Blog
/
Network
/
August 22, 2023

Darktrace’s Detection of Unattributed Ransomware

Leveraging anomaly-based detection, we successfully identified an ongoing ransomware attack on the network of a customer and the activity that preceded it.
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
Natalia Sánchez Rocafort
Cyber Security Analyst
Default blog image
22
Aug 2023

In the current threat landscape, much of the conversation around ransomware focusses on high-profile strains and notorious threat groups. While organizations and their security teams are justified in these concerns, it is important not to underestimate the danger posed by smaller scale, unattributed ransomware attacks.

Unlike attributed ransomware strains, there are often no playbooks or lists of previously observed indicators of compromise (IoCs) that security teams can consult to help them shore up their cyber defenses. As such, anomaly detection is critical to ensure that emerging threats can be detected based on their abnormality on the network, rather than relying heavily on threat intelligence.

In mid-March 2023, a Darktrace customer requested analytical support from the Darktrace Security Operations Center (SOC) after they had been hit by a ransomware attack a few hours earlier. Darktrace was able to uncover a myriad of malicious activity that preceded the eventual ransomware deployment, ultimately assisting the customer to identify compromised devices and contain the ransomware attack.

Attack Overview

While there were a small number of endpoints that had been flagged as malicious by open-source intelligence (OSINT), Darktrace DETECT™ focused on the unusualness of the activity surrounding this emerging ransomware attack. This provided unparalleled visibility over this ransomware attack at every stage of the cyber kill chain, whilst also revealing the potential origins of the compromise which came months area.

Initial Compromise

Initial investigation revealed that several devices that Darktrace were observed performing suspicious activity had previously engaged in anomalous behavior several months before the ransomware event, indicating this could be a part of a repeated compromise or the result of initial access brokers.

Most notably, in late January 2023 there was a spike in unusual activity when some of the affected devices were observed performing activity indicative of network and device scanning.

Darktrace DETECT identified some of the devices establishing unusually high volumes of internal failed connections via TCP and UDP, and the SMB protocol. Various key ports, such as 135, 139, and 445, were also scanned.

Due to the number of affected devices, the exact initial attack vector is unclear; however, one likely scenario is associated with an internet-facing DNS server. Towards the end of January 2023, the server began to receive unusual TCP DNS requests from the rare external endpoint, 103.203.59[.]3, which had been flagged as potentially malicious by OSINT [4]. Based on a portion of the hostname of the device, dc01, we can assume that this server served as a gateway to the domain controller. If a domain controller is compromised, a malicious actor would gain access to usernames and passwords within a network allowing attackers to obtain administrative-level access to an organization’s digital estate.

Around the same time as the unusual TCP DNS requests, Darktrace DETECT observed the domain controller engaging in further suspicious activity. As demonstrated in Figure 1, Darktrace recognized that this server was not responding to common requests from multiple internal devices, as it would be expected to. Following this, the device was observed carrying out new or uncommon Windows Management Instrumentation (WMI) activity. WMI is typically used by network administrators to manage remote and local Windows systems [3].

Figure 1: Device event log depicting the possible Initial attack vector.


Had Darktrace RESPOND™ been enabled in autonomous response mode, it would have to blocked connections originating from the compromised internal devices as soon as they were detected, while also limiting affected devices to their pre-established patterns of file to prevent them from carrying out any further malicious activity.

Darktrace subsequently observed multiple devices establishing various chains of connections that are indicative of lateral movement activity, such as unusual internal RDP and WMI requests. While there may be devices within an organization that do regularly partake these types of connections, Darktrace recognized that this activity was extremely unusual for these devices.

Darktrace’s Self-Learning AI allows for a deep understanding of customer networks and the devices within them. It’s anomaly-based threat detection capability enables it to recognize subtle deviations in a device’s normal patterns of behavior, without depending on known IoCs or signatures and rules to guide it.

Figure 2: Observed chain of possible lateral movement.


Persistence

Darktrace DETECT observed several affected devices communicating with rare external endpoints that had also been flagged as potentially malicious by OSINT tools. Multiple devices were observed performing activity indicative of NTLM brute-forcing activity, as seen in the Figure 3 which highlights the event log of the aforementioned domain controller. Said domain controller continuously engaged in anomalous behavior throughout the course of the attack. The same device was seen using a potentially compromise credential, ‘cvd’, which was observed via an SMB login event.

Figure 3: Continued unusual external connectivity.


Affected devices, including the domain controller, continued to engage in consistent communication with the endpoints prior to the actual ransomware attack. Darktrace identified that some of these malicious endpoints had likely been generated by Domain Generation Algorithms (DGA), a classic tactic utilized by threat actors. Subsequent OSINT investigation revealed that one such domain had been associated with malware such as TrojanDownloader:Win32/Upatre!rfn [5].

