The importance of comprehensive SIEM data collection

The effective implementation of a security information and event management (SIEM) tool heavily relies on robust data collection.

By understanding the importance of data collection to the effectiveness of a SIEM, MSPs can harness the power of their security event data to detect and respond to threats more effectively, deliver incident response, and ensure compliance with regulatory requirements.

In this blog post, we’ll explore what makes a good SIEM and why holistic data collection plays such a pivotal role in fortifying your security posture.

What is a SIEM?

A SIEM is a software solution that provides organizations with a centralized platform for collecting, analyzing, and correlating security event data from various sources across their IT infrastructure. In other words, it helps turn security event data into actionable insights, helping reduce attack surfaces and respond to incidents and other exposures.

What makes a good SIEM?

There are several factors that contribute to a good SIEM.

• Data collection and log management: SIEM solutions collect log data  from different sources, including endpoints or IoT devices, servers, applications, network devices, SaaS applications, cloud environments, and additional security solutions.
  Related content: SIEM logging: A comprehensive guide for MSPs

• Event correlation: SIEM systems analyze and correlate log data to identify patterns, anomalies, and potential security incidents across the data collected.

• Real-time threat detection: SIEM tools provide real-time monitoring capabilities, allowing security teams to detect and respond to threats promptly.

• Threat intelligence integration: Integration with threat intelligence feeds for enhanced threat detection and alert context.
• Alert management: Centralized alert management with prioritization and escalation capabilities.
• Compliance management: SIEM systems help organizations meet regulatory compliance requirements by monitoring systems in real-time, generating reports, and providing audit trails.

Download the SIEM Buyer’s Guide for more insights into what to look for in a SIEM.

Common SIEM data collection sources

Collecting data from various sources is the foundation for comprehensive security monitoring and analysis. Here are some common data sources for a SIEM:

• Logs from IT infrastructure: Collecting logs from servers, workstations, firewalls, routers, switches, and other network devices provides valuable insights into system activities, user behavior, network traffic, and potential security incidents.
  Related content: Learn more about SIEM vs log management
• Security devices: SIEM can collect data from security devices such as intrusion prevention systems (IPS), firewalls and UTMs, application firewalls (WAF), endpoint protection and response platforms (EPP/EDR), data leak prevention (DLP), and many more. This data helps detect and respond to security threats and vulnerabilities.
• Applications and databases: Collecting logs and events from critical applications and databases allows organizations to monitor access, detect unauthorized activities, and identify potential vulnerabilities or breaches. Examples include logs from web servers, database servers, email servers, and enterprise resource planning (ERP) systems.
• Network traffic data: Capturing and analyzing network traffic data using technologies such as network taps, port mirroring, or network packet capture tools provides visibility into network communications, identifying anomalies and potential malicious activities. This includes monitoring services, endpoints, and traffic patterns.
• User activity and identity data: Collecting user activity logs—including login/logout events, privilege changes, and file access logs—helps monitor user behavior, detect insider threats, and identify unauthorized access attempts.
• Threat intelligence feeds: Integrating threat intelligence feeds into SIEM enriches the collected data by providing real-time information about known malicious IP addresses, domains, malware signatures, and other indicators of compromise (IOCs). This helps in proactive threat detection and significantly speeds up incident response.
• Cloud services and APIs: With the increasing adoption of cloud services, it is essential to collect logs and events from cloud platforms, such as infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and software-as-a-service (SaaS). Additionally, integrating with cloud service provider APIs allows organizations to gather relevant security-related data.
• Physical security systems: In some cases, SIEM can also integrate with physical security systems such as video surveillance, access control systems, and alarm systems. This integration enables organizations to correlate physical security events with digital security events, providing a holistic view of potential threats.

Understanding the SIEM process for data collection

Here is a simplified explanation of the SIEM data collection process:

• Data sources identification: The first step is to identify the relevant data sources that need to be monitored for security events. These sources can include network devices, servers, endpoints, firewalls, intrusion detection systems, antivirus software, and more.
• Log collection: Once the data sources are identified, the SIEM system collects logs or event data generated by these sources. Logs contain valuable information about activities, events, and potential security incidents.
• Normalization and parsing: In this step, the collected logs are normalized and parsed to extract relevant information. Different log formats and structures are standardized to ensure consistency and ease of analysis.
• Log aggregation: The collected logs are then aggregated into a centralized repository or log storage. This allows for easy access, correlation, and analysis of the data.
• Threat intelligence integration: SIEM systems often integrate with external threat intelligence feeds to enhance the analysis process. This integration provides additional context and helps identify known malicious IP addresses, domains, or other indicators of threats.
• Event correlation: The SIEM system correlates events from different data sources to identify patterns, anomalies, and potential security incidents. Correlation rules and algorithms are applied to detect suspicious activities or indicators of compromise.
• Alert generation: When the SIEM system detects a security event or anomaly that matches predefined rules or thresholds, it generates alerts. These alerts notify security analysts or administrators about potential security incidents that require investigation.
• Incident response: Upon receiving alerts, security teams investigate the identified security incidents. They analyze the collected data, perform forensic analysis, and take appropriate actions to mitigate the threats and prevent further damage.
• Reporting and compliance: SIEM systems generate reports and provide compliance monitoring capabilities. These reports help organizations meet regulatory requirements, track security incidents, and provide insights for improving security posture.
• Continuous monitoring and improvement: The SIEM process is an ongoing effort. It involves continuous monitoring, fine-tuning of correlation rules, updating threat intelligence, and improving the overall effectiveness of the data collection and analysis process.

