AI XDR: The 6 Benefits of AI-Driven XDR
Right this second, the applications and servers that make up the fabric of your organization’s tech stack are producing a steady stream of information. Traditionally, this constant stream of data used to be a security professional’s nightmare. The fight against the barrage of log files has been a non-stop war for the last few decades, waged entirely under the radar of everyday end users.
Even small organizations just tracking essential metrics amass a significant volume of log data. On the other end of the spectrum, large corporations can accumulate hundreds of gigabytes worth of logging information daily. Today, many organizations rely on a few different solutions – such as Security Information and Event Management (SIEM) and Network Detection and Resolution (NDR) – to keep a handle on it all. Both solutions tackle this issue by aggregating log data from across the network’s scope, and streamlining them into alerts. However, both have limitations: complex setup and management and high false positive rates have kept security analysts on a knife’s edge between effective threat management and the mass of constant alerts. Security is still suffering due to tool siloes.
To address these challenges, Extended Detection and Response (XDR) has emerged. Its focus is on zooming out even further by comparing log files with other vital pieces of security data. Enter, AI integration: cutting-edge analysis of your entire network that contextualizes every alert within its own unique confines. By unifying data from various security layers, XDR promises a rapid improvement of your detection and response capabilities. This article will address how it works, and whether AI-driven XDR is truly worth the hype.
Even small organizations just tracking essential metrics amass a significant volume of log data. On the other end of the spectrum, large corporations can accumulate hundreds of gigabytes worth of logging information daily. Today, many organizations rely on a few different solutions – such as Security Information and Event Management (SIEM) and Network Detection and Resolution (NDR) – to keep a handle on it all. Both solutions tackle this issue by aggregating log data from across the network’s scope, and streamlining them into alerts. However, both have limitations: complex setup and management and high false positive rates have kept security analysts on a knife’s edge between effective threat management and the mass of constant alerts. Security is still suffering due to tool siloes.
To address these challenges, Extended Detection and Response (XDR) has emerged. Its focus is on zooming out even further by comparing log files with other vital pieces of security data. Enter, AI integration: cutting-edge analysis of your entire network that contextualizes every alert within its own unique confines. By unifying data from various security layers, XDR promises a rapid improvement of your detection and response capabilities. This article will address how it works, and whether AI-driven XDR is truly worth the hype.
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What Exactly Is AI-Driven XDR?
First, let’s establish what XDR is.
XDR is a type of security technology that assembles the hodgepodge of security technologies already in your toolkit into a cohesive and streamlined whole. Thanks to this, XDR offers greater visibility into the security posture of all assets and devices and can detect and respond to threats in real-time.
AI offers some considerable benefits within this, thanks to the ability to detect different forms of behavior. For instance, take the traditional antivirus: if a user is about to download a malware-loaded file, traditional malware defense could only scan the file to try and find a set, pre-recognized pattern of bytes that indicate a known malware. However, polymorphic and novel strains of malware have rendered some severe holes in this form of defense. The AI space has already vastly outperformed this, thanks to an ability to analyze the expected behavior of a file, user account, or network device.
AI-driven XDR’s approach toward suspicious behavior can be split into two fields: static and dynamic. Static analysis extracts low-level information about the event – such as system calls, and the control and data flow graphs. This helps lend a degree of depth to an alert or event, without spending too much time on each alert. Dynamic analysis, on the other hand, allows for a suspicious network device or file to be inspected from a runtime POV. For a piece of malware, this sees a suspicious file executed in a sandbox, in order to be analyzed without affecting genuine production systems.
To further illustrate how generative AI is transforming security capabilities across the field, consider its use in the detection of compromised accounts. With no reliance on manually-labeled training data, early AI projects were built to collect network user login activity, and build a model that predicts an expected baseline of activity. For instance, if a user has to try logging in again after one failure, it’s expected that the IP address and time remain roughly consistent. If those are true, then the associated risk score is kept low. Should the IP address, login attempt time, or number of login attempts begin to grow beyond the expected baseline, the model highlights it as suspicious.
