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June 19, 2023

Darktrace Detection of 3CX Supply Chain Attack

Explore how the 3CX supply chain compromise was uncovered, revealing key insights into the detection of sophisticated cyber threats.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Nahisha Nobregas
SOC Analyst
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19
Jun 2023

Ever since the discovery of the SolarWinds hack that affected tens of thousands of organizations around the world in 2020, supply chain compromises have remained at the forefront of the minds of security teams and continue to pose a significant threat to their business operations. 

Supply chain compromises can have far-reaching implications, from disrupting an organization’s daily operations, incurring huge financial and reputational damage, to affecting the critical infrastructure of entire countries. As such, it is essential for organizations to have effective security measures in place able to identify and halt these attacks at the earliest possible stage.

In March 2023 the 3CX Desktop application became the latest victim of a supply chain compromise dubbed as the “SmoothOperator” by SentinelOne. This application is used by over 600,000 companies worldwide and the customer list contains high-profile customers across a variety of industries [2]. The 3CX Desktop application is a Voice over Internet Protocol (VoIP) communication software for enterprises that allows for chats, video calls, and voice calls. [3] The 3CX installers for both Windows and macOS systems were affected by information stealing malware. Researchers were able to discern that threat actors also known as UNC 4736 related to financially motivated North Korean operators also known as AppleJeus were responsible for the supply chain compromise.  Researchers have also linked it to another supply chain compromise that occurred prior on the Trading Technologies X_TRADER platform, making this the first known cascading software supply chain compromise used to distribute malware on a wide scale and still be able to align operator interests. [3] Customer reports following the compromise began to surface about the 3CX software being picked up as malicious by several cybersecurity vendors such as CrowdStrike, SentinelOne, and Palo Alto Networks. [6] 

By leveraging integrations with other security vendors like CrowdStrike and SentinelOne, Darktrace DETECT™ was able to identify activity from the “SmoothOperator” across the customer base at multiple stages of the kill chain in March 2023. Darktrace RESPOND™ was then able to autonomously intervene against these emerging threats, preventing significant disruption to customer networks. 

Background on the first known cascading supply chain attack 

Initial Access

In April 2023, security researchers identified the initial target in this story was not the 3CX desktop application, rather, it was another software application called X_TRADER by Trading Technologies. [3] Trading Technologies is a provider that offers high-performance financial trading packages, allowing financial professionals to analyze and trade assets within the stock market more efficiently. Unfortunately, a compromise already existed in the supply chain for this organization. The X_TRADER installer, which had been retired in 2020, still had its code signing certificate set to expire in October 2022. This code signing certificate was exploited by attackers to digitally sign the malicious software. [3] It also inopportunely led to 3CX when an employee unknowingly downloaded a trojanized installer for the X_TRADER software from Trading Technologies prior to the certificate’s expiration. [4]. This compromise of 3CX via X_TRADER was the first case of a cascading supply chain attack reported on within the wider threat landscape. 

Persistence and Privilege Escalation 

Following these findings, researchers were able to identify the likely kill chain that occurred on Windows systems, beginning with the download of the 3CX DesktopApp installer that executed an executable (.exe) file before dropping two trojanized Data Link Libraries (DLLs) alongside a benign executable that was used to sideload malicious DLLs. These DLLs contained and used SIGFLIP and DAVESHELL; both publicly available projects. [3] In this case, the DLLs were used to decrypt using an RC4 key and load a payload into the memory of a compromised system. [3] SIGFLIP and DAVESHELL also extract and decrypt the modular backdoor named VEILEDSIGNAL, which also contains a command and control (C2) configuration. This malware allowed the North Korean threat operators to gain administrative control to the 3CX employee’s device. [3] This was followed by access to the employee’s corporate credentials, ultimately leading to access to 3CX systems. [4] 

Lateral Movement and C2 activity

Security researchers were also able to identify other malware families that were mainly utilized in the supply chain attack to move laterally within the 3CX environment, and allow for C2 communication [3], these malware families are detailed below:

  • TaxHaul: when executed it decrypts shellcode payload, observed by Mandiant to persist via DLL search-order hijacking.
  • Coldcat: complex downloader, which also beacons to a C2 infrastructure.
  • PoolRat: collects system information and executes commands. This is the malware that was found to affect macOS systems.
  • IconicStealer: served as a third stage payload on 3CX systems to steal data or information.

