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August 2, 2023

Darktrace's Detection of Ransomware & Syssphinx

Read how Darktrace identified an attack technique by the threat group, Syssphinx. Learn how Darktrace's quick identification process can spot a threat.
Written by
Adam Potter
Senior Cyber Analyst
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
Adam Potter
Senior Cyber Analyst
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02
Aug 2023

Introduction

As the threat of costly cyber-attacks continues represent a real concern to security teams across the threat landscape, more and more organizations are strengthening their defenses with additional security tools to identify attacks and protect their networks. As a result, malicious actors are being forced to adapt their tactics, modify existing variants of malicious software, or utilize entirely new variants.  

Symantec recently released an article about Syssphinx, the financially motivated cyber threat group previously known for their point-of-sale attacks. Syssphinx attempts to deploy ransomware on customer networks via a modified version of their ‘Sardonic’ backdoor. Such activity highlights the ability of threat actors to alter the composition and presentation of payloads, tools, and tactics.

Darktrace recently detected some of the same indicators suggesting a likely Syssphinx compromise within the network of a customer trialing the Darktrace DETECT™ and RESPOND™ products. Despite the potential for variations in the construction of backdoors and payloads used by the group, Darktrace’s anomaly-based approach to threat detection allowed it to stitch together a detailed account of compromise activity and identify the malicious activity prior to disruptive events on the customer’s network.

What is Syssphinx?

Syssphinx is a notorious cyber threat entity known for its financially motivated compromises.  Also referred to as FIN8, Syssphinx has been observed as early as 2016 and is largely known to target private sector entities in the retail, hospitality, insurance, IT, and financial sectors.[1]

Although Syssphinx primarily began focusing on point-of-sale style attacks, the activity associated with the group has more recently incorporated ransomware variants into their intrusions in a potential bid to further extract funds from target organizations.[2]

Syssphinx Sardonic Backdoor

Given this gradual opportunistic incorporation of ransomware, it should not be surprising that Syssphinx has slowly expanded its repertoire of tools.  When primarily performing point-of-sale compromises, the group was known for its use of point-of-sale specific malwares including BadHatch, PoSlurp/PunchTrack, and PowerSniff/PunchBuggy/ShellTea.[3]

However, in a seeming response to updates in detection systems while using previous indicators of compromise (IoCs), Syssphinx began to modify its BadHatch malware.  This resulted in the use of a C++ derived backdoor known as “Sardonic”, which has the ability to aggregate host credentials, spawn additional command sessions, and deliver payloads to compromised devices via dynamic-link library (DLL).[4],[5]

Analysis of the latest version of Sardonic reveals further changes to the malware to elude detection. These shifts include the implementation of the backdoor in the C programming language, and additional over-the-network communication obfuscation techniques. [6]

During the post-exploitation phase, the group tends to rely on “living-off-the-land” tactics, whereby an attacker utilizes tools already present within the organization’s digital environment to avoid detection. Syssphinx seems to utilize system-native tools such as PowerShell and the Windows Management Instrumentation (WMI) interface.[7] It is also not uncommon to see Windows-based vulnerability exploits employed on compromised devices. This has been observed by researchers who have examined previous iterations of Syssphinx backdoors.[8] Syssphinx also appears to exhibit elements of strategic patience and discipline in its operations, with significant time gaps in operations noted by researchers. During this time, it appears likely that updates and tweaks were applied to Syssphinx payloads.

Compromise Details

In late April 2023, Darktrace identified an active compromise on the network of a prospective customer who was trialing Darktrace DETECT+RESPOND. The customer, a retailer in EMEA with hundreds of tracked devices, reached out to the Darktrace Analyst team via the Ask the Expert (ATE) service for support and further investigation, following the encryption of their server and backup data storage in an apparent ransomware attack. Although the encryption events fell outside Darktrace’s purview due to a limited set up of trial appliances, Darktrace was able to directly track early stages of the compromise before exfiltration and encryption events began. If a full deployment had been set up and RESPOND functionality had been configured in autonomous response mode, Darktrace may have helped mitigate such encryption events and would have aided in the early identification of this ransomware attack.

Initial Intrusion and Establishment of Command and Control (C2) Infrastructure

As noted by security researchers, Syssphinx largely relies on social engineering and phishing emails to deliver its backdoor payloads. As there were no Darktrace/Email™ products deployed for this customer, it would be difficult to directly observe the exact time and manner of initial payload delivery related to this compromise. This is compounded by the fact that the customer had only recently began using Darktrace’s products during their trial period. Given the penchant for patience and delay by Syssphinx, it is possible that the intrusion began well before Darktrace had visibility of the organization’s network.

