Gaps in the SIEM Market: Opportunities for Future Founders

Author: Rahul Marri

Security Information and Event Management (SIEM) solutions are one of the key pillars of modern cybersecurity frameworks, enabling organizations to detect and respond to threats in real-time. The SIEM market is estimated at $5.7 Billion with a 13% annual growth rate in the year 2023.

However, despite their critical role, SIEM tools aren’t perfect. There are significant gaps in the market that founders can address, particularly by using Artificial intelligence (AI) and Machine Learning (ML). 

In this blog post, we’ll explore the current limitations of SIEM tools, and discuss future opportunities to address them. 

Identifying Existing Gaps: 

1. Data Processing & Scalability: 

Traditional SIEM systems often struggle to ingest, process, and analyze the vast amounts of data generated by modern IT Infrastructures. 56% of organizations report coverage gaps due to legacy SIEM solutions that cannot scale effectively with enterprise networks. 

SIEMs often face performance degradation when handling large datasets, resulting in slow data processing, missed alerts, and delayed response times. Moreover, scaling these SIEM systems to accommodate growing infrastructures requires significant investment in hardware and experts which would be impractical for many organizations. 

2. High Costs & Vendor Lock-in: 

SIEMs often come with significant upfront and ongoing costs, including licensing, storage, and resource allocation for managing large volumes of data. Typical SIEM software costs can range from $20,000 to $1 Million, depending on the capabilities and features required. These expenses can be a major burden for smaller organizations. A survey found that over two-thirds of tech decision makers view SIEM pricing as a significant issue. 

Many SIEM solutions are proprietary, making it difficult to switch providers or integrate with other tools. This can limit flexibility, forcing organizations to remain dependent on a single vendor’s ecosystem, even when their needs or vendor’s service change over time. 

3. Advance Analytics Integration:

A major limitation of traditional SIEMs is their lack of advanced analysis. These systems rely heavily on pre-defined rules or signature-based detection. This process fails over complex and multi-stage attacks. They lack proper integration with external threat intelligence feeds, like not staying updated with the latest global threat information. The CardinalOps 2023 Report on State of SIEM Detection Risk showed that SIEMs can only detect 24% of the techniques listed in the MITRE ATT&CK framework. 

MOVEit, a Managed File Transfer (MFT) application was exploited by the CL0P ransomware gang, affecting 77 Million individuals and over 2600 companies globally. These breaches required organizations to update their SIEM solutions manually and adjust detection rules to identify threats accurately. 

4. Inadequate support for IoT, OT and Ephemeral Resources: 

Traditional SIEMs often lack adequate visibility and control over emerging technologies like IoT devices, OT systems, and ephemeral resources such as containers and serverless functions. With IoT devices projected to reach 29 billion by 2030 (IDC), and 62% of organizations experiencing OT security incidents (SANS Institute, 2022), these areas represent significant attack vectors that require better monitoring.

Additionally, 76% of enterprises use containers in production but 70% lack sufficient visibility into these environments making traditional SIEMs inadequate for detecting threats in these modern architectures. Without proper support for these technologies, organizations face security blind spots that can lead to undetected breaches.

5. False Positives & Automated Responses:

A persistent issue with SIEMs is their high rate of false positives. Security teams often contend with an overwhelming number of alerts, many of which are false or low-priority. This can lead to “alert fatigue” where important alerts are missed or ignored, increasing the risk of real threats going undetected. According to IDC, 83% of cybersecurity employers report struggling with the overwhelming volume of alerts, and about 30% of alerts go ignored or uninvestigated due to this fatigue.   

Furthermore, these tools lack sophisticated automation features for incident response. It forces security teams to manually investigate and mitigate threats, which is time-consuming and prolongs the duration of an attack.

6. Complexity in Maintenance & Compliance: 

Maintaining and optimizing traditional SIEM solutions is a highly specialized task that requires a deep understanding of both the underlying technology and the organization's unique security needs. SIEMs require constant fine-tuning to ensure effective threat detection, as new security policies, data sources, and detection rules are continually added. This complexity makes it difficult for smaller organizations, which may lack the budget and expertise, to deploy and manage SIEM solutions effectively. 

Moreover, compliance reporting with traditional SIEMs is often a manual, time-consuming process. Generating reports for regulatory standards like GDPR, HIPAA, or PCI-DSS even with pre-built templates require significant customization to tailor reports to specific organizational needs and often requires manual intervention. Maintaining compliance with evolving regulations requires ongoing updates to the SIEM.

Bridging the Gaps:

1. Develop Next-Gen SIEM Solutions:

Develop a next-gen SIEM solution built on top of a data lake architecture. This approach offers scalability, flexibility, and cost-effective storage with core functionalities. It would be helpful for long-term data retention without the incurring costs associated with SIEMs. Also, leveraging distributed data processing frameworks like Apache Kafka or serverless computing can ensure seamless scalability. 

2. Incorporate Machine Learning and AI: 

Integrating AI and ML into SIEM solutions can significantly enhance threat detection and reduce false positives. Machine learning algorithms can analyze patterns and anomalies in large datasets to identify potential threats that traditional rule-based systems might miss.

• Anomaly Detection: Unsupervised learning techniques can detect deviations from normal behavior, identifying potential threats without prior knowledge of specific attack signatures.

• Predictive Analysis: Supervised learning models trained on historical data can predict and identify known attack patterns, improving detection rates.

• Automated Response: AI-driven systems can prioritize alerts based on severity and context, automating initial response actions and allowing security teams to focus on critical issues.

  
  

3. Purpose-Built SIEMs for IoT, OT and Modern Architectures:

IoT and OT Integration: SIEMs should support the unique protocols and security needs of IoT and OT devices, which are common in industries like healthcare, manufacturing, and energy. This enables effective monitoring and anomaly detection, protecting critical infrastructure from threats.

Adaptive Monitoring for Ephemeral and Edge Architectures: SIEMs should dynamically monitor ephemeral resources—such as containers and serverless functions—and edge computing environments where data processing happens near the data source. By capturing security events in real time at the source, these SIEMs ensure continuous, low-latency visibility over evolving architectures, strengthening security across distributed systems through centralized insights.

4. User-Friendly Platforms with Automated Compliance Reporting:

Simplifying the user experience is crucial for wider adoption. SIEM platforms should feature:

• Intuitive Interfaces: User-friendly dashboards and visualization tools that make it easier for security teams to interpret data.

• Automated Compliance Reporting: Built-in templates for various regulatory standards that are automatically updated to reflect changes in regulations.

• Customization Options: Allow users to tailor the SIEM to their specific needs without requiring extensive technical expertise. 

  
  

Final Thoughts

In a world where cybersecurity threats are constantly evolving, it’s clear that there’s a pressing need for smarter, more adaptable SIEM solutions. The gaps we’ve discussed above present a unique opportunity for founders and developers to create next-generation SIEMs that truly meet the needs of organizations. Imagine building an SIEM that genuinely understands the nuances of today’s diverse environments—one that not only keeps pace with cloud technologies but also integrates seamlessly with existing security tools.

By leveraging advancements like AI and machine learning, there’s a real chance to enhance threat detection and streamline responses, making life easier for security professionals everywhere. If you’re thinking of diving into the cybersecurity arena, remember: the future is wide open. Let’s create solutions that not only protect businesses but also inspire trust in our digital world. Together, we can pave the way for a safer online experience.