The first successful demonstration of this occurred with Microsoft’s Project Qidemon in 2021 – upon testing the model on real-world data, it successfully highlighted seven compromised accounts in a pool of 20,000 users. Generative AI evolution has only accelerated in the three years since. XDR solutions focus on coupling up the unique applications of AI, and provide a degree of cross-referencing between previously distinct security fields. Fundamentally, XDR is about allowing data from one area – such as malware prevention – to influence the detection remediation capabilities within another – such as account protection. While these two examples only give quick glimpses into the developing benefits of AI, they help illuminate how XDR systems and AI exist in parallel, keeping the entirety of your tech stack benefiting from the field’s growing proficiency.
XDR is a type of security technology that assembles the hodgepodge of security technologies already in your toolkit into a cohesive and streamlined whole. Thanks to this, XDR offers greater visibility into the security posture of all assets and devices and can detect and respond to threats in real-time.
AI offers some considerable benefits within this, thanks to the ability to detect different forms of behavior. For instance, take the traditional antivirus: if a user is about to download a malware-loaded file, traditional malware defense could only scan the file to try and find a set, pre-recognized pattern of bytes that indicate a known malware. However, polymorphic and novel strains of malware have rendered some severe holes in this form of defense. The AI space has already vastly outperformed this, thanks to an ability to analyze the expected behavior of a file, user account, or network device.
AI-driven XDR’s approach toward suspicious behavior can be split into two fields: static and dynamic. Static analysis extracts low-level information about the event – such as system calls, and the control and data flow graphs. This helps lend a degree of depth to an alert or event, without spending too much time on each alert. Dynamic analysis, on the other hand, allows for a suspicious network device or file to be inspected from a runtime POV. For a piece of malware, this sees a suspicious file executed in a sandbox, in order to be analyzed without affecting genuine production systems.
To further illustrate how generative AI is transforming security capabilities across the field, consider its use in the detection of compromised accounts. With no reliance on manually-labeled training data, early AI projects were built to collect network user login activity, and build a model that predicts an expected baseline of activity. For instance, if a user has to try logging in again after one failure, it’s expected that the IP address and time remain roughly consistent. If those are true, then the associated risk score is kept low. Should the IP address, login attempt time, or number of login attempts begin to grow beyond the expected baseline, the model highlights it as suspicious.
The first successful demonstration of this occurred with Microsoft’s Project Qidemon in 2021 – upon testing the model on real-world data, it successfully highlighted seven compromised accounts in a pool of 20,000 users. Generative AI evolution has only accelerated in the three years since. XDR solutions focus on coupling up the unique applications of AI, and provide a degree of cross-referencing between previously distinct security fields. Fundamentally, XDR is about allowing data from one area – such as malware prevention – to influence the detection remediation capabilities within another – such as account protection. While these two examples only give quick glimpses into the developing benefits of AI, they help illuminate how XDR systems and AI exist in parallel, keeping the entirety of your tech stack benefiting from the field’s growing proficiency.
The Benefits of AI-Driven XDR
AI-Driven XDR benefits can be split into three primary fields: data analysis, threat detection, and attack response. Each of these areas has undergone rapid evolution since implementing AI architecture and analysis.
Data Analysis
Access to comprehensive security data has always been a cornerstone for security teams engaged in various critical activities, including monitoring ongoing attacks, conducting post-incident forensic analyses, and executing threat hunting operations. These tasks demand an ability to make sense of the constant streams of event and permissions data emanating from every app, user, and server.
In the past, statisticians and early data science pioneers frequently had to rely on limited subsets of data, working with samples that were representative but not comprehensive. This would then trickle down into static, rule-based security architecture. AI XDR re-negotiates how data is leveraged within your organization’s security philosophy with two analysis benefits: the architecture it’s based upon; and the analysis engine.
In the past, statisticians and early data science pioneers frequently had to rely on limited subsets of data, working with samples that were representative but not comprehensive. This would then trickle down into static, rule-based security architecture. AI XDR re-negotiates how data is leveraged within your organization’s security philosophy with two analysis benefits: the architecture it’s based upon; and the analysis engine.
Rise of Data Lakes
Part of the reason for AI’s sudden break out into the mainstream was the development of data warehouses into data lakes. The former approach segments data into hierarchical files – making it great for human use – but data lakes flatten out the file hierarchies into one massive pool of data. Hosted on uber-efficient architecture, the scale of data at our disposal is bigger than ever before.
