A 2022 SIEM Report shows how important security information and event management (SIEM) is to organizations. An overwhelming 80 percent of the study’s respondents agree that SIEM is extremely significant to their security. A primary threat detection mechanism, SIEM is critical in the handling of vulnerabilities and attacks as well as in post-incident forensics.
SIEM is indispensable as it provides security analysts with the means to deal with security incidents. The majority of organizations that use SIEM express higher confidence in their security posture, at around 60 percent, as compared to those that don’t, which have a 46 percent confidence level. However, there is a need to update SIEM in view of the changing threat landscape.
Legacy SIEM systems tend to over-promise with the protection they provide. They claim to consolidate security data from various sources, but they actually under deliver because of low integration support. They may be advertised as flexible, but they may not work that well with cloud and hybrid environments. Some even claim to be AI-driven, but they are actually only using rules and simple algorithms to automate certain processes.
The need for a NextGen SIEM platform that is capable of addressing new and more complex threats is inherent. Also, it should be able to address challenges that come with security information and event management, especially the following:
NextGen SIEM addresses these challenges to maximize the benefits of SIEM to an organization. The solution to the different challenges listed above can be summarized by these main features: AI/automation and flexibility.
Threat identities are no longer reliable in detecting and preventing attacks. Threat actors have developed creative strategies to successfully evade detection systems and deceive the human security analysts who review security alerts. For this, NextGen SIEM incorporates AI to enable contextualization.
Contextualization is achieved by correlating alerts with incidents to bring out the most important data and facilitate considerably faster detection, investigation, and response. This effectively cuts down the time it takes to detect and respond to threats, from days or weeks to a matter of minutes.
NextGen SIEM goes beyond logging and provides the ability to gather and analyze contextual data. Context is a must when dealing with massive amounts of data on a regular basis. As mentioned, noisy data is a major problem in conventional SIEM. It is impossible to address all security alerts and find the most urgent threats or vulnerabilities detected by the different security controls used by an organization.
Contextualization generates actionable data and facilitates the prioritization of more critical concerns to enact the suitable and prompt responses. It establishes a focus on crucial security data that would otherwise drown amid all the “noisy data” generated by conventional SIEM.
It is also worth noting that AI helps in automating some tasks to expedite processes. Some NextGen SIEM platforms come with the ability to translate processes into automated playbooks to make threat hunting easier and faster. They can also be set to undertake automatic responses to specific scenarios.
Contextualization and automation address the noise, lack of context, as well as the special skills weaknesses associated with the use of conventional SIEM. The involvement of Ai does not necessarily replace human security analysts, but it considerably lightens the workload of those responsible for running an organization’s SIEM operation. Also, the high costs of SIEM drop with the reduction of detection, investigation, and response times as well as the reduced need (because of automation) for employees running SIEM.
Next-generation SIEM platforms are designed to be deployable anywhere. They work with on-premise, cloud, multi-cloud, and hybrid environments. They can be implemented in multi-tenancy and multi-site schemes to suit the varying needs of modern organizations. Multi-tenancy deployment enables flexibility without security compromises. Multi-site support makes it possible for data to have its own region to be compatible with complex operating environments.
Additionally, the flexible design of NextGen SIEM may also come with a scalable architecture to eliminate concerns about changing operational scales and data volumes. Some platforms take on a microservice-based cloud native architecture approach to support horizontal scaling in accordance to demand. These scalability features may also include system monitoring and resource sharing functions.
Integration support for a wide range of applications is also a given for NextGen SIEM. This is important to take full advantage of existing data sources such as endpoints and SaaS applications. They may have log parsers, network sensors, and API connectors that make it easier to integrate data from third-party applications or security controls. It would be difficult to undertake comprehensive security data gathering and analysis to have optimum visibility over attack surfaces.
Moreover, NextGen SIEM platforms may have network detection and response (NDR), endpoint detection and response (EDR), user entity and behavior analytics (UEBA), and threat intelligence platform (TIP) features to further bolster cyber defenses.
In terms of data handling and reporting, just like how most other modern cybersecurity solutions are designed, NextGen SIEM platforms are usually built to produce easier-to-understand reports. They come with customizable reporting templates to quickly generate reports suited for different audiences. Auditing and compliance reports are also available to make it easy for stakeholders to see their organization’s deviations from industry security standards.
Next-generation solutions exist to address weaknesses or inadequacies in their predecessors in view of changing needs. For SIEM, the successor has to resolve the inefficiencies, uncertainties, and lack of flexibility. This is why NextGen SIEM incorporates artificial intelligence, automation, and broad integration support. These improvements enable contextualization to better handle security data overload, achieve interoperability, and make SIEM’s output more usable for SIEM to be more effective as a security solution.
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