Furthermore, it was also reported early on by Kaspersky that a backdoor named Gopuram, routinely used by the North Korean threat actors Lazarus and typically used against cryptocurrency companies, was also used as a second stage payload on a limited number of 3CX’s customers compromised systems. [5]

3CX detections observed by Darktrace

CrowdStrike and SentinelOne, two of the major detection platforms with which Darktrace partners through security integrations, initially revealed that their platforms had identified the campaign appeared to be targeting 3CXDesktopApp customers in March 2023. 

At this time, Darktrace was also observing this activity and alerting customers to unusual behavior on their networks. [1][7] Darktrace DETECT identified activity related to the supply chain compromise primarily through host-level alerts associated with CrowdStrike and SentinelOne integrations, as well as model breaches related to lateral movement and C2 activity. 

Some of the activity related to the 3CX supply chain compromise that Darktrace detected was observed solely via integration models picking up executable and Microsoft Software Installer (msi) file downloads for the 3CXDesktopApp, suggesting the compromise likely was stopped at the endpoint device. 

CrowdStrike integration model breach identifying 3CXDesktopApp[.]exe as possible malware
Figure 1: CrowdStrike integration model breach identifying 3CXDesktopApp[.]exe as possible malware on March 30, 2023.
showcases the Model Breach Event Log for the CrowdStrike integration model breach
Figure 2: The above figure, showcases the Model Breach Event Log for the CrowdStrike integration model breach shown in Figure 1.

In another case highlighted in Figure 3 and 4, security platforms were associating 3CX as malicious. The device in these figures was observed downloading a 3CXDesktopApp executable followed by an msi file about an hour later. This pattern of activity correlates with the compromise process that had been on reported, where the “SmoothOperator” malware that affected 3CX systems was able to persist through DLL side-loading of malicious DLL files delivered with benign executable files, making it difficult for traditional security tools to detect. [2][3][7]

The activity in this case was detected by the DETECT integration model, ‘High Severity Integration Malware Detection’ and was later blocked by the Darktrace RESPOND/Network model, ‘Antigena Significant Anomaly from Client Block’ which applied the “Enforce Pattern of Life” action to intercept the malicious download that was taking place. Darktrace RESPOND uses AI to learn every devices normal pattern of life and act autonomously to enforce its normal activity. In this event, RESPOND would not only intercept the malicious download that was taking place on the device, but also not allow the device to significantly deviate from its normal pattern of activity.

The Model Breach Event log for the device displays the moment in which the SentinelOne integration model breached for the 3CXDesktopApp.exe file
Figure 3: The Model Breach Event log for the device displays the moment in which the SentinelOne integration model breached for the 3CXDesktopApp.exe file followed subsequently by the RESPOND model, ‘Antigena Significant Anomaly from Client Block’, on March 29, 2023.
Another ‘High Severity Integration Malware Detection’ breached
Figure 4: Another ‘High Severity Integration Malware Detection’ breached for the same device in Figure 3 approximately one hour later because of the msi file, 3CXDesktopApp-18.12.416.msi, which also led to the Darktrace RESPOND model, ‘Antigena Significant Anomaly from Client Block’, on March 29, 2023.

In a separate case, Darktrace also detected a device performing unusual SMB drive writes for the file ‘3CXDesktopApp-18.10.461.msi’. This breached the DETECT model ‘SMB Drive Write’. This model detects when a device starts writing files to another internal device it does not usually communicate with via the SMB protocol using the admin$ or drive shares.

This Model Breach Event log highlights the moment Darktrace captured the msi application file for the 3CXDesktopApp being transferred internally on this customer’s network
Figure 5: This Model Breach Event log highlights the moment Darktrace captured the msi application file for the 3CXDesktopApp being transferred internally on this customer’s network, this was picked up as new activity for the device on March 28, 2023. 