As a result, analysts are granted the capability to engage with extensive datasets in their entirety. This shift enables a deeper dive into the full complexity, nuances, and detailed aspects of data, eschewing the need for reliance on mere representative samples.
Furthermore, the efficiency of data lakes allows AI-enabled XDR to circumvent many of the issues faced by previous security systems, and provide deep insight into your organization’s own unique array of security systems. By re-positioning security data into a centralized, constantly-updated database, the stage is set to employ the second key component of XDR AI.
As a result, analysts are granted the capability to engage with extensive datasets in their entirety. This shift enables a deeper dive into the full complexity, nuances, and detailed aspects of data, eschewing the need for reliance on mere representative samples.
Furthermore, the efficiency of data lakes allows AI-enabled XDR to circumvent many of the issues faced by previous security systems, and provide deep insight into your organization’s own unique array of security systems. By re-positioning security data into a centralized, constantly-updated database, the stage is set to employ the second key component of XDR AI.
The Analysis Engine
While data lakes grant AI the ability to access the vast swathes of today’s security data, there’s still the ML component of the tool. In general, Machine Learning employs complex mathematical algorithms to deduce relationships between different elements and categories. This computational analysis allows the systems to learn from data, processing billions of data points to develop optimal responses to new data instances and establish reliable patterns over time.
For XDR, this process is particularly important given the challenges humans face in analyzing large quantities of data and identifying patterns or anomalies, AI and ML technologies offer invaluable assistance. These technologies are adept at swiftly processing and assessing diverse forms of data, such as network packet information, security event logs, and source code. The urgent need for pattern recognition and behavioral analysis in security operations and risk management underscores the growing reliance on AI and ML in these fields, highlighting their critical role in enhancing cybersecurity measures.
For XDR, this process is particularly important given the challenges humans face in analyzing large quantities of data and identifying patterns or anomalies, AI and ML technologies offer invaluable assistance. These technologies are adept at swiftly processing and assessing diverse forms of data, such as network packet information, security event logs, and source code. The urgent need for pattern recognition and behavioral analysis in security operations and risk management underscores the growing reliance on AI and ML in these fields, highlighting their critical role in enhancing cybersecurity measures.
Threat Detection
AI’s sophisticated framework is adept at sifting through the datasets gleaned from a myriad of sources within an organization’s digital ecosystem, including network traffic, endpoints, cloud environments, and application logs. This unified dataset allows a degree of threat detection that far exceeds typical, siloed security toolings.
In the same way that AI takes a step back to collate and analyze every piece of data, the field of XDR tooling aims to zoom out from the humdrum of individual security toolings. Instead, XDR leverages these extensive data volumes to rapidly parse through activity and identify any concerning activities that may link to wider patterns of malicious behavior.
To illustrate, consider attackers that have already established a connection to a command and control server. Relatively advanced attackers may have encrypted these channels, posing a much greater risk, as your SOC team would struggle to spot nefarious sessions from the hundreds of other, legitimate ones in a day. ML models are ideally placed to identify malicious beacons (that is, regular bursts of traffic that include consistent quantities of data) that are conversing with external domains. Even better, this behavior-based identification doesn’t require decryption.
AI XDR allows identification measures such as the above to be employed across vastly more complex and interlinked attack surfaces. While a typical network-based security solution could replicate the threat identification process we just covered, only an XDR could correlate evidence of clicking a link embedded within an email; note a company device’s site access, identify unusual download activity – and finally link it with the network patterns indicative of a command and control server.
AI’s role in XDR marks a transformative shift towards proactive security practices, empowering organizations to preempt and stay ahead of the curve against the backdrop of ever-evolving cyber threats. A key advantage of AI in this context is its capacity for continuous learning and adaptation through deep learning techniques. As the system evolves with new data and shifting threat landscapes, it not only improves threat detection accuracy but also lowers the incidence of false positives. This refined threat discernment allows security teams to concentrate on genuine risks, thereby enhancing operational efficiency and response times, marking a significant leap forward in cybersecurity operations.