However, beginning on April 30, 2023, at 07:17:31 UTC, Darktrace observed the domain controller dc01.corp.XXXX  making repeated SSL connections to the endpoint 173-44-141-47[.]nip[.]io. In addition to the multiple open-source intelligence (OSINT) flags for this endpoint, the construction of the domain parallels that of the initial domain used to deliver a backdoor, as noted by Symantec in their analysis (37-10-71-215[.]nip[.]io). This activity likely represented the initial beaconing being performed by the compromised device. Additionally, an elevated level of incoming external data over port 443 was observed during this time, which may be associated with the delivery of the Sardonic backdoor payload. Given the unusual use of port 443 to perform SSH connections later seen in the kill chain of this attack, this activity could also parallel the employment of embedded backdoor payloads seen in the latest iteration of the Sardonic backdoor noted by Symantec.

Figure 1: Graph of the incoming external data surrounding the time of the initial establishment of command and control communication for the domain controller. As seen in the graph, the spike in incoming external data during this time may parallel the delivery of Syssphinx Sardonic backdoor.

Regardless, the domain controller proceeded to make repeated connections over port 443 to the noted domain.

Figure 2: Breach event log for the domain controller making repeated connections over port 443 to the rare external destination endpoint in constitute the establishment of C2 communication.

Internal Reconnaissance/Privilege Escalation

Following the establishment of C2 communication, Darktrace detected numerous elements of internal reconnaissance. On Apr 30, 2023, at 22:06:26 UTC, the desktop device desktop_02.corp.XXXX proceeded to perform more than 100 DRSGetNCChanges requests to the aforementioned domain controller. These commands, which are typically implemented over the RPC protocol on the DRSUAPI interface, are frequently utilized in Active Directory sync attacks to copy Active Directory information from domain controllers. Such activity, when not performed by new domain controllers to sync Active Directory contents, can indicate malicious domain or user enumeration, credential compromise or Active Directory enumeration.

Although the affected device made these requests to the previously noted domain controller, which was already compromised, such activity may have further enabled the compromise by allowing the threat actor to transfer these details to a more easily manageable device.

The device performing these DRSGetNCChanges requests would later be seen performing lateral movement activity and making connections to malicious endpoints.

Figure 3: Breach log highlighting the DRS operations performed by the corporate device to the destination domain controller. Such activity is rarely authorized for devices not tagged as administrative or as domain controllers.

Execution and Lateral Movement

At 23:09:53 UTC on April 30, 2023, the original domain server proceeded to make multiple uncommon WMI calls to a destination server on the same subnet (server01.corp.XXXX). Specifically, the device was observed making multiple RPC calls to IWbem endpoints on the server, which included login and ExecMethod (method execution) commands on the destination device. This destination device later proceeded to conduct additional beaconing activity to C2 endpoints and exfiltrate data.

Figure 4: Breach log for the domain controller performing WMI commands to the destination server during the lateral movement phase of the breach.

Similarly, beginning on May 1, 2023, at 00:11:09 UTC, the device desktop_02.corp.XXXX made multiple WMI requests to two additional devices, one server and one desktop, within the same subnet as the original domain controller. During this time, desktop_02.corp.XXXX  also utilized SMBv1, an outdated and typically non-compliant version communication protocol, to write the file rclone.exe to the same two destination devices. Rclone.exe, and its accompanying bat file, is a command-line tool developed by IT provider Rclone, to perform file management tasks. During this time, Darktrace also observed the device reading and deleting an unexpected numeric file on the ADMIN$ of the destination server, which may represent additional defense evasion techniques and tool staging.

Figure 5: Event log highlighting the writing of rclone.exe using the outdated SMBv1 communication protocol.
Figure 6: SMB logs indicating the reading and deletion of numeric string files on ADMIN$ shares of the destination devices during the time of the rclone.exe SMB writes. Such activity may be associated with tool staging and could indicate potential defense evasion techniques.

