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September 18, 2024

FortiClient EMS Exploited: Attack Chain & Post Exploitation Tactics

Read about the methods used to exploit FortiClient EMS and the critical post-exploitation tactics that affect cybersecurity defenses.
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
Emily Megan Lim
Cyber Analyst
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18
Sep 2024

Cyber attacks on internet-facing systems

In the first half of 2024, the Darktrace Threat Research team observed multiple campaigns of threat actors targeting vulnerabilities in internet-facing systems, including Ivanti CS/PS appliances, Palo Alto firewall devices, and TeamCity on-premises.

These systems, which are exposed to the internet, are often targeted by threat actors to gain initial access to a network. They are constantly being scanned for vulnerabilities, known or unknown, by opportunistic actors hoping to exploit gaps in security. Unfortunately, this exposure remains a significant blind spot for many security teams, as monitoring edge infrastructure can be particularly challenging due to its distributed nature and the sheer volume of external traffic it processes.

In this blog, we discuss a vulnerability that was exploited in Fortinet’s FortiClient Endpoint Management Server (EMS) and the post-exploitation activity that Darktrace observed across multiple customer environments.

What is FortiClient EMS?

FortiClient is typically used for endpoint security, providing features such as virtual private networks (VPN), malware protection, and web filtering. The FortiClient EMS is a centralized platform used by administrators to enforce security policies and manage endpoint compliance. As endpoints are remote and distributed across various locations, the EMS needs to be accessible over the internet.

However, being exposed to the internet presents significant security risks, and exploiting vulnerabilities in the system may give an attacker unauthorized access. From there, they could conduct further malicious activities such as reconnaissance, establishing command-and-control (C2), moving laterally across the network, and accessing sensitive data.

CVE-2023-48788

CVE-2023-48788 is a critical SQL injection vulnerability in FortiClient EMS that can allow an attacker to gain unauthorized access to the system. It stems from improper neutralization of special elements used in SQL commands, which allows attackers to exploit the system through specially crafted requests, potentially leading to Remote Code Execution (RCE) [1]. This critical vulnerability was given a CVSS score of 9.8 and can be exploited without authentication.

The affected versions of FortiClient EMS include:

  • FortiClient EMS 7.2.0 to 7.2.2 (fixed in 7.2.3)
  • FortiClient EMS 7.0.1 to 7.0.10 (fixed in 7.0.11)

The vulnerability was publicly disclosed on March 12, 2024, and an exploit proof of concept was released by Horizon3.ai on March 21 [2]. Starting from March 24, almost two weeks after the initial disclosure, Darktrace began to observe at least six instances where the FortiClient EMS vulnerability had likely been exploited on customer networks. Seemingly exploited devices in multiple customer environments were observed performing anomalous activities, including the installation of Remote Monitoring and Management (RMM) tools, which was also reported by other security vendors around the same time [3].

Darktrace’s Coverage

Initial Access

To understand how the vulnerability can be exploited to gain initial access, we first need to explain some components of the FortiClient EMS:

  • The service FmcDaemon.exe is used for communication between the EMS and enrolled endpoint clients. It listens on port 8013 for incoming client connections.
  • Incoming requests are then sent to FCTDas.exe, which translates requests from other server components into SQL requests. This service interacts with the Microsoft SQL database.
  • Endpoint clients communicate with the FmcDaemon on the server on port 8013 by default.

Therefore, an SQL injection attack can be performed by crafting a malicious payload and sending it over port 8013 to the server. To carry out RCE, an attacker may send further SQL statements to enable and use the xp_cmdshell functionality of the Microsoft SQL server [2].

Shortly before post-exploitation activity began, Darktrace had observed incoming connections to some of the FortiClient EMS devices over port 8013 from the external IPs 77.246.103[.]110, 88.130.150[.]101, and 45.155.141[.]219. This likely represented the threat actors sending an SQL injection payload over port 8013 to the EMS device to validate the exploit.

Establish C2

After exploiting the vulnerability and gaining access to an EMS device on one customer network, two additional devices were seen with HTTP POST requests to 77.246.103[.]110 and 212.113.106[.]100 with a new PowerShell user agent.

Interestingly, the IP 212.113.106[.]100 has been observed in various other campaigns where threat actors have also targeted internet-facing systems and exploited other vulnerabilities. Open-source intelligence (OSINT) suggests that this indicator of compromise (IoC) is related to the Sliver C2 framework and has been used by threat actors such as APT28 (Fancy Bear) and APT29 (Cozy Bear) [4].

Unusual file downloads were also observed on four devices, including:

  • “SETUP.MSI” from 212.32.243[.]25 and 89.149.200[.]91 with a cURL user agent
  • “setup.msi” from 212.113.106[.]100 with a Windows Installer user agent
  • “run.zip” from 95.181.173[.]172 with a PowerShell user agent

The .msi files would typically contain the RMM tools Atera or ScreenConnect [5]. By installing RMM tools for C2, attackers can leverage their wide range of functionalities to carry out various tasks, such as file transfers, without the need to install additional tools. As RMM tools are designed to maintain a stable connection to remote systems, they may also allow the attackers to ensure persistent access to the compromised systems.

