How Does a SIEM Work?
Most SIEM solutions collect data from across the organization using agents installed on various devices including endpoints, servers, and network equipment, as well as other security solutions, such as firewalls or other network security appliances.
Next-generation SIEM solutions include support for cloud applications and infrastructure, enterprise applications, identity and HR data, and non-technical data feeds as well.
Data enrichment adds context to an event. SIEM solutions should enrich incoming data with identity, asset, geolocation, and threat intelligence to aid in investigations. Data enrichment fills in critical information that a SIEM needs in order to correlate related events together and aid in threat detection.
After data enrichment, security data is stored in a database. There the data can be searched through and referenced to during investigations. Sometimes only enriched data is stored, but sometimes the unenriched data is stored as well. It all depends on what the organization requires.
Next-generation SIEM leverages an open source, big data architecture in order to take advantage of their unlimited scalability and ability to store historical data in a way that it can easily be searched.
Apply Correlation and Analytics
SIEM solutions use different techniques to draw usable conclusions from the data and find anomalies. These analytics techniques vary widely from vendor to vendor.
Legacy SIEMs rely on simple correlation and signature-based alerts. They are prone to error, produce a lot of noise in the form of false positives, and can only find known threats. This causes analysts to waste time chasing events that may or may not be credible threats.
A next-generation SIEM uses advanced analytic techniques, beyond signature-based approachs, to catch known and unknown threats. They use sophisticated machine learning algorithms to detect threats more accurately.
User and entity behavioral analytics (UEBA) is one type of advanced analytics that is integrated into next-generation SIEMs in order to provide better detection. Another technique used are threat chain models. These models help stitch together connected alerts in order to consolidate separate but related alerts into a threat sequence, increasing the risk score of the overall threat.
Investigate and Mitigate Threats
At a basic level, a SIEM should have the ability to integrate with a third-party security orchestration, automation, and response (SOAR) solution to assist analysts as they investigate and mitigate potential threats. A SOAR solution gives analysts a workbench to collect information, track steps taken during the investigation, and remember how the threat was mitigated or whether it wasn’t a real threat.
A next-generation SIEM will incorporate SOAR capabilities natively into the platform for increased security operations efficiency and ROI. This includes playbooks, orchestration workflows, and automation. Save your team time by leveraging machine-learning powered automation that can learn how to solve your problems and guide your T1 analysts in the right direction faster.
Provide Data Insights and Reporting
A next-generation SIEM gives you the ability to search across your data quickly, allowing you to dig into alerts and search for threat actors and indicators of compromise.
You can also pivot on any entity in order to develop valuable threat context and get a full 360-degree view of the attack. Visualized data can be saved as dashboards or exported in a standard data format. You can also use out-of-the-box reports or create ad-hoc reports as needed for your incident response and compliance needs.
Securonix Next-Gen SIEM
A cloud-first next-generation SIEM with compelling detection and response ROI and zero infrastructure to manage. Our solution provides a single pane of glass for detection and response in the cloud, where a company’s data resides, and our integrated UEBA surfaces high-priority threats instead of false positives.