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September 28, 2023

How Darktrace Stopped an Account Hijack Fast

Learn how Darktrace detected an account hijack within days of deployment. Discover the strategies used to protect against cyber threats.
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
Min Kim
Cyber Security Analyst
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28
Sep 2023

Cloud Migration Expanding the Attack Surface

Cloud migration is here to stay – accelerated by pandemic lockdowns, there has been an ongoing increase in the use of public cloud services, and Gartner has forecasted worldwide public cloud spending to grow around 20%, or by almost USD 600 billion [1], in 2023. With more and more organizations utilizing cloud services and moving their operations to the cloud, there has also been a corresponding shift in malicious activity targeting cloud-based software and services, including Microsoft 365, a prominent and oft-used Software-as-a-Service (SaaS).

With the adoption and implementation of more SaaS products, the overall attack surface of an organization increases – this gives malicious actors additional opportunities to exploit and compromise a network, necessitating proper controls to be in place. This increased attack surface can leave organization’s open to cyber risks like cloud misconfigurations, supply chain attacks and zero-day vulnerabilities [2]. In order to achieve full visibility over cloud activity and prevent SaaS compromise, it is paramount for security teams to deploy sophisticated security measures that are able to learn an organization’s SaaS environment and detect suspicious activity at the earliest stage.

Darktrace Immediately Detects Hijacked Account

In May 2023, Darktrace observed a chain of suspicious SaaS activity on the network of a customer who was about to begin their trial of Darktrace/Apps™ and Darktrace/Email™. Despite being deployed on the network for less than a week, Darktrace DETECT™ recognized that the legitimate SaaS account, belonging to an executive at the organization, had been hijacked. Darktrace/Email was able to provide full visibility over inbound and outbound mail and identified that the compromised account was subsequently used to launch an internal spear-phishing campaign.

If Darktrace RESPOND™ were enabled in autonomous response mode at the time of this compromise, it would have been able to take swift preventative action to disrupt the account compromise and prevent the ensuing phishing attack.

Account Hijack Attack Overview

Unusual External Sources for SaaS Credentials

On May 9, 2023, Darktrace/Apps detected the first in a series of anomalous activities performed by a Microsoft 365 user account that was indicative of compromise, namely a failed login from an external IP address located in Virginia.

Figure 1: The failed login notice, as seen in Darktrace/Apps. The notice includes additional context about the failed login attempt to the SaaS account.

Just a few minutes later, Darktrace observed the same user credential being used to successfully login from the same unusual IP address, with multi-factor authentication (MFA) requirements satisfied.

Figure 2: The “Unusual External Source for SaaS Credential Use” model breach summary, showing the successful login to the SaaS user account (with MFA), from the rare external IP address.

A few hours after this, the user credential was once again used to login from a different city in the state of Virginia, with MFA requirements successfully met again. Around the time of this activity, the SaaS user account was also observed previewing various business-related files hosted on Microsoft SharePoint, behavior that, taken in isolation, did not appear to be out of the ordinary and could have represented legitimate activity.

The following day, May 10, however, there were additional login attempts observed from two different states within the US, namely Texas and Florida. Darktrace understood that this activity was extremely suspicious, as it was highly improbable that the legitimate user would be able to travel over 2,500 miles in such a short period of time. Both login attempts were successful and passed MFA requirements, suggesting that the malicious actor was employing techniques to bypass MFA. Such MFA bypass techniques could include inserting malicious infrastructure between the user and the application and intercepting user credentials and tokens, or by compromising browser cookies to bypass authentication controls [3]. There have also been high-profile cases in the recent years of legitimate users mistakenly (and perhaps even instinctively) accepting MFA prompts on their token or mobile device, believing it to be a legitimate process despite not having performed the login themselves.

New Email Rule

On the evening of May 10, following the successful logins from multiple US states, Darktrace observed the Microsoft 365 user creating a new inbox rule, named “.’, in Microsoft Outlook from an IP located in Florida. Threat actors are often observed naming new email rules with single characters, likely to evade detection, but also for the sake of expediency so as to not expend any additional time creating meaningful labels.

