Next-gen SIEM represents the evolution of traditional SIEM. By incorporating advanced technologies like artificial intelligence, machine learning (ML), and behavioral analytics, next-gen SIEM platforms can dynamically analyze vast datasets in real time, enabling the identification of subtle, evolving threats that traditional systems might overlook.
Next-gen SIEM solutions prioritize proactive security measures, incorporating continuous monitoring, threat hunting, and incident response to swiftly counter emerging threats. These solutions are designed with cloud-native architectures, offering scalability and flexibility to meet the demands of modern, dynamic IT environments. By fostering integration, automation, and a user-centric focus, next-gen SIEM solutions empower organizations to fortify their defenses against the evolving sophistication of cyberthreats.
Traditional SIEM systems are encountering significant shortcomings in the face of the rapidly advancing cybersecurity landscape. They typically focus on logs and events from conventional sources like firewalls and servers, which often falter when confronted with the diverse, voluminous data generated by modern technologies. Relying heavily on rule-based correlation for threat detection, legacy SIEM solutions exhibit a static nature, demanding manual rule updates.
The absence of advanced analytics, ML, and behavioral analysis capabilities within legacy SIEM solutions poses a considerable challenge as cyberthreats become increasingly sophisticated with evolving tactics, techniques, and procedures. Real-time monitoring limitations and a lack of agility in processing data hinder legacy SIEM platforms' efficacy in rapidly identifying and responding to security incidents.
Furthermore, legacy SIEM platforms commonly lack robust user and entity behavior analytics (UEBA), rendering them unable to effectively analyze and correlate user behavior patterns to detect insider threats. The complexity of their deployment, configuration, and maintenance coupled with scalability issues compound the challenge. As organizations grow and generate larger volumes of data, traditional SIEM solutions struggle to scale efficiently, resulting in prolonged deployment times and increased operational overhead.
Inadequate support for cloud-native and hybrid environments is another area where traditional SIEM platforms fall short. Additionally, limited automation capabilities and the absence of seamless integrations with threat intelligence feeds impede effective incident response while outdated, complex user interfaces hinder the speed and efficiency of security analysts. Consequently, organizations are increasingly turning to next-gen SIEM solutions, which leverage advanced technologies, broader data source support, and enhanced usability to address the limitations inherent in legacy SIEM solutions.
| Feature | Traditional SIEM solutions | Next-gen SIEM solutions |
|---|---|---|
| Threat detection | Rely on rule-based detection, often struggling with emerging or unknown threats | Utilize advanced analytics, ML, and behavioral analysis for more accurate, proactive threat detection |
| Data sources | Focus on log and event data from traditional sources (like firewalls and servers) | Incorporate a broader range of data sources, including network traffic, cloud platform logs, and endpoints |
| Real-time monitoring | Have limitations in real-time monitoring since they rely on batch processing | Emphasize real-time monitoring for swift detection and response to security incidents |
| UEBA | Have limited or no UEBA capabilities for analyzing user behavior patterns | Integrate robust UEBA for detecting abnormal user behavior and insider threats |
| Scalability | Face challenges with growing volumes of data | Are built with scalability in mind, capable of handling large datasets and diverse data sources |
| Cloud support | Are often limited to on-premises environments | Are designed to support cloud-native and hybrid environments, providing visibility across distributed infrastructures |
| Automation | Have limited automation capabilities for incident response | Incorporate automation and orchestration for streamlined incident response, reducing manual intervention |
| User interface | Have complex, less intuitive interfaces | Often feature more user-friendly interfaces, enhancing the overall user experience |
Modern IT environments demand adaptive security measures because they evolve dynamically. If we look at the current scenario, network topologies have changed from how they were before. They now cover a range of users and assets, from local to cloud to remote. Additionally, data privacy regulations are getting more stringent by the day, and analysts are facing issues of low signal-to-noise ratios.
For these reasons, organizations are increasingly considering moving to next-gen SIEM technologies. If you are also on the lookout for a next-gen SIEM solution, it's essential to consider various factors beyond just UEBA, threat intelligence integrations, and cloud-native architecture.
One of the most crucial jobs of a SIEM platform is maintaining compliance with regulations. A next-gen SIEM platform should also be able to offer customizable reporting features that make it easy to generate and share compliance reports.
Seek the latest techniques, like dynamic peer grouping, which can increase the accuracy of anomaly detection. This technique groups users in a network into different clusters based on the behaviors and patterns they usually show. A baseline of behavior is established for each cluster, and the risk score of a user event is modified based on the cluster to which the user belongs. This approach provides more context on user events and reduces false positives.
The threat detection module in traditional SIEM platforms can point out known or recurring threats. But what about unknown threats? How do we make sure to not miss out on them? This is where we should be looking at the UEBA feature of a next-gen SIEM platform.
A UEBA module collects logs from different sources to understand regular user patterns and establish a general baseline of user behavior. By comparing all the occurring events with this established baseline, the UEBA module generates risk scores. Depending on the risk scores, alerts are raised for anomalous events. This feature is often missing in traditional SIEM solutions, making them too outdated to handle evolving threats.
Take into account the orchestration capabilities of the SIEM solution. Evaluate how well the solution works with the other security tools in your ecosystem, like identity management solutions, firewalls, and endpoint protection solutions. Seamless integrations ensure a cohesive security posture.
Look for a next-gen SIEM platform that allows for customization to meet the specific requirements of your organization. This includes the ability to create custom dashboards, reports, and alerts tailored to the unique security landscape of your business.
Assess the SIEM solution's ability to correlate different events to better detect attacks in their nascent stage. Look out for features like threat detection, investigation, and response as well as smart thresholds, which dynamically adjust alerts' baseline values for normal behavior based on historical data and changing network conditions. This count-based anomaly detection approach minimizes false positives by accounting for variations in user behavior and network patterns over time.
It is also important to look at the incident response capabilities of the SIEM solution, which aid in responding to these anomalous events in no time. Next-gen SIEM solutions like ManageEngine Log360 offer better incident response and post-incident analysis capabilities than legacy SIEM solutions.
