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October 9, 2024

How Darktrace won an email security trial by learning the business, not the breach

Discover how Darktrace identified a sophisticated business email compromise (BEC) attack to successfully acquire a prospective customer in a trial alongside two other email security vendors. This case demonstrates the clear differentiator of true unsupervised machine learning applied to the right use cases, compared to miscellaneous vendor hype around AI.
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
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Django Beek
Solutions Engineer
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09
Oct 2024

Recently, Darktrace ran a customer trial of our email security product for a leading European infrastructure operator looking to upgrade its email protection.

During this prospective customer trial, Darktrace encountered several security incidents that penetrated existing security layers. Two of these incidents were Business Email Compromise (BEC) attacks, which we’re going to take a closer look at here.  

Darktrace was deployed for a trial at the same time as two other email security vendors, who were also being evaluated by the prospective customer. Darktrace’s superior detection of threats in this trial laid the groundwork for the respective company to choose our product.

Let’s dig into some of the elements of this Darktrace tech win and how they came to light during this trial.

Why truly intelligent AI starts learning from scratch

Darktrace’s detection capabilities are powered by true unsupervised machine learning, which detects anomalous activity from its ever-evolving understanding of normal for every unique environment. Consequently, it learns every business from the beginning, training on an organization’s data to understand normal for its users, devices, assets and the millions of connections between them.  

This learning period takes around a week, during which the AI hones its understanding of the business to a precise degree. At this stage, the system may produce some noise or lack precision, but this is a testament to our unsupervised machine learning. Unlike solutions that promise faster results by relying on preset assumptions, our AI takes the necessary time to learn from scratch, ensuring a deeper understanding and increasingly accurate detection over time.

Real threats detected by Darktrace

Attack 1: Supply chain attack

BEC and supply chain attacks are notoriously difficult to detect, as they take advantage of established, trusted senders.  

This attack came from a legitimate server via a known supplier with which the prospective customer had active and ongoing communication. Using the compromised account, the attacker didn’t just send out randomized spam, they crafted four sophisticated social engineering emails with the aim of soliciting users to click on a link – directly tapping into existing conversations. Darktrace / EMAIL was configured in passive mode during this trial; it would otherwise have held the emails before they arrived in the inbox. Luckily in this instance, one user reported the email to the CISO before any other users clicked the link. Upon investigation, the link contained timed ransomware detonation.  

Darktrace was the only vendor that caught any of these four emails. Our unique behavioral AI approach enables Darktrace / EMAIL to protect customers from even the most sophisticated attacks that abuse prior trust and relationships.

How did Darktrace catch this attack that other vendors missed?

With traditional email security, security teams have been obliged to allow entire organizations to eliminate false positives – on the premise that it’s easier to make a broad decision based on an entire known domain and assume that potential risk of a supply chain attack.

By contrast, Darktrace adopts a zero trust mentality, analyzing every email to understand whether communication that has previously been safe remains safe. That’s why Darktrace is uniquely positioned to detect BEC, based on its deep learning of internal and external users. Because it creates individual profiles for every account, group and business composed of multiple signals, it can detect deviations in their communication patterns based on the context and content of each message. We think of this as the ‘self-learning’ vs ‘learning the breach’ differentiator.

Fig 1: Darktrace analysis of one of four malicious emails sent by the trusted supplier. It gives it an anomaly score of 100, despite it being from a known correspondent with a known domain relationship and moderate mailing history.

If set in autonomous mode where it can apply actions, Darktrace / EMAIL would have quarantined all four emails. Using machine learning indicators such as ‘Inducement Shift’ and ‘General Behavioral Anomaly’, it deemed the four emails ‘Out of Character’. It also identified the link as highly likely to be phishing, based purely on its context. These indicators are critical because the link itself belonged to a widely used legitimate domain, leveraging their established internet reputation to appear safe.  

Around an hour later the supplier regained control of the account and sent a legitimate email alerting a wide distribution list to the phishing emails sent. Darktrace was able to discern the previously sent malicious emails from the current legitimate emails and allowed these emails through. Compared to other vendors that have a static understanding of malicious which needs to be updated (in cases like this, once a supplier is de-compromised), Darktrace’s deep understanding of external entities enables further nuance and precision in determining good from bad.

