Understanding SIEM and XDR: A Comprehensive Comparison for Security Operations Centers

Cybersecurity teams, regardless of the size of their business or the industry they work in, face a relentless and increasing barrage of threats. Security information and event management (SIEM) solutions have long been the go-to for consolidating security events, analyzing data, and generating alerts. However, as cyberattacks become more subtle and sophisticated, a new solution — extended detection and response (XDR) — has emerged, offering a more integrated, intelligent approach to threat detection and response.  

SIEM vs. XDR: The Business Perspective

• SIEMs enable organizations to collect and centralize security event data from across its IT infrastructure. SIEMs aggregate logs, alerts, and event information from various sources (like firewalls, endpoint protection, intrusion detection systems, and so on) and apply rule-based correlation (such as automatically blocking suspicious IPs) to detect and stop potential threats. SIEMs are complex, powerful tools for managing compliance and offering visibility across the IT ecosystem. On the flip side, they often require extensive customization and a dedicated (and experienced) security team to operate efficiently. ‍
• XDR builds on the idea of SIEM by offering a more comprehensive approach to security. It integrates detection and response across multiple layers — such as endpoints, networks, servers, and cloud environments — and leverages AI and machine learning to deliver more context-aware threat detection. XDR reduces complexity by correlating alerts and automating responses, making it easier for security teams to manage incidents.

Key business differences:

• SIEMs provide wide visibility but typically need significant manual effort by experts to configure and maintain. It is ideal for larger enterprises with SOC teams capable of handling complex event correlations and incident response workflows. ‍
• XDR offers integrated, intelligent security out of the box with better automation and faster time to value, making it ideal for businesses that want streamlined, high-fidelity threat detection and automated responses without excessive manual overhead.

Technical-Level Summary: SIEM vs. XDR

• SIEM solutions ingest and normalize data across the network and from multiple security tools, including logs from firewalls, endpoints, applications, and user activity. They rely on predefined rules and correlation engines to identify patterns that may indicate security incidents. SIEMs are designed to offer a high degree of customization for advanced threat detection. However, this flexibility often comes with a steep learning curve. ‍
• XDR takes a more integrated, centralized approach. It collects and correlates data across endpoints, networks, and cloud environments into a single solution, reducing the number of standalone security tools required. XDR incorporates AI and machine learning algorithms to automate the detection of complex, multi-vector threats and enables faster response by correlating security events across multiple domains in real time.

Key technical differences:

• SIEMs normally require integration with external tools to achieve a holistic view of the network and generally rely on signature-based detection that is based on known patterns ‍
• XDR natively integrates detection and response across various layers and uses behavioral analytics powered by AI and machine learning to detect anomalies and unknown threats.

SOC Use Cases for SIEM and XDR

Below are four common SOC use cases that illustrate how SIEM and XDR address the same problems in different ways.

Use Case 1: Threat Detection and Response Across Multiple Environments

Technical explanations:

• SIEMs aggregate logs from disparate environments (such as endpoints, firewalls, and cloud infrastructure) and normalize and enrich the data into a centralized platform. To detect threats, SOC teams manually create or customize predefined rules or queries, often with the assistance of a human analyst. The SIEM flags anomalies based on these rules and generates alerts, which can then be investigated. However, the lack of automation can lead to delayed detection and response, especially in high-volume alert environments. ‍
• XDR natively integrates data from various environments (endpoints, networks, clouds) and correlates threat signals across these domains using AI-powered analytics. This unified approach enables XDR to detect advanced threats in real time without requiring SOC teams to write custom correlation rules. When it detects a potential threat, XDR can automatically trigger response workflows, such as isolating compromised devices or blocking malicious IP addresses.

Business impact:

• SIEMs provide strong visibility but require significant manual intervention. SOC teams need to invest time and expertise to build and maintain detection rules across multiple environments. This increases operational costs and can result in missed or delayed detections and alert fatigue. ‍
• XDR delivers faster and more automated threat detection and response. Its automation reduces the operational burden on SOC teams, allowing them to focus on critical incidents. Businesses gain enhanced security without the need to maintain complex detection rules manually.

Use Case 2: Detecting Insider Threats

Technical explanations:

• SIEMs rely on the collection of user activity logs from systems, networks, and applications to detect insider threats. SOC teams typically set up behavioral rules or use user-based analytics modules to track deviations from normal behavior such as improbable travel. However, insider threats are often hard to detect due to the sheer volume of data, making it difficult to pinpoint malicious activity. ‍
• XDR includes advanced user and entity behavior analytics (UEBA) as part of its core functionality. It uses machine learning to baseline user behavior across the organization and can automatically detect anomalies that might indicate insider threats, such as accessing unauthorized data or unusual login activity. XDR’s integrated approach ensures that these anomalies are contextualized with other network or endpoint activity, improving detection accuracy.