In a couple of other cases observed by Darktrace, connections detected were made from affected devices to 3CX compromise related endpoints. In Figure 6, the device in question was detected connecting to the endpoint, journalide[.]org. This breached the model, ‘Suspicious Self-Signed SSL’, which looks for connections being made to an endpoint with a self-signed SSL certificate which is designed to look legitimate, as self-signed certificates are often used in malware communication.

Model Breach Event log for connections to the 3CX C2 related endpoint
Figure 6: Model Breach Event log for connections to the 3CX C2 related endpoint, journalide[.]org, these connections breached the model Suspicious Self-Signed SSL on April 24, 2023.

On another Darktrace customer environment, a 3CX C2 endpoint, pbxphonenetwork[.]com, had already been added to the Watched Domains list around the time reports of the 3CX application software being malicious had been reported. The Watched Domains list allows Darktrace to detect if any device on the network makes connections to these domains with more scrutiny and breach a model for further visibility of threats on the network. Activity in this case was detected and subsequently blocked by a Darktrace RESPOND action, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443”, blocking the device from connecting to this 3CX C2 endpoints on the spot (see Figure 7). This activity subsequently breached the RESPOND model, ‘Antigena Watched Domain Block’. 

Figure 7: History log of the Darktrace RESPOND action applied to the device breaching the Darktrace RESPOND model, Antigena Watched Domain Block and applying the action, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443” on March 31, 2023.

Darktrace Coverage 

Utilizing integrations with Darktrace such as those with CrowdStrike and SentinelOne, Darktrace was able to detect and respond to activity identified as malicious 3CX activity by CrowdStrike and SentinelOne as seen in Figures 1, 2, 3, and 4. This activity breached the following Darktrace DETECT models: 

  • Integration / CrowdStrike Alert
  • Security Integration / High Severity Integration Malware Detection

Darktrace was also able to identify lateral movement activity such as in the case illustrated in Figure 5.

  • Compliance / SMB Drive Write

Lastly, C2 beaconing activity from malicious endpoints associated with the 3CX compromise was also detected as seen in Figure 6, this activity breached the following Darktrace DETECT model:

  • Anomalous Connection / Suspicious Self-Signed SSL

For customers with Darktrace RESPOND configured in autonomous response mode, Darktrace RESPOND models also breached to activity related to the 3CX supply chain compromise as seen in Figures 3, 4, and 7. Below are the models that breached and the following autonomous actions that were applied:

  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block, “Enforce pattern of life”
  • Antigena / Network / External Threat / Antigena Watched Domain Block, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443”

Conclusion 

The first known cascading supply chain compromise occurred inopportunely for 3CX but conveniently for UNC 4736 North Korean threat actors. This “SmoothOperator” compromise was detected by endpoint security platforms such as CrowdStrike who was at the cusp of this discovery when it became one of the first platforms to report on malicious activity related to the 3CX DesktopApp supply chain compromise.  

Although still novel at the time and largely without reported indicators of compromise, Darktrace was able to capture and identify activity related to the 3CX compromise across its customer base, as well as respond autonomously to contain it. Darktrace was able to amplify security integrations with CrowdStrike and SentinelOne, and via anomaly-based model breaches, contribute unique insights by highlighting activity in varied parts of the 3CX supply chain compromise kill chain. The “SmoothOperator” supply chain attack proves that the Darktrace suite of products, including DETECT and RESPOND, can not only act autonomously to identify and respond to novel threats, but also work with security integrations to further amplify intervention and prevent cyber disruption on customer networks. 

Credit to Nahisha Nobregas, SOC Analyst and Trent Kessler, SOC Analyst.

Appendices

MITRE ATT&CK Framework

Resource Development

  • T1588 Obtain Capabilities  
  • T1588.004 Digital Certificates
  • T1608 Stage Capabilities  
  • T1608.003 Install Digital Certificate

Initial Access

  • T1190 Exploit Public-Facing Application
  • T1195 Supply Chain Compromise  
  • T1195.002 Compromise Software Supply Chain

Persistence

  • T1574 Hijack Execution Flow
  • T1574.002 DLL Side-Loading

Privilege Escalation

  • T1055 Process Injection
  • T1574 Hijack Execution Flow  
  • T1574.002 DLL Side-Loading

