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August 7, 2024

How Darktrace’s AI Applies a Zero-Trust Mentality within Critical Infrastructure Supply Chains

Darktrace prevented a Critical National Infrastructure organization from falling victim to a SharePoint phishing attack originating from one of its trusted suppliers. This blog discusses common perceptions of zero-trust in email security, how AI that uses anomaly-based threat detection embodies core zero-trust principles and the relevance of this approach to securing CNI bodies with complex but interdependent supply chains from Cloud account compromise. 
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
Nicole Wong
Cyber Security Analyst
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07
Aug 2024

Note: In order to name anonymity, real organization names have been replaced, all names used in this blog are fictitious.

What are critical national infrastructure sectors?

Critical National Infrastructure (CNI) sectors encompass of assets, systems, and networks essential to the functioning of society. Any disruption or destruction of these sectors could have wide-reaching and potentially disastrous effects on a country’s economy, security and/or healthcare services [1].

Cyber risks across Transportation Systems sector

Transportation Systems is one such CNI sector comprising of interconnected networks of fixed and mobile assets managed by both public and private operators. These systems are highly interdependent with other CNI sectors too. As such, the digital technologies this sector relies on – such as positioning and tracking, signaling, communications, industrial system controls, and data and business management – are often interconnected through different networks and remote access terminals. This interconnectedness creates multiple entry points that need to be security across the supply.

Digital transformation has swept through CNI sectors in recent years, including Transportation Systems. These organizations are now increasingly dependent on third-party and cloud providers for data storage and transmission, making their supply chains vulnerable to exploitation by malicious actors [2].

The exploitation of legitimate and popular cloud services mirrors the well-known “living-off-the-land” techniques, which are not being adapted to the cloud along with the resources they support. In one recent case previously discussed by Darktrace, for example, a phishing attack attempted to abuse Dropbox to deliver malicious payloads.

Zero-Trust within CNI Sectors

One recommended approach to secure an organization’s supply chain and cloud environments is the implementation of zero-trust strategies, which remove inherent trust within the network [3] [4]. The principle of “never trust, always verify” is widely recognized as an architectural design, with 63% of organizations surveyed by Gartner reportedly implementing a zero-trust strategy, but in most cases to less than 50% of their environments [5]

Although this figure reflects the reality and challenge of balancing operations and security, demands from the threat landscape and supply chain risks mean that organizations must adopt zero-trust principles in areas not traditionally considered part of network architecture, such as email and cloud environments.

Email is often the primary entry point for cyber-attacks with Business Email Compromise (BEC) being a major threat to CNI organizations. However, the application of zero-trust principles to secure email environments is still not well understood. Common misconceptions include:

  • “Positively identifying known and trusted senders” – Maintaining a list of “known and trusted senders” contradicts the zero-trust model, which assumes that no entity is inherently trustworthy.
  • “Using DMARC, DKIM and SPF” – While these protocols offer some protection, they are often insufficient on their own, as they can be bypassed and do not protect against email account takeovers. Research published from Darktrace’s last two threat reports consistently shows that at least 60% of phishing emails detected by Darktrace had bypassed Domain-based Message Authentication, Reporting & Conformance (DMARC) [6] [7].  
  • “Mapping transaction flows between internal and external users to determine what access is required/not required” – Although this aligns with the principles of least privilege, it is too static for today’s dynamic supply chains and evolving digital infrastructure. This approach also suggests the existence of “trusted” access routes into a network.

Attack Overview

In July 2024, Darktrace / EMAIL™ detected and contained a sophisticated phishing attack leveraging Microsoft SharePoint. This attack exploited the trusted relationship between a Darktrace customer in the public transport sector and a compromised supplier. Traditional methods, such as those detailed above, would likely have failed to defend against such an advanced threat. However, Darktrace’s behavioral analysis and zero-trust approach to email security allowed it to successfully identify and neutralize the attack, preventing any potential disruption.

Initial Intrusion Attempt

The observed phishing attack by Darktrace would suggest that the customer’s supplier was targeted by a similar campaign beforehand. This initial breach likely allowed the attacker to use the now compromised account as a vector to compromise additional accounts and networks.

On July 9, Darktrace / EMAIL identified a significant spike in inbound emails from “supplier@engineeringcompany[.]com”. The emails appeared to be legitimate notifications sent via SharePoint and contained a file named “Payment Applications Docs”.

Email correspondence in the weeks around the phishing attack.
Figure 1: Email correspondence in the weeks around the phishing attack. The sender is an established correspondent with ongoing communications prior to and after the attack, however there is a significant spike in incoming emails on the day of the attack.

This reflects a common technique in malicious social engineering attempts, where references to payment are used to draw attention and prompt a response. Darktrace observed a large number of recipients within the organization receiving the same file, suggesting that the motive was likely credential harvesting rather than financial gain. Financially motivated attacks typically require a more targeted, ‘under-the-radar’ approach to be successful.

