SIEM Data Ingestion: Bane of the SOC?

For most current SIEMs, their primary function is to collect and ingest data, primarily logs, across the entire network. While the core function and deployment of the SIEM has been for logging, data retention and compliance, it has evolved over the last decade to be more focused on identifying increasingly complex threats.

Understanding the Difference Between SOC and SIEM

Understanding the difference between a SOC and SIEM is essential for effectively managing and responding to cybersecurity threats. These two components are interconnected but serve distinct roles in an organization’s security posture.

A SOC is a centralized team or facility responsible for monitoring, detecting, responding to, and mitigating security incidents and threats in real-time. It consists of security analysts, incident responders, and other cybersecurity professionals who work together to protect an organization’s digital assets. The SOC is the human element in cybersecurity operations, employing expertise and judgment to investigate alerts, assess the severity of incidents, and make informed decisions on how to respond. It coordinates incident response efforts and plays a critical role in managing security incidents effectively.

On the other hand, a SIEM is a technology solution that acts as the nerve center for collecting, aggregating, correlating, and analyzing security event data from various sources across an organization’s IT infrastructure. It automates the process of collecting and organizing vast amounts of data, generating alerts based on predefined rules or machine learning algorithms. The SIEM provides visibility into an organization’s security posture, helps identify patterns and anomalies in data, and supports compliance reporting. While the SIEM is a powerful tool for data analysis and alerting, it relies on human intervention from the SOC to investigate and respond to the alerts it generates.

The Role of SIEM Solution in SOC

The SIEM solution plays a pivotal role within a SOC by serving as the central nervous system of the cybersecurity infrastructure. SIEM solutions collect and aggregate data from various sources across an organization’s network, such as logs, events, and alerts from firewalls, intrusion detection systems, antivirus software, and more. This massive dataset is then normalized, correlated, and analyzed to detect potential security incidents and threats. SIEMs use predefined rules, threat intelligence feeds, and machine learning algorithms to identify patterns and anomalies that might indicate a security breach.

In addition to real-time threat detection, SIEMs provide SOC analysts with the contextual information they need to investigate and respond to incidents effectively. They offer detailed logs and event histories, enabling analysts to trace the timeline and scope of an attack, understand the attack vectors, and determine the impact on the organization’s systems and data. SIEMs also facilitate compliance reporting by generating audit trails and reports, helping organizations meet regulatory requirements. Overall, the SIEM solution acts as a force multiplier for SOC teams, automating the initial stages of threat detection and investigation and allowing analysts to focus their expertise on the most critical security incidents.

Where do SOC and SIEM Meet?

The intersection of a SOC and a SIEM system is a critical point in an organization’s cybersecurity strategy. This meeting point represents the synergy between human expertise and technology automation. SOC analysts rely heavily on SIEM as a central tool for monitoring and investigating security events. SIEM solutions ingest vast amounts of data from various sources, including firewalls, antivirus systems, intrusion detection systems, and more. The SIEM then analyzes this data in real-time, using predefined rules and algorithms to identify potential security incidents. When suspicious or anomalous patterns are detected, the SIEM generates alerts, which are promptly triaged by SOC analysts.

The SOC is where these alerts from the SIEM are thoroughly investigated and acted upon. SOC analysts play a pivotal role in reviewing the alerts, determining their validity, and assessing the severity of potential security incidents. They leverage their knowledge of the organization’s infrastructure and threat landscape to make informed decisions about incident response. If an alert is confirmed as a security incident, SOC analysts take the necessary steps to contain and mitigate the threat, such as isolating affected systems, remediating vulnerabilities, and communicating with relevant stakeholders. This collaboration between the SIEM and the SOC ensures that security incidents are not only detected but also effectively managed and remediated, minimizing the impact of cyber threats on the organization.

Why Excess Data Poses Issues for SOC Efficiency

Excess data poses significant challenges to the efficiency of SOCs for several reasons. First and foremost, a deluge of data can overwhelm SOC analysts, leading to alert fatigue. When a SIEM system ingests an excessive amount of data from numerous sources, it generates a correspondingly high volume of alerts, many of which may be false positives or low-priority events. Sorting through these alerts can be time-consuming, diverting analysts’ attention away from more critical tasks and potentially causing them to overlook genuine threats. This can result in delayed or missed incident responses, jeopardizing the organization’s security posture.