In this case the newly created email rules included several suspicious properties, including ‘AlwaysDeleteOutlookRulesBlob’, ‘StopProcessingRules’ and “MoveToFolder”.

Firstly, ‘AlwaysDeleteOutlookRulesBlob’ suppresses or hides warning messages that typically appear if modifications to email rules are made [4]. In this case, it is likely the malicious actor was attempting to implement this property to obfuscate the creation of new email rules.

The ‘StopProcessingRules’ rule meant that any subsequent email rules created by the legitimate user would be overridden by the email rule created by the malicious actor [5]. Finally, the implementation of “MoveToFolder” would allow the malicious actor to automatically move all outgoing emails from the “Sent” folder to the “Deleted Items” folder, for example, further obfuscating their malicious activities [6]. The utilization of these email rule properties is frequently observed during account hijackings as it allows attackers to delete and/or forward key emails, delete evidence of exploitation and launch phishing campaigns [7].

In this incident, the new email rule would likely have enabled the malicious actor to evade the detection of traditional security measures and achieve greater persistence using the Microsoft 365 account.

Figure 3: Screenshot of the “New Email Rule” model breach. The Office365 properties associated with the newly modified Microsoft Outlook inbox rule, “.”, are highlighted in red.

Account Update

A few hours after the creation of the new email rule, Darktrace observed the threat actor successfully changing the Microsoft 365 user’s account password, this time from a new IP address in Texas. As a result of this action, the attacker would have locked out the legitimate user, effectively gaining full access over the SaaS account.

Figure 4: The model breach event log showing the user password and token change updates performed by the compromised SaaS account.

Phishing Emails

The compromised SaaS account was then observed sending a high volume of suspicious emails to both internal and external email addresses. Darktrace was able to identify that the emails attempting to impersonate the legitimate service DocuSign and contained a malicious link prompting users to click on the text “Review Document”. Upon clicking this link, users would be redirected to a site hosted on Adobe Express, namely hxxps://express.adobe[.]com/page/A9ZKVObdXhN4p/.

Adobe Express is a free service that allows users to create web pages which can be hosted and shared publicly; it is likely that the threat actor here leveraged the service to use in their phishing campaign. When clicked, such links could result in a device unwittingly downloading malware hosted on the site, or direct unsuspecting users to a spoofed login page attempting to harvest user credentials by imitating legitimate companies like Microsoft.

Figure 5: Screenshot of the phishing email, containing a malicious link hidden behind the “Review Document” text. The embedded link directs to a now-defunct page that was hosted on Adobe Express.

The malicious site hosted on Adobe Express was subsequently taken down by Adobe, possibly in response to user reports of maliciousness. Unfortunately though, platforms like this that offer free webhosting services can easily and repeatedly be abused by malicious actors. Simply by creating new pages hosted on different IP addresses, actors are able to continue to carry out such phishing attacks against unsuspecting users.

In addition to the suspicious SaaS and email activity that took place between May 9 and May 10, Darktrace/Email also detected the compromised account sending and receiving suspicious emails starting on May 4, just two days after Darktrace’s initial deployment on the customer’s environment. It is probable that the SaaS account was compromised around this time, or even prior to Darktrace’s deployment on May 2, likely via a phishing and credential harvesting campaign similar to the one detailed above.

Figure 6: Event logs of the compromised SaaS user, here seen breaching several Darktrace/Email model breaches on 4th May.

Darktrace Coverage

As the customer was soon to begin their trial period, Darktrace RESPOND was set in “human confirmation” mode, meaning that any preventative RESPOND actions required manual application by the customer’s security team.

If Darktrace RESPOND had been enabled in autonomous response mode during this incident, it would have taken swift mitigative action by logging the suspicious user out of the SaaS account and disabling the account for a defined period of time, in doing so disrupting the attack at the earliest possible stage and giving the customer the necessary time to perform remediation steps.  As it was, however, these RESPOND actions were suggested to the customer’s security team for them to manually apply.

