SIEM vs XDR: Capabilities and Key Differences
From a security perspective, even small enterprises represent vast webs of interconnected devices. Endpoint devices are only the tip of the iceberg – and the average company relies on hundreds of thousands of them at any one time. Whether it’s employee laptops or your cloud’s virtual machines, your company is dependent on the constant exchange of information. Then, you’ve got all of the surrounding infrastructure that keeps this data flowing: load balancers, data storage, and APIs – to name a few.
As network sizes have ballooned in scope, bad actors are increasingly able to slip through the gaps. Each of these components play their own role in keeping everyone efficient and interconnected. As a security professional, however, the sheer variety of devices and networks can be a source of constant stress. The real-time implications of this are severe: alongside a shockingly high employee churn rate, security teams are reliant on sprawling and disparate tech stacks as they hope to create order out of the chaos.
This article will examine two SOC technologies – Security Information and Event Management (SIEM) and Extended Detection and Response (XDR) – and compare how each can be used to streamline and prioritize the terabytes of information on hand.
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What is SIEM and How Does it Work?
In order to keep a degree of insight into the sprawling mess of devices, firewalls and switches, a SIEM solution would originally make use of one common denominator – logs. Logs are small files that contain information about an application’s or server’s internal workings, such as errors, connections, and events. While these have been commonplace in development for quite some time, SIEM applications were the first to give security teams deeper insight into application health. Coined in 2005, SIEM’s evolution has been rapid: whereas early systems were little more than log collection tools, modern offerings aggregate and analyze this data in near-real-time. As a result, well-configured SIEMs are able to cut through the noise of endless logs and alert security administrators to events they should pay attention to. This process is made possible via rules. For more info, see our guide to ‘What is SIEM?’
SIEM rules allow for raw log data to be transformed into action. To achieve this, SIEM combines and interlaces two forms of analysis: correlation rules and models. Correlation rules simply tell your SIEM system what sequence of events could indicate an attack, and to notify your admin team when something doesn’t seem right.
While individual rules can be as simple as flagging when a user is attempting to download vast amounts of data, there’s generally not enough nuance within each rule – this clogs your alert feed up with junk. Composite rules allow these to hone in on concerning behaviors by chaining multiple rules together. This way, your SIEM can flag alerts if 6 failed login attempts come from the same IP address – but only if that IP address is trying with 6 different usernames.
When scaling composite rules up to the real-time, high-stakes demands of an organization, many teams rely on model profiles. These are representations of your users’ and assets’ normal behavior. By profiling how data usually flows across your networks, it becomes possible for an advanced SIEM tool to build a picture of what’s normal. By then layering rules over a model-based SIEM, it becomes possible to spot and trigger an alert if suspicious behavior pops up – such as a user switching from their normal account to a privileged one, then attempting to perform an abnormal data transfer to or from an external service.
Complementing the deep log analysis, modern SIEM platforms offer dashboards that provide a unified view of threats across most of your organization’s technology stack. Enhanced by data visualizations, these dashboards enable security analysts to easily spot and respond to suspicious activities. This integration of advanced analysis, alongside intuitive visual monitoring, underscores the pivotal role of SIEM in today’s cybersecurity defenses.
What is XDR and How Does it Work?
While SIEM tools have provided security professionals with unparalleled log visibility, there remain two considerable issues: firstly, that many systems either don’t produce logs, or cannot be fed into the SIEM tool, and secondly, that the rule-based approach leaves security teams swamped with unimportant alerts.
An XDR solution is less of a single, off-the-shelf tool, and more of a collection of several security concepts. Ultimately, XDR systems aim to drastically expand the scope of security events by examining the streams of data from endpoints, email systems, networks, IoT devices, and applications. Think of it as an evolution of Endpoint Detection and Response (EDR) systems, but rather than rely on traditional security measures that operate in silos, XDR integrates the log management approach of SIEM with a number of other security components to form a cohesive whole. For instance, integrating EDR systems within XDR allows organizations to extend visibility into every endpoint, detecting and responding to threats on individual devices. By then incorporating network traffic analysis, XDR can analyze data packets in real-time, and enrich the network view with data from endpoints. This process helps identify even advanced attack patterns such as lateral movement and novel intrusion attempts.
Cloud security tools are another crucial integration point for XDR systems. As organizations increasingly move their operations to the cloud, integrating cloud access security brokers (CASBs) and secure web gateways into the XDR ecosystem ensures that cloud environments are continuously monitored and protected against threats. XDR’s scope is as as wide as you want to make it: integrating identity and access management (IAM) solutions further provides insight into user behaviors and access patterns, helping to detect and prevent identity-based attacks.
