SIEM (Security Information and Event Management)

Definition of SIEM

Security Information and Event Management (SIEM) is a cybersecurity technology that aggregates and analyzes log data from various sources within an organization's IT infrastructure, helping to identify, monitor, and respond to potential security incidents.

The analyst firm Gartner coined the term “SIEM” — which stands for Security Information and Event Management — in 2005. It combined the concepts of security information management (SIM) and security event management (SEM) to describe a more effective way to enhance cybersecurity posture.

SIEM gathers machine-generated data from every device, application, and endpoint in the network ecosystem. It provides a centralized interface that enables security teams to visualize their entire environment, easily identify threats, and quickly respond to incidents.

The Business Case for SIEM

Today’s organizations at all levels are drowning in a tsunami of machine-generated data. They are also fighting endlessly expanding cyber threats, increasingly strict regulatory compliance requirements, and an always critical need to protect sensitive data. This level of complexity requires a centralized platform that can handle monster data volumes and provide continuous monitoring, real-time alerting, and in-depth analysis of security events. In short, they need a SIEM.

A robust SIEM can reduce the risk of data breaches, ensure compliance with industry regulations, and minimize the business impact of security incidents. It can also optimize the efficiency of security operations by reducing the burden on IT teams and enabling faster incident detection and response.

Key Functions of a SIEM

The main functions of a SIEM include:

Data collection: SIEMs collect log and event data from servers, firewalls, intrusion detection systems (IDS), endpoint security solutions, and more. This data may be structured (like log files) or unstructured (like raw data from network traffic). They store this data in a centralized repository.
Normalization: Once collected, data is normalized, enabling SIEM systems to apply consistent analysis and correlation rules across different data types and sources.
Correlation engine: The correlation engine lies at the heart of a SIEM. It scours normalized data for patterns, anomalies, or relationships that may indicate a security incident. For example, a correlation rule might detect multiple failed login attempts followed by a successful login from the same IP address and flag it as a potential brute force attack.
Alerting and reporting: SIEMs generate alerts to notify security analysts when they detect potential threats. These alerts can be prioritized based on severity, helping teams focus on the most critical issues. SIEMs also provide reporting capabilities for compliance audits and executive management.
Dashboards and visualization: SIEM systems provide real-time dashboards to help security teams quickly understand the nature and scope of incidents at a glance.
Incident response: SIEM solutions often integrate with incident response tools, allowing security teams to investigate and respond to threats directly from the SIEM interface. Some advanced SIEM systems include automated response capabilities to contain threats quickly.

Why SIEM is Critical for Cybersecurity Teams

SIEM is crucial in cybersecurity for several reasons:

Centralized monitoring: SIEM provides a single pane of glass for monitoring security events across an organization's entire network, making it easier to detect and respond to threats.
Real-time threat detection: SIEM can identify potential security incidents as they happen, reducing the time to detect and respond to attacks.
Improved incident response: SIEM systems enable security teams to quickly investigate and contain threats, minimizing the impact of security incidents.
Regulatory compliance: SIEM helps organizations meet compliance requirements by providing audit trails, log management, and reporting capabilities. This is particularly important for industries subject to strict regulations, such as finance and healthcare.
Data breach prevention: By detecting threats early and providing actionable insights, SIEM reduces the risk of data breaches and protects sensitive information from unauthorized access.

SIEM in the Wild: Five Real-World Examples

Below are five common SIEM use cases:

Detecting insider threats: SIEM can help detect potential insider threats by identifying unusual user behavior — such as accessing sensitive files without authorization or transferring large amounts of data outside the organization — and correlating this activity with access controls.
Identifying advanced persistent threats (APTs): APTs often involve sophisticated, prolonged attacks that evade traditional security measures. SIEM systems analyze multiple data points, such as failed logins, access from unusual locations, and abnormal network traffic patterns, to detect signs of APTs.
Monitoring cloud environments: As organizations move to the cloud, SIEM provides visibility into cloud-based resources, applications, and services. By collecting and analyzing log data from cloud providers (e.g., AWS, Azure, etc.), SIEM helps detect misconfigurations, unauthorized access, and data exfiltration.
Compliance and audit reporting: Financial institutions use SIEM to comply with regulations like PCI-DSS and GDPR. SIEM systems provide automated reporting and audit trails, ensuring organizations meet regulatory requirements and demonstrate due diligence.
Incident response and forensics: If a security breach occurs, SIEM systems can provide a detailed record of events leading up to and during the incident. These reports are critical in forensic analysis, identifying the root cause of the breach, and implementing measures to prevent future occurrences.

Major Challenges with SIEMs

While a great addition to a cybersecurity stack, SIEMs aren’t trouble-free. Depending on the solution you're using, you may run into some of the issues below:

Cost: Most SIEM solutions are expensive to purchase, deploy, and maintain, especially if the pricing is based on data volume or events per second. Hiring senior personnel to administer the SIEM may add to the expense.
Data ingestion: SIEMs need to process massive amounts of data from various sources, such as firewalls, endpoints, and network devices. Managing the high volume, variety, and velocity of data can be complex, especially ensuring real-time ingestion without overwhelming the system. For many organizations, this can be multiple millions of events per second.
Implementation: Deploying a SIEM can be time-consuming and complex since it requires integration with a wide range of security tools and infrastructure. Customizing it for your environment is another challenge that may require an advanced skill set.
Detection tuning: After deployment, you’ll need to continuously “tune” your SIEM to ensure that it effectively detects threats while minimizing noise. Proper tuning involves adjusting rules, filters, and thresholds to reduce false positives and improve detection accuracy. AI attacks have compounded the difficulty factor here, as well.
False positive alerts: SIEMs can generate an overwhelming number of alerts, many of which may be false positives. This can translate into alert fatigue (and may even cause analysts to overlook real threats). There is also the work involved in separating signal from noise, which can take 20 minutes per event in the hands of a skilled analyst, and they are often tasked with thousands of events per day.
Storage challenges: There’s a delicate balancing act between data storage volume, speed of searching that data, and cost. Organizations need to store enough data to both satisfy compliance requirements and to allow robust searches during analysis. Analysts also need to be able to search quickly to stop threats in their tracks. However, meeting all these requirements is an expensive proposition. “Hot” data storage, which keeps data on high-performance storage devices that analysts can search quickly, is expensive. “Cold” storage is less expensive but slower to search. In sum, it’s complicated.
Correlation: SIEMs must correlate events from multiple sources in near real-time to identify potential threats. This requires advanced analytics and can be thwarted by incomplete or inconsistently formatted data. Having data gathered from a wide range of systems and devices can also cause problems

Anomali and SIEM

SIEM is a critical component of modern cybersecurity strategies, providing centralized monitoring, real-time threat detection, and incident response capabilities. Anomali’s Security Operations Platform integrates the core functionalities of SIEM, TIPs, SOAR, and UEBA into a single easy-to-use platform that enhances the effectiveness of security operations, improves threat detection, decreases response times, and simplifies compliance with regulatory requirements.

Interested in learning more? Request a demo today to see how Anomali combines the power of SIEM with generative AI to enhance security posture, reduce the risk of cyber incidents, and remediate threats in one integrated platform.