A scan of the endpoint 95.181.173[.]172 shows various other files such as “RunSchedulerTask.ps1” and “anydesk.exe” being hosted.

Screenshot of the endpoint 95.181.173[.]172 hosting various files [6].
Figure 1: Screenshot of the endpoint 95.181.173[.]172 hosting various files [6].

Shortly after these unusual file downloads, many of the devices were also seen with usage of RMM tools such as Splashtop, Atera, and AnyDesk. The devices were seen connecting to the following endpoints:

  • *[.]relay.splashtop[.]com
  • agent-api[.]atera[.]com
  • api[.]playanext[.]com with user agent AnyDesk/8.0.9

RMM tools have a wide range of legitimate capabilities that allow IT administrators to remotely manage endpoints. However, they can also be repurposed for malicious activities, allowing threat actors to maintain persistent access to systems, execute commands remotely, and even exfiltrate data. As the use of RMM tools can be legitimate, they offer threat actors a way to perform malicious activities while blending into normal business operations, which could evade detection by human analysts or traditional security tools.

One device was also seen making repeated SSL connections to a self-signed endpoint “azure-documents[.]com” (104.168.140[.]84) and further HTTP POSTs to “serv1[.]api[.]9hits[.]com/we/session” (128.199.207[.]131). Although the contents of these connections were encrypted, they were likely additional infrastructure used for C2 in addition to the RMM tools that were used. Self-signed certificates may also be used by an attacker to encrypt C2 communications.

Internal Reconnaissance

Following the exploit, two of the compromised devices then started to conduct internal reconnaissance activity. The following figure shows a spike in the number of internal connections made by one of the compromised devices on the customer’s environment, which typically indicates a network scan.

Advanced Search results of internal connections made an affected device.
Figure 2: Advanced Search results of internal connections made an affected device.

Reconnaissance tools such as Advanced Port Scanner (“www[.]advanced-port-scanner[.]com”) and Nmap were also seen being used by one of the devices to conduct scanning activities. Nmap is a network scanning tool commonly used by security teams for legitimate purposes like network diagnostics and vulnerability scanning. However, it can also be abused by threat actors to perform network reconnaissance, a technique known as Living off the Land (LotL). This not only reduces the need for custom or external tools but also reduces the risk of exposure, as the use of a legitimate tool in the network is unlikely to raise suspicion.

Privilege Escalation

In another affected customer network, the threat actor’s attempt to escalate their privileges was also observed, as a FortiClient EMS device was seen with an unusually large number of SMB/NTLM login failures, indicative of brute force activity. This attempt was successful, and the device was later seen authenticating with the credential “administrator”.

Figure 3: Advanced Search results of NTLM (top) and SMB (bottom) login failures.

Lateral Movement

After escalating privileges, attempts to move laterally throughout the same network were seen. One device was seen transferring the file “PSEXESVC.exe” to another device over SMB. This file is associated with PsExec, a command-line tool that allows for remote execution on other systems.

The threat actor was also observed leveraging the DCE-RPC protocol to move laterally within the network. Devices were seen with activity such as an increase in new RPC services, unusual requests to the SVCCTL endpoint, and the execution of WMI commands. The DCE-RPC protocol is typically used to facilitate communication between services on different systems and can allow one system to request services or execute commands on another.

These are further examples of LotL techniques used by threat actors exploiting CVE-2023-48788, as PsExec and the DCE-RPC protocol are often also used for legitimate administrative operations.

Accomplish Mission

In most cases, the threat actor’s end goal was not clearly observed. However, Darktrace did detect one instance where an unusually large volume of data had been uploaded to “put[.]io”, a cloud storage service, indicating that the end goal of the threat actor had been to steal potentially sensitive data.

In a recent investigation of a Medusa ransomware incident that took place in July 2024, Darktrace’s Threat Research team found that initial access to the environment had likely been gained through a FortiClient EMS device. An incoming connection from 209.15.71[.]121 over port 8013 was seen, suggesting that CVE-2023-48788 had been exploited. The device had been compromised almost three weeks before the ransomware was actually deployed, eventually resulting in the encryption of files.

Mitigating risk with proactive exposure management and real-time detection

Threat actors have continued to exploit unpatched vulnerabilities in internet-facing systems to gain initial access to a network. This highlights the importance of addressing and patching vulnerabilities as soon as they are disclosed and a fix is released. However, due to the rapid nature of exploitation, this may not always be enough. Furthermore, threat actors may even be exploiting vulnerabilities that are not yet publicly known.

As the end goals for a threat actor can differ – from data exfiltration to deploying ransomware – the post-exploitation behavior can also vary from actor to actor. However, AI security tools such as Darktrace / NETWORK can help identify and alert for post-exploitation behavior based on abnormal activity seen in the network environment.