Figure 7: Example of Darktrace RESPOND notices, in response to the anomalous user activity.

Nevertheless, with Darktrace/Apps in place, visibility over the anomalous cloud-based activities was significantly increased, enabling the swift identification of the chain of suspicious activities involved in this compromise.

In this case, the prospective customer reached out to Darktrace directly through the Ask the Expert (ATE) service. Darktrace’s expert analyst team then conducted a timely and comprehensive investigation into the suspicious activity surrounding this SaaS compromise, and shared these findings with the customer’s security team.

Conclusion

Ultimately, this example of SaaS account compromise highlights Darktrace’s unique ability to learn an organization’s digital environment and recognize activity that is deemed to be unexpected, within a matter of days.

Due to the lack of obvious or known indicators of compromise (IoCs) associated with the malicious activity in this incident, this account hijack would likely have gone unnoticed by traditional security tools that rely on a rules and signatures-based approach to threat detection. However, Darktrace’s Self-Learning AI enables it to detect the subtle deviations in a device’s behavior that could be indicative of an ongoing compromise.

Despite being newly deployed on a prospective customer’s network, Darktrace DETECT was able to identify unusual login attempts from geographically improbable locations, suspicious email rule updates, password changes, as well as the subsequent mounting of a phishing campaign, all before the customer’s trial of Darktrace had even begun.

When enabled in autonomous response mode, Darktrace RESPOND would be able to take swift preventative action against such activity as soon as it is detected, effectively shutting down the compromise and mitigating any subsequent phishing attacks.

With the full deployment of Darktrace’s suite of products, including Darktrace/Apps and Darktrace/Email, customers can rest assured their critical data and systems are protected, even in the case of hybrid and multi-cloud environments.

Credit: Samuel Wee, Senior Analyst Consultant & Model Developer

Appendices

References

[1] https://www.gartner.com/en/newsroom/press-releases/2022-10-31-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-reach-nearly-600-billion-in-2023

[2] https://www.upguard.com/blog/saas-security-risks

[3] https://www.microsoft.com/en-us/security/blog/2022/11/16/token-tactics-how-to-prevent-detect-and-respond-to-cloud-token-theft/

[4] https://learn.microsoft.com/en-us/powershell/module/exchange/disable-inboxrule?view=exchange-ps

[5] https://learn.microsoft.com/en-us/dotnet/api/microsoft.exchange.webservices.data.ruleactions.stopprocessingrules?view=exchange-ews-api

[6] https://learn.microsoft.com/en-us/dotnet/api/microsoft.exchange.webservices.data.ruleactions.movetofolder?view=exchange-ews-api

[7] https://blog.knowbe4.com/check-your-email-rules-for-maliciousness

Darktrace Model Detections

Darktrace DETECT and RESPOND Models Breached:

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Unusual Activity / Multiple Unusual External Sources for SaaS Credential

Antigena / SaaS / Antigena Unusual Activity Block (RESPOND Model)

SaaS / Compliance / New Email Rule

Antigena / SaaS / Antigena Significant Compliance Activity Block

SaaS / Compromise / Unusual Login and New Email Rule (Enhanced Monitoring Model)

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (RESPOND Model)

SaaS / Compromise / SaaS Anomaly Following Anomalous Login (Enhanced Monitoring Model)

SaaS / Compromise / Unusual Login and Account Update

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (RESPOND Model)

IoC – Type – Description & Confidence

hxxps://express.adobe[.]com/page/A9ZKVObdXhN4p/ - Domain – Probable Phishing Page (Now Defunct)