In the same way that AI takes a step back to collate and analyze every piece of data, the field of XDR tooling aims to zoom out from the humdrum of individual security toolings. Instead, XDR leverages these extensive data volumes to rapidly parse through activity and identify any concerning activities that may link to wider patterns of malicious behavior.
To illustrate, consider attackers that have already established a connection to a command and control server. Relatively advanced attackers may have encrypted these channels, posing a much greater risk, as your SOC team would struggle to spot nefarious sessions from the hundreds of other, legitimate ones in a day. ML models are ideally placed to identify malicious beacons (that is, regular bursts of traffic that include consistent quantities of data) that are conversing with external domains. Even better, this behavior-based identification doesn’t require decryption.
AI XDR allows identification measures such as the above to be employed across vastly more complex and interlinked attack surfaces. While a typical network-based security solution could replicate the threat identification process we just covered, only an XDR could correlate evidence of clicking a link embedded within an email; note a company device’s site access, identify unusual download activity – and finally link it with the network patterns indicative of a command and control server.
AI’s role in XDR marks a transformative shift towards proactive security practices, empowering organizations to preempt and stay ahead of the curve against the backdrop of ever-evolving cyber threats. A key advantage of AI in this context is its capacity for continuous learning and adaptation through deep learning techniques. As the system evolves with new data and shifting threat landscapes, it not only improves threat detection accuracy but also lowers the incidence of false positives. This refined threat discernment allows security teams to concentrate on genuine risks, thereby enhancing operational efficiency and response times, marking a significant leap forward in cybersecurity operations.
Attack Response
AI XDR’s impact isn’t limited to just the identification phase: its reach continues throughout the triage and response process.
Root Cause Insight
By providing deep insights into the root causes of incidents and outlining the sequence of attack, AI-powered XDR tools enable faster and more efficient investigations. This accelerates the process from detection to response, helping organizations to quickly understand and mitigate the effects of security breaches.
Alert Prioritization
While security tools have typically developed a tendency to swamp analysts with endless alerts, XDR is uniquely positioned to compare an alert with the associated data flows and activities surrounding it. This contextual focus significantly reduces the burden on security teams by automating the triage process, allowing them to focus on the most critical alerts first.
Automated Response
AI streamlines the response to security incidents by automatically executing actions such as isolating compromised devices, blocking malicious activities, and implementing remediation measures in real-time. This rapid response capability minimizes the potential impact of threats and ensures that security measures are swiftly enacted with fewer demands for manual intervention.
Why Is AI-Driven XDR Replacing SIEM?
The driving factor of XDR’s current success is its internal AI engine. With an unparalleled ability to compare the hundreds of datapoints surrounding each alert – and with an in-depth, customizable dashboard to match – budget-conscious managers are only a stone’s throw away from re-evaluating the necessity of other pieces of the cybersecurity tech stack. For good reason: tool sprawl has been a concern for over half a decade now, as large organizations have tried to patch the cybersecurity skills gap with an abundance of hyper-specific tools. However, more tools have simply just added to analysts’ workflows – instead of one alert-generating machine, they’ve got to deal with dozens. Now, however, AI is pushing cybersecurity’s development past hyper-specific, niche tooling and toward overarching, high-level understanding.
The consolidation revolution has already begun – Gartner’s 2024 predictions show that within the next three years, 70% of organizations will have combined data loss prevention tools and insider risk prevention with IAM context. The identification of potential attack data is becoming increasingly behavior-focused, and allows security teams to issue single policies that have dual effects within both data security and insider risk.
SIEM tools have typically prided themselves on the granular degree of information that can be gleaned from log analysis. However, modern AI-driven XDR tooling incorporates the same log data ingestion and analysis – alongside so much more. By capturing and analyzing all security data within a single repository, traditional SIEM tools begin to look obsolete. While next-gen SIEMs have since started to implement AI models of their own – to help analyze the abundance of log data they’re collecting – the wider scope of XDR further leaves SIEM in the dust. Instead of analyzing log data alone, XDR takes SIEM’s individual datapoints and contextualizes them within the wider landscape of network and user activity.
