this at https://voltrondata.com/codex/a-new-frontier
A key consideration when sizing your infrastructure is determining the number of Events Per Second (EPS) your network devices generate. This can be done by inventorying the number of devices in your network and multiplying their average EPS by the total number of seconds in a day. This will give you a rough estimate of the size of your SIEM infrastructure.
Another factor when sizing your SIEM infrastructure is deciding how you will store and process your log data. You can deploy a Syslog server to collect and store the raw log data from your network devices or use a database for longer-term storage. You will also need to consider how you will compress the logs and implement retention schedules to delete old data.
The Correlation Engine is a vital component of a SIEM solution, as it enables the system to detect security attacks by putting together events and patterns common to different networks. To do this, the correlation engine examines the raw log data from the network devices and identifies any potential threats using predefined or user-defined rules.
Invest in Analytics
SIEM solutions collect a stack of logs and event data, collate them, and analyze the aggregated information. They rely on machine learning algorithms to flag anomalies in the event stream, but these systems can only be effective with plenty of high-quality data to train them.
As a result, cybersecurity professionals need to ensure all data sources are properly logging events. This includes network devices and servers, antivirus software, specialized security equipment, identity and access management systems, and vulnerability scanner systems. Setting up data aggregation across multiple locations is also a good idea.
Data normalization is another critical process that turns raw and unstructured data into something a SIEM can understand. This includes parsing, categorization, data enrichment, and more. It’s crucial to test these systems to see how they perform.
These steps help ensure your SIEM solution can scale and meet your organization’s security needs. However, it’s important to remember that more isn’t always better regarding scalability for SIEM solutions. Too much data can cause a system to slow down or even crash, leaving the system susceptible to cyberattacks. You can avoid these risks by planning carefully and implementing a robust architecture. This way, you can get the most out of your investment.