37.19.221[.]142 – IP Address – Unusual Login Source

35.174.4[.]92 – IP Address – Unusual Login Source

MITRE ATT&CK Mapping

Tactic - Techniques

INITIAL ACCESS, PRIVILEGE ESCALATION, DEFENSE EVASION, PERSISTENCE

T1078.004 – Cloud Accounts

DISCOVERY

T1538 – Cloud Service Dashboards

CREDENTIAL ACCESS

T1539 – Steal Web Session Cookie

RESOURCE DEVELOPMENT

T1586 – Compromise Accounts

PERSISTENCE

T1137.005 – Outlook Rules

Probability yardstick used to communicate the probability that statements or explanations given are correct.
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
Min Kim
Cyber Security Analyst

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May 26, 2026

The CIP-015 Countdown: What Utilities Should Be Doing Before October 2028

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CIP-015 what you need to know

The electric sector already knows CIP-015 is coming. The better question is whether utilities are using the time before October 1, 2028 to build an Internal Network Security Monitoring program that is defensible, auditable, and operationally useful.

I have spent most of my OT cybersecurity career around the power sector, from early NERC CIP program work as an asset owner, to consulting with utilities ranging from small municipalities and rural cooperatives to some of the largest power companies in the country, to now working with technology that helps organizations improve visibility and detection across IT and OT. One lesson has been consistent across all of those roles: compliance is not just about having a control in place. It is about being able to prove the control works.

That is where CIP-015 becomes important.

The standard is not simply asking utilities to deploy a tool inside the Electronic Security Perimeter and call the job done. CIP-015 is about improving the probability of detecting anomalous or unauthorized network activity so that organizations can improve response and recovery from an attack. That purpose is directly stated in the standard itself. (NERC)

The real work between now and October 2028 is not just buying technology. It is building an INSM capability that can collect the right data, detect meaningful activity, support evaluation, retain the right evidence, and protect that evidence from unauthorized deletion or modification.

Why CIP-015 exists

CIP-015 exists because perimeter security alone does not solve the internal visibility problem.

For years, many CIP controls have focused heavily on access management, segmentation, patching, logging, training, and other security practices that help reduce the likelihood of unauthorized access. Those controls still matter. But they do not fully answer what happens after an attacker, insider, compromised vendor account, misused credential, or malicious activity is already operating inside a trusted environment.

NERC’s technical rationale explains that Internal Network Security Monitoring focuses on the collection and analysis of network communications inside a “trust zone,” such as an ESP. In other words, CIP-015 is not only about defending the edge. It is about understanding what is happening inside the environment once traffic is already within the trusted zone. (NERC)

That is the internal visibility gap utilities need to close.

Why traditional security monitoring does not fully satisfy CIP-015

One mistake utilities should avoid is assuming that existing security event monitoring automatically solves CIP-015.

Many organizations already have logging programs tied to CIP-007, SIEM use cases, host-level security events, authentication logs, malware alerts, and incident response workflows. Those capabilities remain valuable, but they are not the same as Internal Network Security Monitoring.

Security event monitoring often tells you what happened on or to a system. INSM is intended to help show what is happening between systems, across network communications, devices, connections, and internal traffic patterns. That distinction is especially important in OT environments where adversaries may use legitimate pathways, valid credentials, native protocols, remote access, engineering workstations, or trusted systems to move inside the environment.

CIP-015 pushes utilities toward a different level of visibility: not just “did a system log something,” but “can we see and evaluate anomalous or unauthorized activity occurring inside the ESP?”

What CIP-015 requires

At a high level, CIP-015-1 requires three core capabilities.

Requirement R1: Monitoring internal network activity  

First, under Requirement R1, Responsible Entities must implement, using a risk-based rationale, network data feeds to monitor network activity, including connections, devices, and network communications. They must also implement one or more methods to detect anomalous network activity using those feeds, and one or more methods to evaluate detected anomalous activity to determine further actions.

Requirement R2: Retaining INSM data for investigations

Second, under Requirement R2, entities must retain INSM data associated with anomalous network activity at least until the related evaluation and action are complete. The standard also notes that entities are not required to retain INSM data that is not relevant to detected anomalous activity.

Requirement R3: Protecting monitoring data from tampering

Third, under Requirement R3, entities must protect INSM data collected for R1 and retained for R2 from unauthorized deletion or modification.