The hidden dangers: 11 risks of partial data collection on SIEM performance

Some SIEM vendors are advocating for partial data collection, which lowers the purchase price but could have costly negative repercussions in the long run. It is important to carefully consider the potential risks to your organization’s cybersecurity posture and incident detection capabilities. Let’s explore a few of the related risks:

1. Incomplete threat detection: The SIEM might miss critical security events if relevant data sources are not included.
   Example: If the vendor decides to drop logs from IoT devices, an attack targeting these devices could go undetected.

2. Compliance violations: Failure to collect logs required by regulatory standards can lead to non-compliance and potential fines.
   Example: Standards such as PCI-DSS require logs from all cardholder data environments to be collected and reviewed. Omitting logs from a particular data source or dropping certain logs could result in non-compliance and potential fines.

3. Reduced incident response effectiveness: Incomplete log data can hinder your ability to conduct thorough incident investigations.
   Example: During a security breach, missing logs from critical systems could prevent security teams from understanding the root cause of an attack, leading to delayed or ineffective response and giving attackers more time to cause further damage.

4. Limited event correlation: By limiting data collection, the SIEM’s ability to accurately correlate events is compromised, resulting in missed opportunities to detect coordinated attacks.
   Example: An attacker might exploit a vulnerability in a web application, move laterally to a database server, and exfiltrate data. If logs from the web application, network devices, and database server are not all collected, the SIEM might miss the correlation between these events, failing to detect the APT.

5. Lack of operational visibility: The organization may not have a comprehensive view of its security posture.
   Example: If logs from certain network segments are dropped, the organization might miss important security trends, patterns, and potential areas of improvement.

6. Increased false positives/negatives: Incomplete data can lead to an increase in false positives or negatives, affecting the accuracy of alerts.
   Example: Without contextual data from all sources, the SIEM might generate false alarms or fail to detect genuine threats.

7. Forensic analysis challenges: Incomplete log data can make forensic analysis difficult, if not impossible.
   Example: After a data breach, forensic analysts need logs from all relevant systems to reconstruct the attack timeline and gather evidence for legal proceedings. Missing logs could hinder the investigation and weaken the legal case.

8. Lack of customization: The organization may not be able to tailor data collection to its specific needs and environment.
   Example: Unique or proprietary systems within the organization might not be adequately covered if the vendor’s data collection policies are too generic.

10. Potential for data loss: Important log data could be inadvertently dropped or lost.
    Example: If the vendor’s data retention policies are not aligned with the organization’s needs, critical logs might be purged too early.

11. Vendor bias and limitations: The SIEM vendor’s decisions might be influenced by their own biases, limitations, or business interests.
    Example: A SIEM vendor might prioritize data sources that align with their partnerships or integrated solutions rather than what is best for the organization’s security.
12. Dependency on vendor expertise: The organization becomes heavily dependent on the vendor's expertise and decisions.
    Example: If the vendor lacks a deep understanding of the organization’s specific threats and risks, their data collection decisions might not be optimal.

To mitigate these risks, organizations should take an active role in determining what data to collect based on their specific security requirements, compliance obligations, and industry best practices. You should not rely on a SIEM vendor to determine what data is collected. 

Conclusion

The importance of comprehensive data collection in a SIEM system cannot be overstated. While there may be new entrants in the market advocating for a more selective, cost-effective approach, organizations must carefully evaluate the risks associated with partial data collection.

By prioritizing comprehensive data collection, organizations can gain valuable insights, detect emerging threats, meet compliance obligations, and proactively protect their systems and data. Remember, in the ever-evolving landscape of cybersecurity, knowledge is power, and comprehensive data collection is the foundation upon which effective security strategies are built.

ConnectWise SIEM™ proudly offers comprehensive data collection and analysis designed for MSPs to scale attack detection and response. We also provide a co-managed option for 24/7 managed detection and response if desired.