This massive quantities of telemetry data is then fed into an analysis engine that determines the severity and scope of every alert. Once a potential threat is identified, XDR platforms can automatically respond to it by isolating affected systems, blocking malicious activity, rolling back actions to a safe state, or sending contextual alerts to the security team. Thanks to its wider lens of visibility, XDR provides a promising foundation for automated security responses.
These automated playbooks help automate responses based on threat severity, drastically reducing response time and alert backlogs. If not remediation, then the XDR is still able to collect and visualize the cross-department information that – ordinarily – an analyst would be left with. This hi-fi picture of a security incident or attack then allows analysts to invest time in more focused, strategic work. If you’re still left asking ‘What is XDR?’, see our deep-dive into this new and exciting field.
SIEM vs XDR Comparison: 5 Key Differences
The differences between SIEM and XDR solutions are nuanced but incredibly important: from a security perspective, SIEM offers a way to collect and store logs for compliance, data storage, and analysis. For traditional SIEM solutions, overarching security analytics have largely just been bolted on top of those pre-existing log collection and normalization features. As a result, SIEM tools often require a hefty analytics function in order to adequately identify threats. Without a native ability to distinguish between genuine threats and false alarms, security teams are often left climbing a Mount Everest of log data.
XDR, on the other hand, is purpose-built for threat identification: its development has risen to fill the voids left between the logs collected by SIEM. Its distinctly different approach is anchored in endpoint and firewall data, rather than raw logs alone. While XDR offers organizations new security capabilities and enhanced protection, it’s important to note that it should not fully replace SIEM, as SIEM still has vital use cases outside of threat detection, such as log management and compliance.
The following table offers an in-depth XDR vs SIEM comparison.
| SIEM | XDR | |
| Data Source | Any device that generates an event or collects it in the form of a flat log file. | Endpoints, firewalls, servers, and other security tooling – including SIEM. |
| Deployment Location | Data collected via agents installed on-device. The SIEM is hosted in your data center with a dedicated SIEM appliance. | Agents on each endpoint and network appliance. Central depository is within own architecture. Vendor threat intelligence is used to enrich internal analysis. |
| Deployment Model | Storage systems require manual maintenance – log-based alerts must be managed by trained security personnel. Pre-integration with cloud systems and data sources is common, allowing for quicker deployment. | Vendors’ in-house threat detection teams identify new or emerging threats. Threat identification and response processes are increasingly automated. Manual security ops are needed to address the highest-priority threats. |
| Performance and Storage Considerations | No negative performance impact. Large quantity of logs – storage required for between 1 and 7 years, depending on compliance. Historical log quantity can be managed with syslog servers, which retain only essential information in a standardized format. | When monitoring east-west traffic, performance can be impacted. Depending on the organization’s size, a data lake may be required for telemetry data. |
| Fundamental Approach | Enables organizations to scrutinize log data from all network applications and hardware at any given moment. | Enhances an organization’s security by streamlining the collection, analysis, and remediation across the full spectrum of its security tools. |
SIEM Pros and Cons
SIEM, while groundbreaking at its inception, is still only a log-focused approach to security. You may already be familiar with the benefits of SIEM, and how it can accelerate incident detection, but its intense resource demands can leave many organizations scrambling to put a stop to the firehose of alerts. While Stellar Cyber’s Next-Gen SIEM Platform combats many of these downsides, traditional SIEM remains a white elephant for many companies.
SIEM Pros
Faster Than Manual Log Management
Effectively deployed, SIEM reduces the timeframe for detecting and recognizing threats, enhancing your capacity to respond swiftly and either mitigate or entirely avert damage. Furthermore, SIEM’s adaptability in monitoring for behaviors indicative of an attack, rather than just relying on attack signatures, aids in identifying elusive zero-day threats that might bypass conventional security measures like spam filters, firewalls, and antivirus programs. Ultimately, SIEM solutions significantly improve detection and response times by handling some of the manual event analysis.
Strong All Rounder
SIEM serves a broad spectrum of uses throughout your organization, ranging from operational support to troubleshooting. It equips IT teams with essential data and historical logs, enhancing their efficiency and effectiveness in managing and troubleshooting issues outside of cybersecurity alone.
SIEM Cons
The struggle of real-time reporting
One inherent limitation of SIEM is its time-related issues such as synchronization and processing. Even if a report is generated quickly, the time needed for an analyst to process and act upon an alert means that responses almost inevitably lag behind real events. While automation can mitigate some delays, especially for common threats, even real-time analysis must go through the time-consuming process of report generation.