Given that the net loader sample analyzed by Symantec injects the backdoor into a WmiPrvSE.exe process, the use of WMI operations is not unexpected. Employment of WMI also correlates with the previously mentioned “living-off-the-land” tactics, as WMI services are commonly used for regular network and system administration purposes. Moreover, the staging of rclone.exe, a legitimate file management tool, for data exfiltration underscores attempts to blend into existing and expected network traffic and remain undetected on the customer’s network.

Data Exfiltration and Impact

Initial stages of data exfiltration actually began prior to some of the lateral movement events described above. On April 30, 2023, 23:09:47 the device server01.corp.XXXX, transferred nearly 11 GB of data to 173.44[.]141[.]47, as well as to the rare external IP address 170.130[.]55[.]77, which appears to have served as the main exfiltration destination during this compromise. Furthermore, the host made repeated connections to the same external IP associated with the initial suspicious beaconing activity (173.44[.]141[.]47) over SSL.

While the data exfiltration event unfolded, the device, server01.corp.XXXX, made multiple HTTP requests to 37.10[.]71[.]215, which featured URIs requesting the rclone.exe and rclone.bat files. This IP address was directly involved in the sample analyzed by Symantec. Furthermore, one of the devices that received the SMB file writes of rclone.exe and the WMI commands from desktop_02.corp.XXXX also performed SSL beaconing to endpoints associated with the compromise.

Between 01:20:45 - 03:31:41 UTC on May 1, 2023, a Darktrace detected a series of devices on the network performing a repeated pattern of activity, namely external connectivity followed by suspicious file downloads and external data transfer operations. Specifically, each affected device made multiple HTTP requests to 37.10[.]71[.]215 for rclone files. The devices proceeded to download the executable and/or binary files, and then transfer large amounts of data to the aforementioned endpoints, 170.130[.]55[.]77 and or 173-44-141-47[.]nip[.]io. Although the devices involved in data exfiltration utilized port 443 as a destination port, the connections actually used the SSH protocol. Darktrace recognized this behavior as unusual as port 443 is typically associated with the SSL protocol, while port 22 is reserved for SSH. Therefore, this activity may represent the threat actor’s attempts to remain undetected by security tools.

This unexpected use of SSH over port 443 also correlates with the descriptions of the new Sardonic backdoor according to threat researchers. Further beaconing and exfiltration activity was performed by an additional host one day later whereby the device made suspicious repeated connections to the aforementioned external hosts.

Figure 7: Connection details highlighting the use of port 443 for SSH connections during the exfiltration events.

In total, nine separate devices were involved in this pattern of activity. Five of these devices were labeled as ‘administrative’ devices according to their hostnames. Over the course of the entire exfiltration event, the attackers exfiltrated almost 61 GB of data from the organization’s environment.

Figure 8: Graph showing the levels of external data transfer from a breach device for one day on either side of the breach time. There is a large spike in such activity during the time of the breach that underscores the exfiltration events.

In addition to the individual anomaly detections by DETECT, Darktrace’s Cyber AI Analyst™ launched an autonomous investigation into the unusual behavior carried out by affected devices, connecting and collating multiple security events into one AI Analyst Incident. AI Analyst ensures that Darktrace can recognize and link the individual steps of a wider attack, rather than just identifying isolated incidents. While traditional security tools may mistake individual breaches as standalone activity, Darktrace’s AI allows it to provide unparalleled visibility over emerging attacks and their kill chains. Furthermore, Cyber AI Analyst’s instant autonomous investigations help to save customer security teams invaluable time in triaging incidents in comparison with human teams who would have to commit precious time and resources to conduct similar pattern analysis.

In this specific case, AI Analyst identified 44 separate security events from 18 different devices and was able to tie them together into one incident. The events that made up this AI Analyst Incident included:

  • Possible SSL Command and Control
  • Possible HTTP Command and Control
  • Unusual Repeated Connections
  • Suspicious Directory Replication ServiceActivity
  • Device / New or Uncommon WMI Activity
  • SMB Write of Suspicious File
  • Suspicious File Download
  • Unusual External Data Transfer
  • Unusual External Data Transfer to MultipleRelated Endpoints
Figure 9: Cyber AI Incident log highlighting multiple unusual anomalies and connecting them into one incident.

Had Darktrace RESPOND been enabled in autonomous response mode on the network of this prospective customer, it would have been able to take rapid mitigative action to block the malicious external connections used for C2 communication and subsequent data exfiltration, ideally halting the attack at this stage. As previously discussed, the limited network configuration of this trial customer meant that the encryption events unfortunately took place outside of Darktrace’s scope. When fully configured on a customer environment, Darktrace DETECT can identify such encryption attempts as soon as they occur. Darktrace RESPOND, in turn, would be able to immediately intervene by applying preventative actions like blocking internal connections that may represent file encryption, or limiting potentially compromised devices to a previously established pattern of life, ensuring they cannot carry out any suspicious activity.