All external engagements were observed by Darktrace DETECT and would have been actioned on by Darktrace RESPOND, had it been configured in autonomous response mode. It would have blocked any suspicious outgoing connections originating from the compromised devices, thus preventing additional external engagement from taking place. Darktrace RESPOND works in tandem with DETECT to autonomously take action against suspicious activity based on its unusualness, rather than relying on static lists of ‘known-bads’ or malicious IoCs.

Reconnaissance

On March 14, 2023, a few days before the ransomware attack, Darktrace observed multiple internal devices failing to establish connections in a manner that suggests SMB, RDP and network scanning. Among these devices once more was the domain controller, which was seen performing potential SMB brute-forcing, representing yet another example of malicious activity carried out by this device.

Lateral Movement

Immediately prior to the attack, many compromised devices were observed mobilizing to conduct an array of high-severity lateral movement activity. Darktrace detected one device using two administrative credentials, namely ‘Administrator’ and ‘administrator’, while it also observed a notable spike in the volume of successful SMB connections from the device around the same time.

At this point, Darktrace DETECT was observing the progression of this attack along the cyber kill chain. What had started as internal recognisance, had escalated to exploitation and ensuing command-and-control activity. Following an SMB brute-force attempt, Darktrace DETECT identified a successful DCSync attack.

A DCSync attack occurs when a malicious actor impersonates a domain controller in an effort to gather sensitive information, such as user credentials and passwords hashes, by replicating directory services [1]. In this case, a device sent various successful DRSGetNCChanges operation requests to the DRSUAPI endpoint.

Data Exfiltration

Around the same time, Darktrace detected the compromised server transferring a high volume of data to rare external endpoints associated with Bublup, a third-party project management application used to save and share files. Although the actors attempted to avoid the detection of security tools by using a legitimate file storage service, Darktrace understood that this activity represented a deviation in this device’s expected pattern of life.

In one instance, around 8 GB of data was transferred, and in another, over 4 GB, indicating threat actors were employing a tactic known as ‘low and slow’ exfiltration whereby data is exfiltrated in small quantities via multiple connections, in an effort to mask their suspicious activity. While this tactic may have evaded the detection of traditional security measures, Darktrace’s anomaly-based detection allowed it to recognize that these two incidents represented a wider exfiltration event, rather than viewing the transfers in isolation.

Impact

Finally, Darktrace began to observe a large amount of suspicious SMB activity on the affected devices, most of which was SMB file encryption. DETECT observed the file extension ‘uw9nmvw’ being appended to many files across various internal shares and devices. In addition to this, a potential ransom note, ‘RECOVER-uw9nmvw-FILES.txt’, was detected on the network shortly after the start of the attack.

Figure 4: Depiction of the high-volume of suspicious SMB activity, including file encryption.


Conclusion

Ultimately, this incident show cases how Darktrace was able to successfully identify an emerging ransomware attack using its unrivalled anomaly-based detection capabilities, without having to rely on any previously established threat intelligence. Not only was Darktrace DETECT able to identify the ransomware at multiple stages of the kill chain, but it was also able to uncover the anomalous activity that took place in the buildup to the attack itself.

As the attack progressed along the cyber kill chain, escalating in severity at every juncture, DETECT was able to provide full visibility over the events. Through the successful identification of compromised devices, anomalous administrative credentials usage and encrypted files, Darktrace was able to greatly assist the customer, ensuring they were well-equipped to contain the incident and begin their incident management process.

Darktrace would have been able to aid the customer even further had they enabled its autonomous response technology on their network. Darktrace RESPOND would have taken targeted, mitigative action as soon as suspicious activity was detected, preventing the malicious actors from achieving their goals.

Credit to: Natalia Sánchez Rocafort, Cyber Security Analyst, Patrick Anjos, Senior Cyber Analyst.