Those requirements may sound straightforward, but implementation is where the challenge begins.

What should utilities be asking themselves for CIP-015?

  • Where are we collecting network data inside the ESP, and why are those feeds defensible?
  • What methods are we using to detect anomalous network activity?
  • How do we distinguish meaningful anomalous behavior from normal operational change?
  • Who evaluates detections, and how are decisions documented?
  • What data is retained, and how is it protected from unauthorized deletion or modification?
  • Can we produce evidence that proves this process has worked over time?

Those answers matter because auditors will not be looking for marketing claims. They will be looking for evidence.

Why anomaly detection is central to CIP-015 compliance

One of the most important parts of CIP-015 is also one of the easiest to oversimplify: the word anomalous.

NERC’s technical rationale provides useful context. It explains that, as used in CIP-015, “anomalous” refers to unexpected, undesired, unusual, or undetermined network traffic. It also makes clear that the term does not refer to any single proprietary technology commonly marketed as “anomaly detection.”

Understanding static baselines vs true anomaly detection

A static baseline is not the same thing as meaningful anomaly detection. If a platform observes traffic for a limited period of time, assumes that observed behavior is “normal,” and then flags future deviations without deeper context, the result can be noisy, brittle, and operationally frustrating.

In real OT environments, “normal” is not fixed. Maintenance windows, vendor access, failovers, engineering changes, testing activity, backup jobs, and operational shifts can all change behavior. Detection has to keep learning and understand context. Otherwise, the organization may end up with alerts that are technically anomalous but not practically useful.

CIP-015 is not just about producing anomalies. It is about producing meaningful detections that can be evaluated, documented, and acted upon.

What should utilities consider when looking for anomaly detection tools

Some technologies were built around behavioral analysis and anomaly detection long before CIP-015 existed. What practitioners should look for is if the technology behind the phrase can identify meaningful deviations, provide context, reduce noise, and support the evaluation and evidence expectations of the standard.

Utilities should be cautious of vendor positioning that treats “anomaly” as a simple compliance keyword. This is especially important when evaluating tools historically built around signature-based, threat-based, or rule-based detection methods that are now being positioned as anomaly detection because CIP-015 uses the term.

A platform does not solve CIP-015 simply because it can baseline traffic or generate alerts when something changes.

The question is not: Can this tool create alerts?

The question is: Can this tool identify meaningful anomalous activity with enough context, prioritization, and evidence to support evaluation and response?

Why evidence and audit readiness matter for CIP-015

In NERC CIP, the control is only part of the story. Evidence is the part that proves the control existed, worked, and was followed.

That is why CIP-015 readiness should not be treated as a simple deployment project. It should be treated as a compliance operations and evidence program.

What auditors will expect utilities to prove

For R1, examples of evidence include documentation of network data feeds and the risk-based rationale for selecting them, anomalous network detection events, INSM configuration settings, communication baselines or other detection methods, methods used to evaluate anomalous activity, and actions taken in response to detected anomalies.

For R2, evidence may include documentation of the retention process, system configurations, or system-generated reports showing retention timelines sufficient to support evaluation. For R3, evidence may include documentation showing how INSM data is protected from unauthorized deletion or modification.

Common evidence gaps that can create compliance risk

If an entity implements a platform that generates noisy detections, lacks context, does not retain the right data, cannot demonstrate how data is protected, or cannot produce useful audit evidence, the issue may not become obvious until much later. By then, an organization may discover during an audit that it cannot prove what it thought it had implemented.

That is a bad place to be.

CIP evidence gaps can create exposure that goes back over time, not just to the day the audit finding is discovered. This is why utilities need to validate the process early. Do not wait until an audit cycle to find out whether your INSM approach can stand up to scrutiny.

How utilities should prepare for CIP-015 before 2028

October 2028 may sound far away, but in utility planning terms, it is not.

Utilities should already be moving through a structured readiness process.