These phishing emails were able to bypass the customer’s email gateways as they were sent from a trusted and authoritative source, SharePoint, and utilized an email address with which the customer had previously corresponded. The compromised account was likely whitelisted by traditional email security tools that rely on SPF, DKIM, and DMAC, allowing the malicious emails to evade detection.

Autonomous Response

Darktrace / EMAIL analysis of the unusual characteristics of the phishing email in relation to the supplier’s typical behaviour, despite the email originating from a legitimate SharePoint notification.
Figure 2: Darktrace / EMAIL analysis of the unusual characteristics of the phishing email in relation to the supplier’s typical behavior, despite the email originating from a legitimate SharePoint notification.

However, Darktrace / EMAIL did not use these static rules to automatically trust the email. Darktrace’s Self-Learning AI detected the following anomalies:

  • Although the sender was known, it was not normal for the supplier to share files with the customer via SharePoint.
  • The supplier initiated an unusually large number of file shares in a short period of time, indicating potential spam activity.
  • The SharePoint link had wide access permissions, which is unusual for a sensitive payment document legitimately shared between established contacts.

Darktrace understood that the email activity constituted a significant deviation in expected behavior between the sender and customer, regardless of the known sender and use of a legitimate filesharing platform like SharePoint.

As a result, Darktrace took action to hold more than 100 malicious emails connected to the phishing attack, preventing them from landing in recipient inboxes in the first instance.  By taking a behavioral approach to securing customer email environments, Darktrace’s Self-Learning AI embodies the principles of zero trust, assessing each interaction in real-time against a user’s dynamic baseline rather than relying on static and often inaccurate rules to define trust.

Conclusion

Cloud services, such as SharePoint, offer significant advantages to the transportation sector by streamlining data exchange with supply chain partners and facilitating access to information for analytics and planning. However, these benefits come with notable risks. If a cloud account is compromised, unauthorized access to sensitive information could lead to extortion and lateral movement into mission-critical systems for more damaging attacks on CNI. Even a brief disruption in cloud access can have severe economic repercussions due to the sector’s dependence on these services for resource coordination and the cascading impacts on other critical systems [9].

While supply chain resilience is often evaluated based on a supplier’s initial compliance with baseline standards, organizations must be wary of potential future threats and focus on post-implementation security. It is essential for organizations to employ strategies to protect their assets from attacks that would exploit vulnerabilities within the trusted supply chain. Given that CNI and the transportation sector are prime targets for state-sponsored actors and Advanced Persistent Threat (APT) groups, the complex and interconnected nature of their supply chains opens the door for opportunistic attackers.

Defenders face the challenge of ensuring secure access and collaboration across numerous, dynamic assets, often without full visibility. Therefore, security solutions must be as dynamic as the threats they face, avoiding reliance on static rules. Real-time assessment of devices behavior, even if deemed trusted by end-users and human security teams, is crucial for maintaining security.

Darktrace’s AI-driven threat detection aligns with the zero-trust principle of assuming the risk of a breach. By leveraging AI that learns an organization’s specific patterns of life, Darktrace provides a tailored security approach ideal for organizations with complex supply chains.

Credit to Nicole Wong, Senior Cyber Analyst Consultant and Ryan Traill, Threat Content Lead

Appendices

Darktrace Model Detections

Key model alerts:

  • Personalized Sharepoint Share + New Unknown Link
  • Personalized Sharepoint Share + Bad Display Text
  • Personalized Sharepoint Share + Distant Recipient Interaction with Domain
  • Personalized Sharepoint Share + Sender Surge
  • Personalized Sharepoint Share + Wide Access Sharepoint Link

MITRE ATT&CK Mapping

Resource Development • Compromise Accounts: Cloud Accounts • T1586.003

Initial Access • Supply Chain Compromise • T1195

References

[1] https://www.cisa.gov/topics/critical-infrastructure-security-and-resilience/critical-infrastructure-sectors

[2]  https://committees.parliament.uk/writtenevidence/126313/pdf/

[3] https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-161r1.pdf

[4] https://cloudsecurityalliance.org/press-releases/2023/11/15/cloud-security-alliance-launches-the-industry-s-first-authoritative-zero-trust-training-and-credential-the-certificate-of-competence-in-zero-trust-cczt

[5] https://www.gartner.com/en/documents/5286863#:~:text=Summary,anticipate%20staffing%20and%20cost%20increases.

[6] https://darktrace.com/threat-report-2023

[7] https://darktrace.com/resources/first-6-half-year-threat-report-2024

[8] https://dfrlab.org/2023/07/10/critical-infrastructure-and-the-cloud-policy-for-emerging-risk/#transportation

[9] https://access-national-risk-register.service.cabinetoffice.gov.uk/risk-scenario/cyber-attack-transport-sector

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
Nicole Wong
Cyber Security 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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