Moreover, excess data can strain the SOC’s infrastructure, leading to performance issues. The increased volume of data ingestion requires additional storage, processing power, and network bandwidth, which can lead to SIEM system bottlenecks and slowdowns. These technical challenges not only hamper the SOC’s ability to analyze and respond to security incidents in real-time but can also result in system instability and downtime. To address these issues, SOC teams must implement robust data management practices, such as data normalization, filtering, and prioritization, to ensure that analysts are presented with actionable and relevant information, reducing the impact of excess data on SOC efficiency. Additionally, organizations should consider scalability planning and investment in advanced analytics and automation to handle growing data volumes effectively while maintaining SOC operational efficiency.

The traditional school of thought has been that SOC teams should not try to feed the SIEM every log and data source from all the business infrastructure. There have been two reasons for this:

  1. The more data you feed into your SIEM, the more alerts you create leading to an increased number of false positives.
  2. The cost of your SIEM dramatically increases over time, often unpredictably. This is because most SIEMs charge based on the amount of data ingested and collected. This equates to customers getting penalized the more they want to protect their organization. Adding additional security analytics such as UEBA or NTA simply exacerbates the problem with more alerts.

The result is: security teams suffer serious burnout, not to mention the burying of a real attack campaign potentially getting missed altogether.

How This Impacts Security Teams

More data for the SIEM can have both positive and negative impacts on SOC security teams. On the positive side, increased data ingestion can enhance the SIEM’s ability to detect a wider range of security threats and provide a more comprehensive view of an organization’s security posture. It allows for the inclusion of additional data sources, such as cloud services, IoT devices, and endpoints, which are becoming increasingly important as technology ecosystems evolve. This expanded data set provides SOC analysts with more context and visibility into potential security incidents, enabling them to make more informed decisions about the nature and severity of threats. Furthermore, a richer data set can support more sophisticated analytics and machine learning algorithms, improving the SIEM’s ability to identify subtle and complex attack patterns. However, the downside is that an influx of data can overwhelm SOC teams if not managed effectively.

On the flip side, the influx of data can strain SOC security teams by generating a higher volume of alerts and information to process. This can lead to alert fatigue, where analysts are inundated with a high volume of alerts, many of which may be false positives or less critical in nature. As a result, SOC analysts may struggle to distinguish between noise and genuine threats, potentially leading to missed or delayed responses to critical security incidents. To mitigate this, SOC teams must implement effective data filtering, prioritization, and correlation mechanisms within the SIEM to reduce the noise and ensure that analysts are focused on the most relevant and high-impact security events. Additionally, the increased workload stemming from more data requires organizations to invest in adequate staffing, training, and automation to maintain SOC efficiency and effectiveness in the face of growing data volumes.

The limitations of current SIEM solutions have drastically inhibited security teams from gaining the visibility necessary to identify and respond to an attack before it can impact an organization. Beyond penalizing customers with higher licensing fees for more data ingestion, current solutions have proven inadequate in terms of handling the capacity of data, leveraging it for security purposes (i.e., preventing breaches) and improving overall security operations.

Rethinking Data Impact on SOC Operations

Security teams need to stop being limited by the very vendors that are pitching visibility and detection. Further, these vendors charge exceedingly for architecture and deployment services, more data, and more parsers.

Gurucul Next-Gen SIEM offers a solution that automatically ingests and interprets any data source, works in complex hybrid-cloud and geographically dispersed locations, scales predictably, and lowers your overall operational and storage costs.

A SIEM solution that offers automatic ingestion and interpretation of any data source provides organizations with a powerful advantage in the ever-evolving cybersecurity landscape. Such flexibility enables security teams to seamlessly adapt to new technologies and data formats without the need for manual configuration, saving precious time and resources. This adaptability is particularly valuable in complex hybrid-cloud environments and geographically dispersed locations, where data sources may be diverse and continually changing. By effortlessly accommodating data from across the organization’s infrastructure, the SIEM ensures comprehensive visibility into potential security threats, regardless of where they originate. This capability enables security teams to stay ahead of emerging threats and respond swiftly to incidents, reducing the risk of data breaches and operational disruptions.

Moreover, the ability of the SIEM to scale predictably is essential for organizations facing growing data volumes and expanding digital footprints. As businesses evolve, their security requirements change as well, and a scalable SIEM solution can effortlessly handle increasing data loads without compromising performance. This scalability ensures that the SIEM remains effective and responsive, even in high-demand situations, and prevents the need for costly infrastructure upgrades or replacements. Additionally, by lowering overall operational and storage costs, organizations can allocate resources more efficiently, freeing up budget and manpower for other critical security initiatives. In essence, a versatile and scalable SIEM solution that reduces operational costs while maintaining robust security capabilities is a valuable asset for organizations seeking to stay secure and agile in today’s dynamic threat landscape.