Assessing internal network visibility across trusted environments

Start with scope. Identify the applicable High and Medium Impact BES Cyber Systems, the relevant ESPs, and the environments where INSM requirements will apply. Then map current visibility. Where do you already have useful network monitoring? Where are you relying mostly on logs, perimeter controls, or assumptions? Where do you have limited east-west visibility inside trusted environments?

Building a defensible network data feed strategy

Next, define the network data feed strategy. CIP-015 requires a risk-based rationale, so the organization should be able to explain why specific feeds were selected and how they support detection of anomalous activity across relevant connections, devices, and communications.

Validating anomaly detection workflows

Then validate the detection method. This is where utilities need to go deeper than vendor claims. Ask how the platform identifies anomalous activity. Ask how it reduces noise. Ask what context is provided for evaluation. Ask how it handles changes in normal operations. Ask what evidence is retained and how that evidence can be produced.

Testing evidence retention and protection processes

After that, build the evaluation workflow. Who reviews detections? How are anomalies classified as benign, abnormal but not suspicious, suspicious, or potentially malicious? When does an event move into CIP-008 incident response? What documentation is created during that process?

Finally, test evidence production. Utilities should be able to show detection records, configuration settings, evaluation notes, response actions, retention records, and data protection controls before an auditor asks for them.

Where Darktrace Fits into CIP-015

This is where technology matters, but only as part of the broader program.

Darktrace was built on self-learning anomaly detection long before CIP-015 created a new compliance driver around anomalous network activity. Its value is rooted in continuous behavioral understanding, multiple analytical techniques, and the ability to identify meaningful deviations across complex IT and OT environments. That matters because CIP-015 requires more than basic alerting. It requires detection that supports evaluation, evidence, and action.

This IT and OT visibility is especially important in power utility environments. High and Medium Impact environments are not made up only of industrial protocols and field devices. Control centers, operational workstations, engineering workstations, servers, remote access systems, domain services, printers, and other enterprise-class assets often sit inside or adjacent to critical operational environments. A useful INSM capability should understand a wide range of communications across both IT and OT, not only traditional industrial protocols like Modbus, DNP3, or IEC 61850.

That distinction matters because “protocol support” can mean very different things. Identifying that a protocol is present is not the same as performing deeper packet analysis that can provide behavioral context, richer protocol understanding, and meaningful detection across the communications actually used inside the environment. For CIP-015, utilities should be asking whether a platform can help evaluate activity across both enterprise and industrial communications, because real power utility environments are rarely “OT-only.”

This is also why utilities should look carefully at how vendors use the word “anomaly.” Some platforms were designed around behavioral understanding and anomaly detection long before CIP-015 created a new compliance driver. Others may now be adopting the language because the standard uses the term. The difference matters. Utilities should ask whether the platform’s detection approach is foundational to the technology, or simply a new label applied to existing signature-based, threat-based, or rule-based methods.

In OT environments, detection quality matters. Utilities do not need more noise. They need visibility into internal communications, confidence in what is normal, context when something changes, and prioritization that helps security and operations teams focus on what matters.

A strong INSM program should help utilities move from raw monitoring to operational confidence. It should support east-west visibility, better anomaly evaluation, defensible evidence retention, protection of monitoring data, and alignment between compliance and security outcomes.

That is the right way to think about CIP-015.

Not as “deploy a tool and move on.”But as “build a capability that can be trusted, operated, and proven.”

CIP-015 is about proving your INSM capability works

The CIP-015 countdown is real, but the countdown itself is not the whole story.

The real story is what utilities do with the time that remains.

Organizations that treat CIP-015 as a checkbox may be able to say they deployed something. But organizations that treat it as an opportunity to close the internal visibility gap will gain something much more valuable: better detection, better response, better evidence, and stronger operational resilience.

The question utilities should be asking now is not whether they can produce more alerts before October 2028.

The question is whether they can prove their INSM capability actually works.

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About the author
Jeffrey Macre
Principal Industrial Security Solutions Architect

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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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About the author
Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
Elevate your network security with Darktrace AI