Fig 2: Darktrace let through four emails (subject line: Virus E-Mail) from the supplier once they had regained control of the compromised account, with a limited anomaly score despite having held the previous malicious emails. If any actions had been taken a red icon would show on the right-hand side – in this instance Darktrace did not take action and let the emails through.

Attack 2: Microsoft 365 account takeover

As part of building behavioral profiles of every email user, Darktrace analyzes their wider account activity. Account activity, such as unusual login patterns and administrative activity, is a key variable to detect account compromise before malicious activity occurs, but it also feeds into Darktrace’s understanding of which emails should belong in every user’s inbox.  

When the customer experienced an account compromise on day two of the trial, Darktrace began an investigation and was able to provide the full breakdown and scope of the incident.

The account was compromised via an email, which Darktrace would have blocked if it had been deployed autonomously at the time. Once the account had been compromised, detection details included:

  • Unusual Login and Account Update
  • Multiple Unusual External Sources for SaaS Credential
  • Unusual Activity Block
  • Login From Rare Endpoint While User is Active
Fig 3: Darktrace flagged the following indicators of compromise that deviated from normal behavior for the user in question, signaling an account takeover

With Darktrace / EMAIL, every user is analyzed for behavioral signals including authentication and configuration activity. Here the unusual login, credential input and rare endpoint were all clear signals a compromised account, contextualized against what is normal for that employee. Because Darktrace isn’t looking at email security merely from the perspective of the inbox. It constantly reevaluates the identity of each individual, group and organization (as defined by their behavioral signals), to determine precisely what belongs in the inbox and what doesn’t.  

In this instance, Darktrace / EMAIL would have blocked the incident were it not deployed in passive mode. In the initial intrusion it would have blocked the compromising email. And once the account was compromised, it would have taken direct blocking actions on the account based on the anomalous activity it detected, providing an extra layer of defense beyond the inbox.  

Account takeover protection is always part of Darktrace / EMAIL, which can be extended to fully cover Microsoft 365 SaaS with Darktrace / IDENTITY. By bringing SaaS activity into scope, security teams also benefit from an extended set of use cases including compliance and resource management.

Why this customer committed to Darktrace / EMAIL

“Darktrace was the only AI vendor that showed learning,” – CISO, Trial Customer

Throughout this trial, Darktrace evolved its understanding of the trial customer’s business and its email users. It identified attacks that other vendors did not, while allowing safe emails through. Furthermore, the CISO explicitly cited Darktrace as the only technology that demonstrated autonomous learning. As well as catching threats that other vendors did not, the CISO saw maturity areas such as how Darktrace dealt with non-productive mail and business-as-usual emails, without any user input.  Because of the nature of unsupervised ML, Darktrace’s learning of right and wrong will never be static or complete – it will continue to revise its understanding and adapt to the changing business and communications landscape.

This case study highlights a key tenet of Darktrace’s philosophy – that a rules and tuning-based approach will always be one step behind. Delivering benign emails while holding back malicious emails from the same domain demonstrates that safety is not defined in a straight line, or by historical precedent. Only by analyzing every email in-depth for its content and context can you guarantee that it belongs.  

While other solutions are making efforts to improve a static approach with AI, Darktrace’s AI remains truly unsupervised so it is dynamic enough to catch the most agile and evolving threats. This is what allows us to protect our customers by plugging a vital gap in their security stack that ensures they can meet the challenges of tomorrow's email attacks.

Interested in learning more about Darktrace / EMAIL? Check out our product hub.

Download: Darktrace / EMAIL Solution Brief

Discover the most advanced cloud-native AI email security solution to protect your domain and brand while preventing phishing, novel social engineering, business email compromise, account takeover, and data loss.

  • Gain up to 13 days of earlier threat detection and maximize ROI on your current email security
  • Experience 20-25% more threat blocking power with Darktrace / EMAIL
  • Stop the 58% of threats bypassing traditional email security

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
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Django Beek
Solutions Engineer

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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
Technical Author (AI) Developer

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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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Mikey Anderson
Product Marketing Manager, Network Detection & Response
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