Command and Control

  • T1071 Application Layer Protocol
  • T1071.001 Web Protocols
  • T1071.004 DNS  
  • T1105 Ingress Tool Transfer
  • T1573 Encrypted Channel

List of IOCs

C2 Hostnames

  • journalide[.]org
  • pbxphonenetwork[.]com

Likely C2 IP address

  • 89.45.67[.]160

References

  1. https://www.crowdstrike.com/blog/crowdstrike-detects-and-prevents-active-intrusion-campaign-targeting-3cxdesktopapp-customers/
  2. https://www.bleepingcomputer.com/news/security/3cx-confirms-north-korean-hackers-behind-supply-chain-attack/
  3. https://www.mandiant.com/resources/blog/3cx-software-supply-chain-compromise
  4. https://www.securityweek.com/cascading-supply-chain-attack-3cx-hacked-after-employee-downloaded-trojanized-app/
  5. https://securelist.com/gopuram-backdoor-deployed-through-3cx-supply-chain-attack/109344/
  6. https://www.bleepingcomputer.com/news/security/3cx-hack-caused-by-trading-software-supply-chain-attack/
  7. https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Nahisha Nobregas
SOC Analyst

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May 26, 2026

Journey of a Threat: How Multi-Layered AI Works in Darktrace / EMAIL

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Darktrace / EMAIL is an implementation of the Darktrace methodology – a multi-layered AI system built into a single product. As with other Darktrace products, Darktrace / EMAIL learns the expected behaviours of an organization and its employees to identify novel threats and anomalous activity.

The diagram below represents the architecture of Darktrace / EMAIL’s multi-layered AI: a structured visualization of how intelligence is built, step by step, from raw data to actionable insight. Each layer plays a distinct role, feeding into the next: collecting data, understanding behaviour, analysing intent, making decisions, and presenting clear outcomes.

It all starts with an email

In this blog, we’ll follow a malicious email as it passes through the Darktrace / EMAIL system, showing exactly what happens as it travels through each layer of the pyramid, from basic data extraction to AI-powered metric creation, and finally deciding on any autonomous actions.

Let’s take this example email. As an end-user, you can see that this is an obvious extortion attempt where an adversary is threatening legal action if money isn’t paid within 24 hours, but how does Darktrace figure that out?

Part 1: Data Gathering

Processing of an email begins on point-of-transit for all inbound, outbound, or lateral emails. The first step is to extract information directly. This includes taking information from the headers (such as sending and receiving addresses, sender IP address, routing, and authentication protocols), as well as extraction of raw HTML and CSS data from the email itself.

This directly extracted information only allows for immediate surface level analysis, such as identifying signature-based attacks (known malicious addresses / domains), but is insufficient for identifying novel threats, complex attacks, or potential email or vendor compromise. This is where Darktrace’s AI analysis shines.

In this example, the SPF, DKIM, and DMARC authentication all passed successfully, showing that even malicious emails can still bypass these signature-based checks. Even with this success, Darktrace will continue to analyse the email.

Diving deeper into the technical information, we can see further information extracted from the headers, including aggregations from the header information, historical calculations such as the frequency and volume of emails to and from a particular domain, and much more.

Part 2: Social Graphing

Social Graphing involves the analysis of sending and receiving behaviours of different mailboxes to create peer-groups. Mailboxes who often send and receive to and from the same mailboxes, or exhibit other correlated behaviours, will be clustered together using a collection of unsupervised AI clustering systems. These groups may represent uses in the same teams who perform similar activity, groups of external facing mailboxes which often receive unsolicited emails, or groups of VIP users (such as C-suite or executives).

Social graphing is an essential component of Darktrace’s pattern of life analysis. This clustering allows Darktrace to understand the responsibilities of individuals – for example, behaviours which are anomalous for one group of users may be completely expected of another group.

In our example, the email was sent to 3 different users within the organization. As part of the social graphing, an “Association Anomaly” is calculated which indicates the likelihood that these users would receive emails from this user or domain, based on historical patterns.

Part 3: Metric Calculation

Metrics are calculated for every email, representing more complex characteristics of an email which can’t be directly extracted. Darktrace / EMAIL features over 1000 unique metrics, calculated both algorithmically and using an ensemble of AI systems.