The consolidation revolution has already begun – Gartner’s 2024 predictions show that within the next three years, 70% of organizations will have combined data loss prevention tools and insider risk prevention with IAM context. The identification of potential attack data is becoming increasingly behavior-focused, and allows security teams to issue single policies that have dual effects within both data security and insider risk.
SIEM tools have typically prided themselves on the granular degree of information that can be gleaned from log analysis. However, modern AI-driven XDR tooling incorporates the same log data ingestion and analysis – alongside so much more. By capturing and analyzing all security data within a single repository, traditional SIEM tools begin to look obsolete. While next-gen SIEMs have since started to implement AI models of their own – to help analyze the abundance of log data they’re collecting – the wider scope of XDR further leaves SIEM in the dust. Instead of analyzing log data alone, XDR takes SIEM’s individual datapoints and contextualizes them within the wider landscape of network and user activity.
How AI XDR Reduces False Positives
SIEM, email protection tools, and firewalls are notorious for the number of alerts they fire off. With no context or root cause included, the human analyst is left having to make sense of the spiraling volumes of alerts. Tracking down impacted users and determining whether it’s genuinely malicious activity all takes time: leaving more time for other alerts to pile up, and get in the way of genuine threat detection.
Market-leading AI-based XDR solutions offer a way to balance the wealth of data at their disposal with the security analysts’ own capacity. To achieve the maximum degree of security without sacrificing sensitivity, XDRs take alerts and correlate them with relevant assets, users, and signals – this correlated whole is then an Incident. As new alerts pop up, each is automatically assigned to its relevant incident. In this way, complex attacks can be discovered and acted on, while singular false alarms are left out of the conversation.
The ability to track an attack story across the device, user identity, or cloud deployment means that security analysts can handle a far greater number of alerts in a far more cohesive way. By evaluating the wider context of alerts, AI-based XDR essentially cuts out a huge time waster, allowing lean security teams to concentrate on remediating the most critical threats as quickly as possible. It’s this incident-first approach that is leading the way to simplified security tech stacks, rapid remediation, and less stressed analysts. Discover more about how Stellar Cyber’s AI-powered alerts work here.
Market-leading AI-based XDR solutions offer a way to balance the wealth of data at their disposal with the security analysts’ own capacity. To achieve the maximum degree of security without sacrificing sensitivity, XDRs take alerts and correlate them with relevant assets, users, and signals – this correlated whole is then an Incident. As new alerts pop up, each is automatically assigned to its relevant incident. In this way, complex attacks can be discovered and acted on, while singular false alarms are left out of the conversation.
The ability to track an attack story across the device, user identity, or cloud deployment means that security analysts can handle a far greater number of alerts in a far more cohesive way. By evaluating the wider context of alerts, AI-based XDR essentially cuts out a huge time waster, allowing lean security teams to concentrate on remediating the most critical threats as quickly as possible. It’s this incident-first approach that is leading the way to simplified security tech stacks, rapid remediation, and less stressed analysts. Discover more about how Stellar Cyber’s AI-powered alerts work here.
Choose Advanced, AI-Driven Threat Detection
With how much an XDR solution relies on cross-channel visibility, it’s vital that the solution in question is kept open and highly-implementable. Instead of being locked into one vendor’s tech stack, Stellar Cyber’s open XDR grants cutting-edge threat detection to your pre-existing architecture. This transforms the siloed operations that you may already have in place into a fully universal EDR tool.
While the core AI-driven XDR tool benefits lean cybersecurity teams immensely, Stellar Cyber’s commitment to security analysts has seen yet further generative AI advancements. Now, analysts are even granted the ability to pose investigation-related queries at the tool itself. Responding with conversational insight, the tooling allows analysts with still-developing skillsets – or limited time – to make the most of the tool’s intelligence quicker than ever before.
Discover more about our open XDR capabilities and begin unleashing your security team’s full potential.
While the core AI-driven XDR tool benefits lean cybersecurity teams immensely, Stellar Cyber’s commitment to security analysts has seen yet further generative AI advancements. Now, analysts are even granted the ability to pose investigation-related queries at the tool itself. Responding with conversational insight, the tooling allows analysts with still-developing skillsets – or limited time – to make the most of the tool’s intelligence quicker than ever before.
Discover more about our open XDR capabilities and begin unleashing your security team’s full potential.
