All the data in the world has no value unless it’s transformed into actionable security insights. Modern SIEM solutions use advanced analytics capabilities, such as machine learning to identify abnormal behavior in real-time and detect security incidents.
The critical components of a robust SIEM architecture include Ingestion: Software agents collect event data from network devices such as servers, firewalls, routers, and cloud systems.
So, what is SIEM in cybersecurity? The core of SIEM is its event data, a set of records containing information about security incidents. This includes event logs from network routers, servers, and other devices and events from user activities in enterprise systems.
A SIEM’s correlation engine analyzes all these events to detect and report security incidents. It does this in real-time using a complex event processing (CEP) system, which is an intelligent processor that understands the meaning of the events being recorded and correlates them accordingly.
Correlation is the key to detecting threats, and the best SIEM solutions provide pre-configured correlation rules that help enterprises detect and respond to attacks quickly. However, these rules depend on indicators of compromise (IOCs) and are ineffective in detecting more advanced techniques attackers use to hack into enterprise networks.
Moreover, the performance and scalability of a SIEM solution depend on the amount of event data that is processed and the speed with which the results are available. For example, if a SIEM collects millions of events per day and processes them in less than a millisecond, it will require a compelling computing platform and many CPU cores.
Modern systems produce massive amounts of logs, but there’s a high chance that most of them aren’t helpful. Maybe you have a redundant system that spews log messages for every page load or an app your team only uses sparingly, generating minimal value.
An excellent way to keep costs down and reduce toil is using log filtering to store the needed logs. This can also help you avoid wasting time searching records for relevant data when troubleshooting.
To set a log filter, click System | Collection | Log filters. This displays a table of the log filters that are currently in effect. The table’s columns indicate the type of log filter (for example, Filter data context, Filter senders, or Storage), and each row describes the condition that must be met for the log to be filtered.
Each log filter rule is configured to match data based on a query. When triggered, the filter removes matching data from the ingestion pipeline before it is written to the New Relic database. This reduces the amount of data forwarded to NRDB, thus cutting costs and freeing up resources. The filter also helps you control more detailed logging settings not handled by the default log level setting.
Security information and event management (SIEM) help enterprises detect data breaches and other malicious activities by constantly monitoring and analyzing network devices. It collects and analyzes logs from various network appliances, allowing security teams to identify and respond quickly to any threats.
The architecture of a SIEM solution should be designed to meet the needs of its users and the environment in which it is deployed. For example, the system should be able to scale up or down depending on the volume of events being processed and stored. It should also be able to handle multiple streams of log data. This is important because attackers can be very subtle and might not be detected if only a few data streams are monitored.
One way to scale a SIEM is by using a SIEM-as-a-Service model, which provides the analysis services for a fee. However, this requires organizations to have the proper human resources and expertise for managing the platform.
Another approach uses a software-based architecture that can scale up or down as needed. This method is often preferred as it allows for a more cost-effective implementation of a SIEM. SIEM solutions use a variety of techniques to collect and analyze logs. One standard method is to deploy agents responsible for collecting and forwarding logs to a SIEM server. These agents can filter the records at the device level based on predefined parameters and perform summarization to reduce log storage size.
This component of the SIEM architecture focuses on how data is stored. This includes file formats (e.g., Syslog), hardware deployment, and storage options such as centralized on-premise, cloud, or virtualized systems. Also, consider whether any log data is sensitive and should be encrypted as it arrives at the SIEM. Define a retention policy and processes. Determine what types of data need to be backed up and for how long and the process for deleting records once their retention period expires.
Once the data has been collected, it must be processed to make it worthwhile for monitoring and alerting. This is accomplished through various techniques, including normalization, aggregation, and filtering. Additionally, next-generation SIEMs offer user and entity behavior analytics (UEBA) technology that utilizes machine learning and behavioral profiling to detect unnatural patterns in data compared to historical data.
A robust SIEM architecture is vital to an organization’s cybersecurity strategy, protecting it from costly breaches and providing valuable information to help security teams identify incidents quickly and efficiently.
To realize the total business value of a SIEM solution, organizations must understand the scope and requirements of the system to be deployed and evaluate multiple solutions through exhaustive POCs. With the average cost of a breach reaching millions of dollars, the return on investment in a quality solution is readily apparent.