Algorithmically calculated (non-AI) metrics include further historical calculations, and counts of features such as code blocks, and hidden text, to name a few.

AI-driven metrics include Inducement Classification which uses Natural Language Processing to identify potential phishing, solicitation, or extortion attempts; Named Entity Recognition to identify PII and other sensitive data within an email to support Data Loss Prevention; and many more.

We can follow our example email through this process and view the outcome of these metric calculations. Looking at the language metrics for this email, we can see that our email has reported a high extortion inducement, along with identification of banking information and language indicating urgency.

Part 4: Evaluation and Combination Engine (models)

Once all metrics have been calculated for an email, it gets sent to an evaluation and combination engine where the metrics are compared against blocks of logic to determine if an email contains a threat. One key model which alerted for this example message was a model to tag and block extortion attempts.

Since our example email has a high inducement score for extortion, along the presence of a bitcoin wallet address in the message, this model alerts. When a model in the engine is activated, actions are taken – in this case adding a tag to the email to flag it as extortion in the console and hold the email to prevent it from reaching the end-user mailbox.

Part 5: Meta-Modelling and Actions

Once the models have been run, the actions are taken against the email. If the email hasn’t been blocked or held, this is the point where it will reach the end-user's mailbox.

In the Darktrace / EMAIL UI, all actions models which alerted for an email and actions taken as a result can be seen. At the top of this page, you can see the alert indicating an extortion attempt along with the action to hold the message.

Alongside this, a meta-classifier is used to calculate an overall anomaly score for each email, based on how much the email differs from the pattern of life for the user. The score of the email is boosted by any actions that have taken place.

Part 6: Campaign Clustering

All emails are passed through the Darktrace / EMAIL campaign clustering system. This system creates clusters based on related features within the emails to identify groups of emails with the same sender or intent.

In our case, the email was identified as part of a campaign, alongside other emails which were also identified as extortion attempts against a small group of recipients.

Email campaigns may have additional actions applied to them if the campaign is deemed malicious, and in this case, you can see that the autonomous response was to hold all emails in the campaign. This means that if an email manages to avoid being blocked in the evaluation and combination engine but gets identified as part of the campaign, the hold action will be applied to it retroactively.

Part 7: Cyber AI Analyst

Darktrace’s Cyber AI Analyst presents key information and anomaly indicators for each email, such as further information about authentication, specific metrics, or other identified anomalies and mismatches.

Cyber AI Analyst can also utilize data from Darktrace / EMAIL to enhance its investigation of incidents from other Darktrace products, correlating relevant information to build a fuller picture. More information about the Cyber AI Analyst is available in the Darktrace AI Arsenal.

Part 8: Data Presentation (UI)

Once all processing has taken place against the email, it is presented in the Darktrace / EMAIL UI. Here, members of the SOC team can investigate incidents and anomalies, interact with malicious emails to see why they were blocked, and much more.

Our email stands out here with its 100 anomaly score. Every email which passes through a Darktrace / EMAIL will undergo the same thorough and rigorous analysis to identify potential risks, apply autonomous actions where required, and will ultimately be assigned a score to be displayed here. By providing a single overall score in the UI, rather than presenting emails in full, Darktrace / EMAIL allows SOC teams to more easily identify which emails are most important to investigate, increasing efficiency and reducing alert fatigue.

Take the next step

Many email security tools on the market that claim to be AI-driven are in fact bolting AI onto attack-centric approaches, which rely on automating the identification of known threats. These approaches struggle, and will continue to struggle, with adapting to novel, AI-generated threats.

By analyzing every email within its deeply integrated, multi-layered AI system, Darktrace / EMAIL is able to identify the subtle threats that others miss. This depth not only improves detection accuracy, but enables confident, autonomous action, giving security teams clearer insight into AI outcomes and greater control while supporting users.

For a full deep dive into each stage of the AI system, check out the white paper: A Guide to the Multi-Layered AI in Darktrace / EMAIL

Learn more about securing AI in your enterprise.

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May 26, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

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Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

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Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

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Product Marketing Manager, Network Detection & Response
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