Cyberthreats aren’t just the worry of big enterprises. They’ve become increasingly frequent and widespread, with bad actors targeting every size and type of organization. In response, cybersecurity solutions are evolving to become more holistic and sophisticated. It’s not enough to know what to do after a breach or ransomware infection happens. IT teams need to detect attacker activity before the damage can be done. That means having tools to spot behavioral signs within the environment, even when attackers are using sophisticated means to evade detection.
IT teams are adding extended detection and response (XDR) to their security stack for a more integrated approach to threat detection, response, and mitigation. At its core, XDR uses data from multiple sources across an organization’s entire ecosystem to paint a comprehensive picture of potential threats. This can vastly speed detection and reduce time to mitigation. Anyone searching for an XDR solution will find that vendors are taking different approaches based on their underlying technology.
Until now, it’s been hard to find a comprehensive guide for comparing available XDR offerings. This report will help you weigh the pros and cons of the major technology approaches so you can focus on getting the most security coverage for your organization. Features and functionality will be assessed alongside setup time, the cost of add-ons or tuning, and the expertise required for monitoring and maintenance.
XDR solutions will continue to evolve. This snapshot provides a good guide for evaluating five current options in light of your organization’s size, sophistication, and current infrastructure. The chart at the end of this report provides a detailed comparison along seven decision-making criteria.
The choice of an XDR platform is primarily driven by the needs and current state of your organization. There’s no one-size-fits-all solution, so you’ll want to compare approaches to find the one that best addresses your specific circumstances. The first step is to define your organizational needs with some basic questions:
Cloud-native XDR leverages cloud architecture for flexible and scalable analysis. This approach is worth a look for organizations with high or variable data volumes. These solutions often include built-in AI and behavioral analytics tools that can enable advanced detection without extensive tuning.
Vendor-specific cloud-native XDR such as Microsoft Sentinel usually do well within their platform environment, but they provide less support for third-party platforms, and visibility is limited to cloud-based sources. In addition, costs can be unpredictable as data volumes grow and additional cloud resources are consumed.
Three considerations for cloud-native XDR:
Closed XDR primarily targets enterprise-scale organizations, offering a hybrid on-prem and cloud solution. These vendorspecific systems are tied to the provider’s own platform, which can limit the user’s flexibility.
Many closed XDR solutions lack automated response, longterm data retention or SIEM, and 24/7 support, requiring expensive add-ons. They are often complex to set up and maintain, requiring steep learning curves and extensive tuning.
Three considerations for closed XDR:
Vendors like SentinelOne and CrowdStrike have evolved their original endpoint detection and response (EDR) solutions to add correlation, automation, and response capabilities. These solutions use endpoint data for AIdriven threat hunting based on behavioral patterns and the MITRE ATT&CK framework. MITRE ATT&CK is an industry framework that’s used to design detection rules around known attack techniques.
Users are finding that EDR-based XDR tends to provide noisy detections and high numbers of false positives. Extensive tuning and optimization by the customer or an engineer is often required to prevent “alert fatigue” which can distract users from identifying high-impact threats.
Three considerations for EDR-based XDR:
Another approach to XDR is being driven by artificial intelligence. These solutions use AI and machine-learning algorithms to detect threats and automate responses. The promise of this approach is a reduction in manual tuning time for detections and policies, but higher false positives tend to show up until the system is trained on a sufficient amount of data.
AI-driven XDR solutions are currently limited to specific data sources, often endpoints or cloud applications, and they struggle with correlation across many different sources. These issues can cause users to miss critical threats, which slows investigation and mitigation.
Three considerations for AI-driven XDR:
Blumira starts with a robust security information and event management solution (SIEM) as a base and integrates XDR capabilities for improved threat detection and response. This approach combines compliance, log analysis, security analytics, and automated response in a single platform. The result is better correlation across diverse sources because SIEM-driven XDR analyzes data from firewalls and cloud integrations—not just endpoints.
The Blumira all-in-one solution is a good fit for organizations with busy IT teams or limited internal security expertise, and most of the features can be used right out of the box. It’s an approach that gives equal importance to compliance and security, providing value to multiple stakeholders. Traditional SIEM-driven XDR solutions charge by data volume, which can limit their scalability. But the Blumira cloud SIEM has a flexible pricing model for growing organizations, and provides the ability to collect and retain mass amounts of data without corresponding cost increases.
Key considerations for SIEM-driven XDR:
The Blumira allin-one solution is a good fit for organizations with busy IT teams or limited internal security expertise, and most of the features can be used right out of the box.
XDR solutions will continue to evolve with the development of technology capabilities and with the new opportunities presented by AI and machine learning. While it seems that everything about security and compliance is getting more complex, Blumira has built a solution that does more while making life easier for IT teams. Blumira stands out for providing comprehensive visibility with less distracting noise, earlier detection and automated response to stop attackers in their tracks, guided response playbooks and 24/7 SecOps support, and a datafirst approach with time-saving compliance capabilities.
Blumira’s SIEM + XDR platform makes advanced detection and response easy and effective for small and medium-sized businesses, accelerating ransomware and breach prevention for hybrid environments. Time-strapped IT teams can do more with one solution that combines SIEM, endpoint visibility and automated response. Contact us today to find out more or schedule a demo.
|Correlation & Collection
|Scalability & Overhead
|Ease of Use
|C&C are first class citizens
|Built-in Detection Engineering & Threat Hunting
|Automatic generation of Analysis and Workflow based on context
|Scales with your environment, reduces TCO of security
|SIEM+XDR simplifies use by combining important technology into a simple platform
|Some Unlimited, others charge per/GB month
|Some Automated Parsing
|Collection of SaaS data improves
|Library of Detections, often Open Source and not tuned
|Cookie cutter workflows and/or base alerting
|Scales with your environment but can cost extra to do so.Often requires hands-on configuration
|Sometimes easy to manage, often has great dashboarding and visualization.Detections take additional overhead.
|Unlimited for Vendor Data
|Only Automated for Vendor
|Great Correlation InVendor Tooling
|Library of Detections,focused on Vendor
|Focused on fancy UI,often requires a SOC to complete
|Integration into like-technology simplifies overhead, can be difficult to get value from other sources
|Can work seamlessly within tech stack but often built for enterprise use in either case. Difficult for SMB alignment.
|Unlimited for EDR Data
|Only Automatedfor EDR
|Correlation of EDR telemetry
|Library of Detections, focused on EDR telemetry
|Workflows are often mature within detections for the EDR product
|Integration into like-EDR simplifies overhead, can be difficult to get value from other sources
|Great for a power user who wants to dig in, can be difficult to find signal in noise and work detections without a team.
|Generally Parsed for AI use
|Correlation driven by AI
|Anomaly and behavior detections based on AI models
|Generated workflows and automated responses where setup is possible
|Can be difficult to scale large volumes of data in AI models. Significant tuning
overhead at times
|The need for upfront training and continuous evaluation can be cumbersome
This chart provides a side-by-side comparison of the five XDR approaches along seven key dimensions.