MITRE Tactics/Techniques Mapping

RECONNAISSANCE

Scanning IP Blocks  (T1595.001)

RECONNAISSANCE

Vulnerability Scanning  (T1595.002)

IMPACT

Service Stop  (T1489)

LATERAL MOVEMENT

Taint Shared Content (T1080)

IMPACT

Data Encrypted for Impact (T1486)

INITIAL ACCESS

Replication Through Removable Media (T1200)

DEFENSE EVASION

Rogue Domain Controller (T1207)

COMMAND AND CONTROL

Domain Generation Algorithms (T1568.002)

EXECUTION

Windows Management Instrumentation (T1047)

INITIAL ACCESS

Phishing (T1190)

EXFILTRATION

Exfiltration Over C2 Channel (T1041)

IoC Table

IoC ----------- TYPE ------------- DESCRIPTION + PROBABILITY

CVD --------- credentials -------- Possible compromised credential

.UW9NMVW - File extension ----- Possible appended file extension

RECOVER-UW9NMVW-FILES.TXT - Ransom note - Possible ransom note observed

84.32.188[.]186 - IP address ------ C2 Endpoint

AS.EXECSVCT[.]COM - Hostname - C2 Endpoint

ZX.EXECSVCT[.]COM - Hostname - C2 Endpoint

QW.EXECSVCT[.]COM - Hostname - C2 Endpoint

EXECSVCT[.]COM - Hostname ------ C2 Endpoint

15.197.130[.]221 --- IP address ------ C2 Endpoint

AS59642 UAB CHERRY SERVERS - ASN - Possible ASN associated with C2 Endpoints

108.156.28[.]43

108.156.28[.]22

52.84.93[.]26

52.217.131[.]241

54.231.193[.]89 - IP addresses - Possible IP addresses associated with data exfiltration

103.203.59[.]3 -IP address ---- Possible IP address associated with initial attack vector

References:

[1] https://blog.netwrix.com/2021/11/30/what-is-dcsync-an-introduction/

[2] https://www.easeus.com/computer-instruction/delete-system32.html#:~:text=System32%20is%20a%20folder%20on,DLL%20files%2C%20and%20EXE%20files.

[3] https://www.techtarget.com/searchwindowsserver/definition/Windows-Management-Instrumentation#:~:text=WMI%20provides%20users%20with%20information,operational%20environments%2C%20including%20remote%20systems.

[4] https://www.virustotal.com/gui/ip-address/103.203.59[.]3

[5] https://otx.alienvault.com/indicator/ip/15.197.130[.]221

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
Natalia Sánchez Rocafort
Cyber Security Analyst

More in this series

No items found.

Blog

/

Compliance

/

May 26, 2026

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

cip-015, utilities, cybersecurityDefault blog imageDefault blog image

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.

Continue reading
About the author
Jeffrey Macre
Principal Industrial Security Solutions Architect

Blog

/

Email

/

May 26, 2026

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

Man at a computerDefault blog imageDefault blog image

Darktrace / EMAIL is an implementation of the Darktrace methodology – a multi-layered AI system built into a single product. As with other Darktrace products, Darktrace / EMAIL learns the expected behaviours of an organization and its employees to identify novel threats and anomalous activity.

The diagram below represents the architecture of Darktrace / EMAIL’s multi-layered AI: a structured visualization of how intelligence is built, step by step, from raw data to actionable insight. Each layer plays a distinct role, feeding into the next: collecting data, understanding behaviour, analysing intent, making decisions, and presenting clear outcomes.

It all starts with an email

In this blog, we’ll follow a malicious email as it passes through the Darktrace / EMAIL system, showing exactly what happens as it travels through each layer of the pyramid, from basic data extraction to AI-powered metric creation, and finally deciding on any autonomous actions.

Let’s take this example email. As an end-user, you can see that this is an obvious extortion attempt where an adversary is threatening legal action if money isn’t paid within 24 hours, but how does Darktrace figure that out?

Part 1: Data Gathering

Processing of an email begins on point-of-transit for all inbound, outbound, or lateral emails. The first step is to extract information directly. This includes taking information from the headers (such as sending and receiving addresses, sender IP address, routing, and authentication protocols), as well as extraction of raw HTML and CSS data from the email itself.

This directly extracted information only allows for immediate surface level analysis, such as identifying signature-based attacks (known malicious addresses / domains), but is insufficient for identifying novel threats, complex attacks, or potential email or vendor compromise. This is where Darktrace’s AI analysis shines.

In this example, the SPF, DKIM, and DMARC authentication all passed successfully, showing that even malicious emails can still bypass these signature-based checks. Even with this success, Darktrace will continue to analyse the email.

Diving deeper into the technical information, we can see further information extracted from the headers, including aggregations from the header information, historical calculations such as the frequency and volume of emails to and from a particular domain, and much more.

Part 2: Social Graphing

Social Graphing involves the analysis of sending and receiving behaviours of different mailboxes to create peer-groups. Mailboxes who often send and receive to and from the same mailboxes, or exhibit other correlated behaviours, will be clustered together using a collection of unsupervised AI clustering systems. These groups may represent uses in the same teams who perform similar activity, groups of external facing mailboxes which often receive unsolicited emails, or groups of VIP users (such as C-suite or executives).