Conclusion

Despite the limitations posed by the customer’s trial configuration, Darktrace demonstrated its ability to detect malicious activity associated with Syssphinx and track it across multiple stages of the kill chain.

Darktrace’s ability to identify the early stages of a compromise and various steps of the kill chain, highlights the necessity for machine learning-enabled, anomaly-based detection. In the face of threats such as Syssphinx, that exhibit the propensity to recast backdoor payloads and incorporate on “living-off-the-land” tactics, signatures and rules-based detection may not prove as effective. While Syssphinx and other threat groups will continue to adopt new tools, methods, and techniques, Darktrace’s Self-Learning AI is uniquely positioned to meet the challenge of such threats.

Appendix

DETECT Model Breaches Observed

•      Anomalous Server Activity / Anomalous External Activity from Critical Network Device

•      Anomalous Connection / Anomalous DRSGetNCChanges Operation

•      Device / New or Uncommon WMI Activity

•      Compliance / SMB Drive Write

•      Anomalous Connection / Data Sent to Rare Domain

•      Anomalous Connection / Uncommon 1 GiB Outbound

•      Unusual Activity / Unusual External Data Transfer

•      Unusual Activity / Unusual External Data to New Endpoints

•      Compliance / SSH to Rare External Destination

•      Anomalous Connection / Unusual SMB Version 1 Connectivity

•      Anomalous File / EXE from Rare External Location

•      Anomalous File / Script from Rare External Location

•      Compromise / Suspicious File and C2

•      Device / Initial Breach Chain Compromise

‍

AI Analyst Incidents Observed

•      Possible SSL Command and Control

•      Possible HTTP Command and Control

•      Unusual Repeated Connections

•      Suspicious Directory Replication Service Activity

•      Device / New or Uncommon WMI Activity

•      SMB Write of Suspicious File

•      Suspicious File Download

•      Unusual External Data Transfer

•      Unusual External Data Transfer to Multiple Related Endpoints

IoCs

IoC - Type - Description

37.10[.]71[.]215 – IP – C2 + payload endpoint

173-44-141-47[.]nip[.]io – Hostname – C2 – payload

173.44[.]141[.]47 – IP – C2 + potential payload

170.130[.]55[.]77 – IP – Data exfiltration endpoint

Rclone.exe – Exe File – Common data tool

Rclone.bat – Script file – Common data tool

MITRE ATT&CK Mapping

Command and Control

T1071 - Application Layer Protocol

T1071.001 – Web protocols

T1573 – Encrypted channels

T1573.001 – Symmetric encryption

T1573.002 – Asymmetric encryption

T1571 – Non-standard port

T1105 – Ingress tool transfer

Execution

T1047 – Windows Management Instrumentation

Credential Access‍

T1003 – OS Credential Dumping

T1003.006 – DCSync

Lateral Movement

T1570 – Lateral Tool Transfer

T1021 - Remote Services

T1021.002 - SMB/Windows Admin Shares

T1021.006 – Windows Remote Management

Exfiltration

T1048 - Exfiltration Over Alternative Protocol

T1048.001 - Exfiltration Over Symmetric Encrypted Non-C2 Protocol

T1048.002 - Exfiltration Over Symmetric Encrypted Non-C2 Protocol

T1041 - Exfiltration Over C2 Channel

References

[1] https://cyberscoop.com/syssphinx-cybercrime-ransomware/

[2] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/Syssphinx-FIN8-backdoor

[3] https://www.bleepingcomputer.com/news/security/fin8-deploys-alphv-ransomware-using-sardonic-malware-variant/

[4] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/Syssphinx-FIN8-backdoor

[5] https://thehackernews.com/2023/07/fin8-group-using-modified-sardonic.html

[6] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/Syssphinx-FIN8-backdoor

[7] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/Syssphinx-FIN8-backdoor

[8] https://www.mandiant.com/resources/blog/windows-zero-day-payment-cards

執筆者
Adam Potter
Senior Cyber Analyst
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
Adam Potter
Senior Cyber 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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Jamie Bali
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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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About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response
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