Business impact:

• SIEMs can detect insider threats but require substantial customization to be effective. Without AI-driven behavioral analysis, SOC teams may face a high number of false positives, which reduces the overall efficiency of threat-hunting efforts. ‍
• XDR has built-in UEBA capabilities, which offer a more effective and automated solution for detecting insider threats. By continuously learning user behavior and applying AI-driven detection, XDR minimizes false positives and speeds up the identification of legitimate threats.

Use Case 3: Investigating Advanced Persistent Threats (APTs)

Technical explanations:

• SIEMs are valuable in tracking long-term attack patterns associated with APTs by aggregating and analyzing logs over time. To investigate an APT, analysts must often pore through and compare different datasets — such as network traffic, endpoint logs, and firewall alerts — to manually correlate indicators of compromise (IoCs). While SIEMs provide detailed forensic capabilities, the process is labor-intensive and prone to missing subtle threat indicators. ‍
• XDR excels in detecting and responding to APTs by automatically correlating signals across multiple layers — such as endpoints, networks, and email security — using AI. By continuously analyzing behavioral data, XDR can detect APT’s lateral movement or persistence techniques in real time. Additionally, XDR simplifies threat hunting by presenting correlated attack vectors in a single interface, significantly reducing the time it takes to identify and respond to APTs.

Business impact:

• SIEMs can provide a thorough investigation of APTs but require expert security analysts to sift through vast amounts of data and manually link attack patterns. The time-consuming nature of this process can delay response and mitigation. ‍
• XDR’s ability to automatically correlate data across multiple sources allows for faster identification and mitigation of APTs, reducing the potential for damage. This proactive approach reduces costs associated with prolonged investigations and incident response.

Use Case 4: Managing Compliance Requirements

Technical explanations:

• Most SIEM solutions excel at compliance reporting by collecting, storing, and correlating logs across an organization’s systems. SIEMs offer predefined compliance reports (such as PCI-DSS, GDPR, HIPAA) that help organizations demonstrate adherence to regulatory requirements. However, compliance-related investigations may require custom queries and manual validation of security controls. ‍
• XDR also facilitates compliance by offering real-time monitoring and reporting across integrated systems. Its advantage over SIEM is that it can provide deeper insights into security incidents and automate much of the compliance monitoring process. XDR integrates across endpoints, networks, and cloud environments, ensuring that compliance data is collected and correlated seamlessly, reducing the manual effort required.

Business impact:

• SIEMs provide reliable compliance capabilities but require ongoing customization tailored to specific regulatory standards. This adds to operational complexity and costs. ‍
• XDR reduces the manual workload associated with compliance reporting by automating many of the data collection and correlation processes. For businesses, this translates into lower compliance costs and faster audit preparation.

Pros and Cons of SIEM and XDR

SIEM pros:

• Wide visibility: SIEMs offer comprehensive log management, which helps organizations maintain visibility across diverse systems. ‍
• Customizable: SIEMs can be tailored to specific use cases, providing flexibility for organizations with complex security needs. ‍
• Compliance: SIEMs are strong at generating reports for regulatory compliance.

SIEM cons:

• Resource intensive: SIEMs require a dedicated SOC team to manage, maintain, and create custom rules, which increases operational costs. ‍
• Slow response: Detecting and mitigating threats can take longer due to the manual nature of threat correlation and response.

XDR pros:

• Integrated security: XDR provides built-in integrations across endpoints, networks, and cloud environments for streamlined detection and response. ‍
• Automation and AI: XDR leverages AI and machine learning to reduce false positives and automate detection and response workflows. ‍
• Faster time to value: XDR’s pre-built integrations and use of automation enable quicker deployments and more immediate results.

XDR cons:

• Limited flexibility: While comprehensive, XDR solutions may not offer the same level of customization as SIEMs for organizations with highly specific or niche security requirements. ‍
• Vendor lock-In: XDR solutions often require using security tools from the same vendor, which can limit flexibility.

How to Combine SIEM and XDR Capabilities

Both SIEM and XDR play crucial roles in modern security operations, but their suitability depends on an organization’s specific needs. SIEMs provide highly customizable, broad visibility across the enterprise, making them ideal for businesses with established SOCs and the resources to maintain them.  

XDR, on the other hand, offers a more integrated, automated, and intelligent approach to detection and response, making it a better fit for organizations that need faster time-to-value and reduced complexity in managing security incidents.

Choosing between SIEM and XDR ultimately depends on your organization’s security maturity, operational capacity, and need for either flexibility (SIEM) or automation (XDR). At Anomali, we’re focused on combining the best of both solutions into a fully integrated Security and IT Operations Platform that spans from external threat data to internal telemetry and workflows for immediate remediation. Schedule a demo to see how the Anomali Security and IT Operations Platform offers the flexibility of a SIEM with the automation normally associated with XDR, with the added benefits of integrated threat intelligence and leading-edge AI.