Social graphing is an essential component of Darktrace’s pattern of life analysis. This clustering allows Darktrace to understand the responsibilities of individuals – for example, behaviours which are anomalous for one group of users may be completely expected of another group.

In our example, the email was sent to 3 different users within the organization. As part of the social graphing, an “Association Anomaly” is calculated which indicates the likelihood that these users would receive emails from this user or domain, based on historical patterns.

Part 3: Metric Calculation

Metrics are calculated for every email, representing more complex characteristics of an email which can’t be directly extracted. Darktrace / EMAIL features over 1000 unique metrics, calculated both algorithmically and using an ensemble of AI systems.

Algorithmically calculated (non-AI) metrics include further historical calculations, and counts of features such as code blocks, and hidden text, to name a few.

AI-driven metrics include Inducement Classification which uses Natural Language Processing to identify potential phishing, solicitation, or extortion attempts; Named Entity Recognition to identify PII and other sensitive data within an email to support Data Loss Prevention; and many more.

We can follow our example email through this process and view the outcome of these metric calculations. Looking at the language metrics for this email, we can see that our email has reported a high extortion inducement, along with identification of banking information and language indicating urgency.

Part 4: Evaluation and Combination Engine (models)

Once all metrics have been calculated for an email, it gets sent to an evaluation and combination engine where the metrics are compared against blocks of logic to determine if an email contains a threat. One key model which alerted for this example message was a model to tag and block extortion attempts.

Since our example email has a high inducement score for extortion, along the presence of a bitcoin wallet address in the message, this model alerts. When a model in the engine is activated, actions are taken – in this case adding a tag to the email to flag it as extortion in the console and hold the email to prevent it from reaching the end-user mailbox.

Part 5: Meta-Modelling and Actions

Once the models have been run, the actions are taken against the email. If the email hasn’t been blocked or held, this is the point where it will reach the end-user's mailbox.

In the Darktrace / EMAIL UI, all actions models which alerted for an email and actions taken as a result can be seen. At the top of this page, you can see the alert indicating an extortion attempt along with the action to hold the message.

Alongside this, a meta-classifier is used to calculate an overall anomaly score for each email, based on how much the email differs from the pattern of life for the user. The score of the email is boosted by any actions that have taken place.

Part 6: Campaign Clustering

All emails are passed through the Darktrace / EMAIL campaign clustering system. This system creates clusters based on related features within the emails to identify groups of emails with the same sender or intent.

In our case, the email was identified as part of a campaign, alongside other emails which were also identified as extortion attempts against a small group of recipients.

Email campaigns may have additional actions applied to them if the campaign is deemed malicious, and in this case, you can see that the autonomous response was to hold all emails in the campaign. This means that if an email manages to avoid being blocked in the evaluation and combination engine but gets identified as part of the campaign, the hold action will be applied to it retroactively.

Part 7: Cyber AI Analyst

Darktrace’s Cyber AI Analyst presents key information and anomaly indicators for each email, such as further information about authentication, specific metrics, or other identified anomalies and mismatches.

Cyber AI Analyst can also utilize data from Darktrace / EMAIL to enhance its investigation of incidents from other Darktrace products, correlating relevant information to build a fuller picture. More information about the Cyber AI Analyst is available in the Darktrace AI Arsenal.

Part 8: Data Presentation (UI)

Once all processing has taken place against the email, it is presented in the Darktrace / EMAIL UI. Here, members of the SOC team can investigate incidents and anomalies, interact with malicious emails to see why they were blocked, and much more.

Our email stands out here with its 100 anomaly score. Every email which passes through a Darktrace / EMAIL will undergo the same thorough and rigorous analysis to identify potential risks, apply autonomous actions where required, and will ultimately be assigned a score to be displayed here. By providing a single overall score in the UI, rather than presenting emails in full, Darktrace / EMAIL allows SOC teams to more easily identify which emails are most important to investigate, increasing efficiency and reducing alert fatigue.

Take the next step

Many email security tools on the market that claim to be AI-driven are in fact bolting AI onto attack-centric approaches, which rely on automating the identification of known threats. These approaches struggle, and will continue to struggle, with adapting to novel, AI-generated threats.

By analyzing every email within its deeply integrated, multi-layered AI system, Darktrace / EMAIL is able to identify the subtle threats that others miss. This depth not only improves detection accuracy, but enables confident, autonomous action, giving security teams clearer insight into AI outcomes and greater control while supporting users.

For a full deep dive into each stage of the AI system, check out the white paper: A Guide to the Multi-Layered AI in Darktrace / EMAIL

Learn more about securing AI in your enterprise.

[related-resource]

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