Improving Data Ingestion with Gurucul Next-Gen SIEM

To learn more about our Next-Gen SIEM and how we have innovated to flip the narrative on current SIEMs, check out our white paper, “Improving Data Ingestion While Decreasing Complexity and Cost.”

Improving Data Ingestion While Decreasing Complexity and Cost


In this whitepaper we discuss:

  • The challenges security teams have in pulling in more data into their SIEM
  • The lack of visibility that ensues, especially when migrating to the cloud
  • How to change the game and get more data that improves the entire SOC lifecycle
  • How you can actually lower your overall operational and storage costs

You can also contact us to discuss your needs and how we can help.


About The Author

Sanjay RajaSanjay Raja, VP Product Marketing and Solutions, Gurucul

Sanjay brings over 20 years of experience in building, marketing and selling cyber security and networking solutions to enterprises, medium-to-small business, and managed service providers. Previously, Sanjay was VP of Marketing at Prevailion, a cyber intelligence startup. Sanjay has also several successful leadership roles in Marketing, Product Strategy, Alliances and Engineering at Digital Defense (acquired by Help Systems), Lumeta (acquired by Firemon), RSA (Netwitness), Cisco Systems, HP Enterprise Security, Crossbeam Systems, Arbor Networks, Top Layer Networks, Caw Networks (acquired by Spirent Communications), Nexsi Systems, 3Com, and Cabletron Systems. Sanjay holds a B.S.EE and an MBA from Worcester Polytechnic Institute. Sanjay is also a CISSP as well as Pragmatic Marketing certified.

Frequently Asked Questions

How does automation impact SIEM data ingestion and SOC efficiency?

Automation has a profound impact on SIEM data ingestion and SOC efficiency. Through automated processes, SIEM solutions can collect, parse, and normalize vast volumes of security data from diverse sources more swiftly and accurately than manual methods, reducing the risk of human error and alert fatigue. Moreover, automation enables the immediate correlation of events, aiding in the rapid identification of threats and vulnerabilities, while also allowing for the execution of predefined response actions. This, in turn, streamlines SOC workflows by prioritizing incidents and reducing the workload on security analysts, who can focus on more complex and strategic tasks. Ultimately, the integration of automation enhances the overall effectiveness and responsiveness of security operations, enabling organizations to better defend against cyber threats in an increasingly dynamic threat landscape.

Are there any security considerations related to SIEM data ingestion?

Indeed, there are critical security considerations tied to SIEM data ingestion. The very process of collecting and aggregating vast quantities of sensitive security data from diverse sources poses inherent risks. First and foremost, the security of data transmission channels must be safeguarded to prevent interception or tampering. Additionally, the proper authentication and authorization mechanisms must be in place to ensure that only authorized personnel can access and manipulate the data. Furthermore, the SIEM system itself must be fortified against vulnerabilities and adequately monitored to prevent it from becoming a point of compromise.

How does the scalability of a SIEM solution impact data ingestion?

The scalability of a SIEM solution has a profound impact on data ingestion. As an organization’s infrastructure and data sources grow, a scalable SIEM can seamlessly accommodate increased data volumes without compromising performance or accuracy. It allows for the efficient addition of new data sources and the ability to handle a higher frequency of events, ensuring that security analysts can effectively monitor and analyze the expanding attack surface. Scalability is vital in avoiding bottlenecks that could hinder the timely processing of security data, which is essential for real-time threat detection and response. Furthermore, a scalable SIEM can adapt to evolving business needs, making it a valuable asset for organizations experiencing growth or those with fluctuating data ingestion requirements.

What are some best practices for optimizing SIEM data ingestion?

Optimizing SIEM data ingestion involves several best practices. Firstly, carefully assess and prioritize data sources to ensure that you’re collecting the most relevant and critical information for your security objectives, avoiding unnecessary data overload. Employ data parsing and normalization techniques to streamline the data ingestion process and facilitate effective analysis. Implement strong access controls and auditing mechanisms to safeguard against unauthorized configuration changes and maintain data integrity. Utilize compression and encryption for data transmission to protect sensitive information in transit. Regularly review and fine-tune data ingestion rules to adapt to evolving security requirements and reduce false positives. Lastly, consider leveraging automation to handle routine data ingestion tasks, freeing up security analysts for more strategic activities. These practices collectively enhance the efficiency and accuracy of SIEM data ingestion, helping organizations better detect and respond to security threats.