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November 6, 2023

How PlugX Malware Has Evolved & Adapted

Discover how Darktrace effectively detected and thwarted the PlugX remote access trojan in 2023 despite its highly evasive and adaptive nature.
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
Nahisha Nobregas
SOC Analyst
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06
Nov 2023

What is PlugX Remote Access Trojan?

Understanding remote access trojans (RATs)

As malicious actors across the threat landscape continue to pursue more efficient and effective ways of compromising target networks, all while remaining undetected by security measures, it is unsurprising to see an increase in the use of remote access trojans (RATs) in recent years. RATs typically operate stealthily, evading security tools while offering threat actors remote control over infected devices, allowing attackers to execute a wide range of malicious activities like data theft or installing additional malware.

Definition and general functionality of RATs: A Remote Access Trojan (RAT) is a type of malware that enables unauthorized remote control of an infected computer. Once installed, RATs allow attackers to monitor user activities, steal sensitive information, manipulate files, and execute commands. RATs are typically distributed via phishing emails, malicious attachments, drive-by downloads, or exploiting software vulnerabilities. Due to their ability to provide comprehensive control over a compromised system, RATs pose a significant security threat to individuals and organizations.

Historical overview of PlugX

PlugX is one such example of a RAT that has attributed to Chinese threat actors such as Mustang Panda, since it first appeared in the wild back in 2008. It is known for its use in espionage, a modular and plug-in style approach to malware development. It has the ability to evolve with the latest tactics, techniques, and procedures (TTPs) that allow it to avoid the detection of traditional security tools as it implants itself target devices.

How does PlugX work?

The ultimate goal of any RAT is to remotely control affected devices with a wide range of capabilities, which in PlugX’s case has typically included rebooting systems, keylogging, managing critical system processes, and file upload/downloads. One technique PlugX heavily relies on is dynamic-link library (DLL) sideloading to infiltrate devices. This technique involves executing a malicious payload that is embedded within a benign executable found in a data link library (DLL) [1]. The embedded payload within the DLL is often encrypted or obfuscated to prevent detection.

What’s more, a new variant of PlugX was observed in the wild across Papua New Guinea, Ghana, Mongolia, Zimbabwe, and Nigeria in August 2022, that added several new capabilities to its toolbox.

Key capabilities of PlugX

The new variation is reported to continuously monitor affected environments for new USB devices to infect, allowing it to spread further through compromised networks [2]. It is then able to hide malicious files within a USB device by using a novel technique that prevents them from being viewed on Windows operating systems (OS). These hidden files can only be viewed on a Unix-like (.nix) OS, or by analyzing an affected USB devices with a forensic tool [2]. The new PlugX variant also has the ability to create a hidden directory, “RECYCLER.BIN”, containing a collection of stolen documents, likely in preparation for exfiltration via its command and control (C2) channels. [3]

Since December 2022, PlugX has been observed targeting networks in Europe through malware delivery via HTML smuggling campaigns, a technique that has been dubbed SmugX [4].

This evasive tactic allows threat actors to prepare and deploy malware via phishing campaigns by exploiting legitimate HTML5 and JavaScript features [5].

Darktrace Coverage of PlugX

Between January and March 2023, Darktrace observed activity relating to the PlugX RAT on multiple customers across the fleet. While PlugX’s TTPs may have bypassed traditional security tools, the anomaly-based detection capabilities of Darktrace allowed it to identify and alert the subtle deviations in the behavior of affected devices, while Darktrace was able to take immediate mitigative action against such anomalous activity and stop attackers in their tracks.  

C2 Communication

Between January and March 2023, Darktrace detected multiple suspicious connections related to the PlugX RAT within customer environments. When a device has been infected, it will typically communicate through C2 infrastructure established for the PlugX RAT. In most cases observed by Darktrace, affected devices exhibited suspicious C2 connections to rare endpoints that were assessed with moderate to high confidence to be linked to PlugX.

On the network of one Darktrace customer the observed communication was a mix of successful and unsuccessful connections at a high volume to rare endpoints on ports such as 110, 443, 5938, and 80. These ports are commonly associated with POP3, HTTPS, TeamViewer RDP / DynGate, and HTTP, respectively.  Figure 1 below showcases this pattern of activity.

Figure 1: Model Breach Event Log demonstrating various successful and unsuccessful connections to the PlugX C2 endpoint 103.56.53[.]46 via various destination ports.

On another customer’s network, Darktrace observed C2 communication involving multiple failed connection attempts to another rare external endpoint associated with PlugX. The device in this case was detected attempting connections to the endpoint, 45.142.166[.]112 on ports 110, 80, and 443 which caused the DETECT model ‘Anomalous Connection / Multiple Failed Connections to Rare Endpoint’ to breach. This model examines devices attempting connections to a rare external endpoint over a short period of time, and it breached in response to almost all PlugX C2 related activity detected by Darktrace. This highlights Darktrace DETECT’s unique ability to identify anomalous activity which appears benign or uncertain, rather than relying on traditional signature-based detections.