Fine-tuning Demands Full-Time Support
You may already have a solid understanding of your own network and services, but SIEM success is solely reliant on the solution reflecting this knowledge as well. This process demands far more than just a spreadsheet of IP addresses – instead, SIEM systems demand constant updates at regular intervals. This is why such large-scale tools require full-time support teams. These security staff are solely focused on keeping the SIEM tool running well – rather than actively analyzing and triaging alerts.
It’s certainly possible to simply throw all alarms from all devices into the SIEM, but finding genuine incidents would be almost impossible. The noisiest alerts would likely come from the typical malware that most commonly targets your organization. Beyond that, however, the mess of alerts would become essentially meaningless. Without tuning, thousands of alerts can end up as meaningless noise.
Siloed
In most cases, SIEM tools are siloed—there’s no communication or cross-referencing with other security tools in your stack. As a result, your security team needs to manually compare alerts across different dashboards and tools. This means that most incident identification and triaging is still almost entirely manual. As a result, all processes downstream of a SIEM report still demand considerable technical expertise. Knowing what information is important – and how it relates to the rest of your network – is still critical.
XDR Pros and Cons
As organizations grapple with increasing volumes of cyber threats, the appeal of XDR’s integrated approach is undeniable. However, like any technology, XDR comes with its own set of advantages and challenges. A balanced understanding of the tool’s pros and cons requires an exploration of the potential complexities and resource requirements associated with implementing and managing an XDR solution. This comparison aims to equip cybersecurity professionals and enthusiasts with a clearer understanding of XDR’s true value proposition.
XDR Pros
Extended Detection
XDR collects security-relevant data from across an organization: this is then collated and analyzed, reducing the spools of raw information into smaller, high-fidelity incident alerts. The wider scope of telemetry data – and the enhanced understanding of interconnected systems – makes it more likely that your team can find an active threat. Of course, collecting data is only half of the process.
Extended Analysis
When a suspect incident pops up, an in-depth inquiry soon follows. A competent XDR system delivers the essential analysis required for organizations to address critical queries: is this threat genuine or merely a false alarm? Does it signify a more significant risk? If that’s the case, what extent does it cover? In the current landscape, numerous cyberattacks unfold in multiple stages, with sections of the attack vanishing once their specific role is fulfilled. XDR platforms understand that the absence of initial signs does not guarantee the organization’s security – or indicate that the danger has fully passed.
XDR Cons
Vendor Lock-In
Despite XDR’s potential, the reality of today’s cybersecurity market is still holding back many XDR tools’ potential. Vendors that specialize in specific security tooling are currently the ones offering vendor-locked XDR: as a result, the additional security demands of an XDR are rapidly developed and bolted on. For organizations that aren’t as experienced with certain capabilities, security teams end up with a flawed toolkit that performs worse than a basic SIEM.
Why AI-Driven XDR is Overtaking SIEM
While SIEM continues to be a useful tool for some organizations, its ongoing heavy reliance on siloed datapoints and labor-intensive security mechanisms has left many teams questioning the future of traditional SIEM. The ability of lean cybersecurity teams to keep up with the volumes of log, network, and user data – all spread across a myriad of different dashboards – has never been under more pressure. This is the crack in traditional tooling that XDR is poised to fill.
Essentially, AI-driven XDR grants teams the granular visibility that SIEM once promised – alongside an entire suite of cybersecurity systems that simply eclipse SIEM’s possibilities. No longer confined to a single, isolated view of your tech stack, XDR’s multi-faceted approach allows data to be pulled in from every corner of your attack surface. From network traffic to user access, an all-encompassing XDR solution delivers more than just basic threat detection. By pulling in all the information gleaned by SIEM, NDR, and more, an XDR’s AI engine can act as a rudimentary security analyst. Analyzing and querying potential threats to establish their legitimacy, can even build a picture of the associated attack chain. Learn how the benefits of AI-driven XDR extend far beyond SIEM’s threat detection potential.
A growing emphasis on lean, developing cybersecurity teams is increasingly demanding more and more from the tools your organization puts in place. While XDR is generally not plug-and-play, certain tools are built with implementation in mind: choosing one with pre-built integrations can minimize changeover time, and rejuvenate your defenses with eye-watering efficiency.
Avoid Lock-In And Unlock Full Security Comprehension
Stellar Cyber’s Open XDR platform offers the next evolution of security tooling: an integrated solution that empowers organizations to proactively detect, investigate, and respond to threats across their entire digital ecosystem. With its open and scalable architecture, the platform seamlessly aggregates data from various security tools, including network, cloud, and endpoint sources, providing a unified view and comprehensive insights into potential security threats. Explore Stellar Cyber’s Open XDR Platform today.
