Transforming SecOps: How Hyperautomation in Next-Generation Multi-SIEM Environments Enhances Threat Detection and Response

Torq’s Leonid Belkind offers insights on transforming SecOps and how hyperautomation in multi-SIEM environments enhances threat detection and response. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

Modern digital enterprises are embracing hybrid cloud architectures at an increasing pace. But establishing a tight security events pipeline remains a challenge. This holds true whether the organization is on a multi-year migration plan from its current–predominantly on-premises–IT architecture towards a hybrid-cloud or a multi-cloud solution, or a balance has already been established between the IT stack components that remain hosted on premises and those which are better off delivered using cloud-based SaaS / IaaS / PaaS infrastructure.

High data transfer costs, paired with non-trivial data processing and storage costs, often lead organizations to handle their security events “close to the source,” leaving the processing of events originating from on-premises stack components in a traditional SIEM, while leveraging cloud-native SIEM, or security data lake solutions for events originating in the cloud platforms. Significant differences in technology across on-premises and cloud-based stacks also contributes to security event data being very different in complexity, form, and fidelity across different platforms.

Integrating multiple SIEM solutions across cloud platforms has become a significant challenge for SecOps teams operating in modern hybrid-cloud enterprises, creating data silos that hinder event correlation and threaten effective attack detection.

Yet, with advancements in security automation, the management of multi-SIEM environments is transforming, making it easier for teams to strengthen security postures with confidence in addressing an ever-evolving threat landscape through enhanced data correlation and analysis, automated, enhanced incident responses, and reduction of alert fatigue.

Improving Data Correlation and Analysis to Enhance Threat Detection and Response

One of the most significant challenges in modern multi-SIEM environments is the ability to better correlate security events across disparate systems. Even traditional SIEM solutions often struggle with event correlation due to the fragmented nature of data collected from multiple sources. This fragmentation often leads to inefficiencies in identifying patterns and anomalies indicative of potential threats. Now, with the reality that has very different security events stored and processed in more than one event management platform, the need to aggregate and correlate data from multiple SIEM solutions is even more important to an organization’s threat detection and response capabilities.

In a security operation that covers a multi-SIEM environment, security teams should strive to obtain a comprehensive view of the security landscape, leveraging data from all integrated SIEM platforms. Hyperautomation allows the SOC to implement a multi-SIEM strategy efficiently without negatively affecting current processes or operational performance.

By automating triage, investigation, and remediation across various SIEMs, organizations can transition to cost-saving benefits and confidently transition away from the limitations of legacy SIEM systems. By consolidating this data, SecOps teams can perform more effective event correlation and analysis. Advanced algorithms and machine learning techniques can de-duplicate events, reducing noise and highlighting genuine security concerns. This improved correlation enables security teams to identify and respond to threats more accurately and swiftly.

A key motivation for organizations to adopt emerging best practices in data aggregation and event management is cost reduction. The increasing volume and variety of data have turned legacy SIEM solutions into a significant financial burden, prompting teams to send only essential data to the SIEM, creating gaps in security threat detection and investigation. Now, modern cloud-native data platforms have emerged to address these challenges, these include ETL data orchestrators, next-gen SIEMs, cloud security data lakes, and multi-data SIEMs, which are designed to filter, cleanse, and structure security data more effectively, enabling real-time detection and threat hunting while minimizing expenses.

This holistic threat detection approach enables the identification of complex attack patterns that might go unnoticed in isolated SIEM systems. By correlating events across different environments, security teams can uncover multi-stage attacks and sophisticated threat vectors, improving their overall threat detection accuracy. Consequently, the enhanced detection capabilities lead to more effective threat response, reducing the risk of substantial negative impacts on the organization.

Centralizing Repositories and Decreasing Incident Response Time

A centralized repository of security events is a game-changer for incident response processes. In a hybrid cloud environment, SecOps teams often face the challenge of sifting through fragmented data spread across multiple SIEM platforms, prolonging the time needed to identify and respond to security incidents and hindering organizations from fully leveraging the value of their data to their advantage.

Leveraging hyperautomation in multi-SIEM environments streamlines this process by consolidating data into an equivalent of a single, accessible repository. While there isn’t necessarily anything close to parity when looking at the data located in different environments, building a logical level on top of the data queries allowing a sensible comparison of “apples to oranges” on the level of security risks, threat techniques and controls, is what allows security operations teams to work efficiently across multiple platforms.

This centralization allows security teams to quickly retrieve and analyze security events, significantly reducing the time required for incident investigation and response. Faster incident response not only minimizes potential damage but also enhances the overall resilience of the organization’s security posture.

Reducing Alert Fatigue

Alert fatigue is a common problem in all kinds of SecOps environments. Multi-SIEM environments are on the rise, as reported by one report, mentioning 43% of all participating organizations operating more than one SIEM platform, which means security analysts are inundated with numerous alerts, many of which are false positives. This overload can lead to missed critical incidents and decreased threat management efficiency.

By integrating data from multiple SIEMs into a joint events pipeline and utilizing modern security automation, SecOps teams can implement more effective filtering and prioritization of alerts. Advanced analytics and machine learning models can distinguish between genuine threats and benign activities, significantly reducing the number of false positives. This reduction in alert noise allows security analysts to focus on the most critical incidents, improving overall efficiency and effectiveness in threat management.

The evolution of multi-SIEM environments coupled with hyperautomation represents a significant paradigm shift in cybersecurity operations. Organizations can achieve enhanced threat detection and response capabilities by centralizing data from disparate systems — ultimately improving incident response times and reducing cost. With modern cloud-native platforms and advanced analytics, security teams can proactively identify and mitigate complex threats, bolstering their security postures, maintaining trust, and safeguarding critical assets in an increasingly digital world.