It is no longer a secret that security information and event management (SIEM) has become one of the favourite security tools for many businesses and companies. This is mainly due to the comprehensive and holistic type of security it offers, even with the addition of helping to meet compliance requirements.
However, there are several components or key elements that assemble to help in creating SIEM solutions. Apparently, these elements/components help with monitoring, detecting, and responding to security threats. Below, we will explore some of the major components and capabilities of SIEM and how to evaluate them before making a choice.
What is SIEM, And Why is it Important?
Security information and event management (SIEM) results from two functions merging together to function simultaneously. Security information management and security event management make this combination, and it involves monitoring information and events happening within an IT infrastructure. To understand how to evaluate SIEM systems, these two processes must be present as the core functions. Furthermore, combining these two allows the full collection, monitoring, tracking, and analysis of data and events.
In recent years, many organizations have shifted to integrating SIEM solutions into their security framework due to how important they are becoming. Notably, it does a lot of work with the sole aim of protecting and preventing any form of cyber threat from being successful. SIEM usually starts with collecting data, creating baselines, monitoring, detecting, and responding to security incidents. Many security solutions with next-gen SIEM functionalities, such as Stellar Cyber, integrate artificial intelligence and intelligent data feeds to detect and respond effectively to threats.
The Main Components of SIEMs
Below are some principal or primary components that should be available for SIEM systems to perform at the highest level.
Data Collection and Aggregation
One of the major components of SIEM systems, both the legacy and next-gen, is data collection, aggregation, and analysis. Log data from different locations contain information about what happened on a particular device or software, and they are stored in a centralized location for easy access and analysis. In SIEM solutions, log data can be collected in different ways: API-based log collection, agent-based log data collection, and agentless log collection.
The API-based log collection collects data directly from the devices or software using application programming interfaces. The primary characteristic of this type of log collection is that it allows remote data collection. Agent-based log collection involves the installation of agents on any device that generates log data. Thus, these agents help collect data and then move them to a central storage solution. Lastly, the agentless log collection does not need any agent installed but needs certain configurations to transfer important data.
User and Entity Behaviour Analytics (UEBA)
User and entity behaviour analytics (UEBA) is a component of next-gen SIEM solutions like Stellar Cyber offers, and it can be the difference maker for many organizations. Cybercriminals are constantly looking for different methods of attacking companies. In turn, this means that organizations have to leave a bit of convention behind if they must handle certain cyber threats.
UEBA is a necessary component that helps organizations detect and prevent complex and highly advanced security threats. It employs machine learning capabilities to create a behaviour model for all the users and devices entering an IT infrastructure. Thus, what happens is that whenever these users or devices deviate from their normal behaviour, the SIEM system is immediately alerted for further analysis.
Data Analytics
Data analytics is a component that SIEM systems have to ensure proper data analysis. It mostly comes in the form of intuitive reports and dashboards containing graphs and charts of the security overview of an organization's infrastructure. With the help of these dashboards, it help the security team in event correlation, detection of malicious activities, and provision of quick response.
While discussing data analytics as a SIEM component, one can't do away with the predefined reports it provides. These reports are mostly built based on known indicators of compromise (IoCs), and they help to provide more visibility into security events, detect threats, and address compliance issues.
Data Storage
You can't talk about a security solution that collects and analyzes data without discussing how it stores it. Previously, many legacy SIEM solutions had to rely on storage spaces deployed in the data centres, but this is a liability. The issue here was that it was very challenging to store and manage the large data volumes the SIEM solutions produced.
Hence, many security solutions with next-gen SIEM functionalities are building their data storage facilities in the cloud. Here, they have unlimited options for data lake technologies such as Hadoop and Amazon C3, which provide an unseen level of scalability. In other words, using these new and modern data storage systems allows easy expansion of an organization's IT infrastructure.
Policy Creation and Rules
Although there are modifications in the concept of policy creation, it still has the same functionality of helping to detect normal and malicious activity. Policy creation helps the security team set thresholds for how the IT infrastructure or enterprise system should behave at any moment. For instance, a policy can say that 10 attempted logins within a space of 2 minutes is a malicious activity.
Thus, when something like this happens, it becomes straightforward for the security operations centre to determine if it's malicious. Moreover, many next-gen SIEM solutions now employ AI and machine learning functionalities to help automate the detection of anomalies.
Data Retention and Compliance
Data retention is another major SIEM component, which is when the SIEM system stores important data for a long time. This is vital for many reasons, including tracking, reporting, forensic investigations, and compliance purposes. Since many organizations need to meet certain regulatory requirements in different countries, SIEM systems automate the collection of required compliance data. HITECH, SOX, HIPAA, PCI/DSS, and GDPR are some of the compliance standards that reports generated by SIEM systems help to meet.
Wrapping Up
Above, we discussed the meaning and importance of an organization having SIEM solutions in its security architecture. Moreover, we also established that certain components and capabilities must be in place for SIEMs to function effectively. These components include data collection and User and Entity Behaviour Analytics (UEBA), which helps analyze user behaviour. Other critical components are policy creation and rules, data analytics, data storage, and data retention and compliance.