Despite CVE-2023-48788 having been publicly disclosed and fixed in March, it appears that multiple threat actors, such as the Medusa ransomware group, have continued to exploit the vulnerability on unpatched systems. With new vulnerabilities being disclosed almost every other day, security teams may find it challenging continuously patch their systems.

As such, Darktrace / Proactive Exposure Management could also alleviate the workload of security teams by helping them identify and prioritize the most critical vulnerabilities in their network.

Insights from Darktrace’s First 6: Half-year threat report for 2024

First 6: half year threat report darktrace screenshot

Darktrace’s First 6: Half-Year Threat Report 2024 highlights the latest attack trends and key threats observed by the Darktrace Threat Research team in the first six months of 2024.

  • Focuses on anomaly detection and behavioral analysis to identify threats
  • Maps mitigated cases to known, publicly attributed threats for deeper context
  • Offers guidance on improving security posture to defend against persistent threats

Appendices

Credit to Emily Megan Lim (Cyber Security Analyst) and Ryan Traill (Threat Content Lead)

References

[1] https://nvd.nist.gov/vuln/detail/CVE-2023-48788

[2] https://www.horizon3.ai/attack-research/attack-blogs/cve-2023-48788-fortinet-forticlientems-sql-injection-deep-dive/

[3] https://redcanary.com/blog/threat-intelligence/cve-2023-48788/

[4] https://www.fortinet.com/blog/threat-research/teamcity-intrusion-saga-apt29-suspected-exploiting-cve-2023-42793

[5] https://redcanary.com/blog/threat-intelligence/cve-2023-48788/

[6] https://urlscan.io/result/3678b9e2-ad61-4719-bcef-b19cadcdd929/

List of IoCs

IoC - Type - Description + Confidence

  • 212.32.243[.]25/SETUP.MSI - URL - Payload
  • 89.149.200[.]9/SETUP.MSI - URL - Payload
  • 212.113.106[.]100/setup.msi - URL - Payload
  • 95.181.173[.]172/run.zip - URL - Payload
  • serv1[.]api[.]9hits[.]com - Domain - Likely C2 endpoint
  • 128.199.207[.]131 - IP - Likely C2 endpoint
  • azure-documents[.]com - Domain - C2 endpoint
  • 104.168.140[.]84 - IP - C2 endpoint
  • 77.246.103[.]110 - IP - Likely C2 endpoint
  • 212.113.106[.]100 - IP - C2 endpoint

Darktrace Model Detections

Anomalous Connection / Callback on Web Facing Device

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Posting HTTP to IP Without Hostname

Anomalous Connection / Powershell to Rare External

Anomalous Connection / Rare External SSL Self-Signed

Anomalous Connection / Suspicious Self-Signed SSL

Anomalous Server Activity / Rare External from Server

Anomalous Server Activity / New User Agent from Internet Facing System

Anomalous Server Activity / Server Activity on New Non-Standard Port - External

Compliance / Remote Management Tool On Server

Device / New User Agent

Device / New PowerShell User Agent

Device / Attack and Recon Tools

Device / ICMP Address Scan

Device / Network Range Scan

Device / Network Scan

Device / RDP Scan

Device / Suspicious SMB Scanning Activity

Anomalous Connection / Multiple SMB Admin Session

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / Unusual Admin SMB Session

Device / Increase in New RPC Services

Device / Multiple Lateral Movement Breaches

Device / New or Uncommon WMI Activity

Device / New or Unusual Remote Command Execution

Device / SMB Lateral Movement

Device / Possible SMB/NTLM Brute Force

Unusual Activity / Successful Admin Brute-Force Activity

User / New Admin Credentials on Server

Unusual Activity / Enhanced Unusual External Data Transfer

Unusual Activity / Unusual External Data Transfer

Unusual Activity / Unusual External Data to New Endpoint

Device / Large Number of Model Breaches

Device / Large Number of Model Breaches from Critical Network Device

MITRE ATT&CK Mapping

Tactic – ID: Technique

Initial Access – T1190: Exploit Public-Facing Application

Resource Development – T1587.003: Develop Capabilities: Digital Certificates

Resource Development – T1608.003: Stage Capabilities: Install Digital Certificate

Command and Control – T1071.001: Application Layer Protocol: Web Protocols

Command and Control – T1219: Remote Access Software

Execution – T1059.001: Command and Scripting Interpreter: PowerShell

Reconnaissance – T1595: Active Scanning

Reconnaissance – T1590.005: Gather Victim Network Information: IP Addresses

Discovery – T1046: Network Service Discovery

Credential Access – T1110: Brute Force

Defense Evasion,Initial Access,Persistence,Privilege Escalation – T1078: Valid Accounts

Lateral Movement – T1021.002: Remote Services: SMB/Windows Admin Shares

Lateral Movement – T1021.003: Remote Services: Distributed Component Object Model

Execution – T1569.002: System Services: Service Execution

Execution – T1047: Windows Management Instrumentation

Exfiltration – T1041: Exfiltration Over C2 Channel

Exfiltration – T1567.002: Exfiltration Over Web Service: Exfiltration to Cloud Storage

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
Emily Megan Lim
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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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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