Figure 2: Device Event Log demonstrating various successful and unsuccessful connections to the PlugX C2 endpoint 45.142.166[.]112 via various destination on January 27, 2023.

New User Agent

Darktrace's Self-Learning AI approach to threat detection also allowed it to recognize connections to PlugX associated endpoints that utilized a new user agent. In almost all connections to PlugX endpoints detected by Darktrace, the same user agent, Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36, was observed, illustrating a clear pattern in PlugX-related activity

In one example from February 2023, an affected device successfully connected to an endpoint associated with PlugX, 45.142.166[.]112, while using the aforementioned new user agent, as depicted in Figure 3.

Figure 3: The Device Event log above showcases a successful connection to the PlugX associated IP address, 45.142.166[.]112 using the new user agent ‘Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36’.

On March 21, 2023, Darktrace observed similar activity on a separate customer’s network affected by connections to PlugX. This activity included connections to the same endpoint, 45.142.166[.]112. The connection was an HTTP POST request made via proxy with the same new user agent, ‘Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36’. When investigated further this user agent actually reveals very little about itself and appears to be missing a couple of common features that are typically contained in a user agent string, such as a web browser and its version or the mention of Safari before its build ID (‘537.36’).

Additionally, for this connection the URI observed consisted of a random string of 8 hexadecimal characters, namely ‘d819f07a’. This is a technique often used by malware to communicate with its C2 servers, while evading the detection of signature-based detection tools. Darktrace, however, recognized that this external connection to an endpoint with no hostname constituted anomalous behavior, and could have been indicative of a threat actor communicating with malicious infrastructure, thus the ‘Anomalous Connection / Possible Callback URI’ model was breached.

Figure 4: An affected device was detected using the new user agent, ‘Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36’ while connecting to the rare external endpoint 45.142.166[.]112 via proxy.

Numeric File Download

Darktrace’s detection of PlugX activity on another customer’s network, in February 2023, helped to demonstrate related patterns of activity within the C2 communication and tooling attack phases. Observed PlugX activity on this network followed the subsequent pattern; a connection to a PlugX endpoints is made, followed by a HTTP POST request to a numeric URI with a random string of 8 hexadecimal characters, as previously highlighted. Darktrace identified that this activity represented unusual ‘New Activity’ for this device, and thus treated it with suspicion.

Figure 5: New activity was identified by Darktrace in the Device Event Log shown above for connections to the endpoint 45.142.166[.]112 followed by HTTP POSTs to URIs “/8891431c” and “/ba12b866” on February 15, 2023.

The device in question continued to connect to the endpoint and make HTTP POST connections to various URIs relating to PlugX. Additionally, the user agent `Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36` was again detected for these connections. Figure 6 details the activity captured by Darktrace’s Cyber AI Analyst.

Figure 6: The image above showcases activity captured by Darktrace’s AI Analyst for PlugX connections made on February 15, 2023.

Darktrace detected that during these connections, the device in question attempted to download a suspicious file named only with numbers. The use of numeric file names is a technique often used by threat actors to obfuscate the download of malicious files or programs and bypass traditional security tools. Darktrace understood that the download of a numeric file, coupled with the use of an anomalous new user agent, mean the incident should be treated with suspicion. Fortunately, Darktrace RESPOND was enabled in autonomous response mode during this attack, meaning it was able to automatically block the device from downloading the file, or any other files, from the suspicious external location for a two-hour period, potentially preventing the download of PlugX’s malicious tooling.

Conclusion

Amid the continued evolution of PlugX from an espionage tool to a more widely available malware, it is essential that threat detection does not rely on a set of characteristics or indicators, but rather is focused on anomalies. Throughout these cases, Darktrace demonstrated the efficacy of its detection and alerting on emerging activity pertaining to a particularly stealthy and versatile RAT. Over the years, PlugX has continually looked to evolve and survive in the ever-changing threat landscape by adapting new capabilities and TTPs through which it can infect a system and spread to new devices without being noticed by security teams and their tools.

However, Darktrace’s Self-Learning AI allows it to gain a strong understanding of customer networks, learning what constitutes expected network behavior which in turn allows it to recognize the subtle deviations indicative of an ongoing compromise.

Darktrace’s ability to identify emerging threats through anomaly-based detection, rather than relying on established threat intelligence, uniquely positions it to detect and respond to highly adaptable and dynamic threats, like the PlugX malware, regardless of how it may evolve in the future.

Credit to: Nahisha Nobregas, SOC Analyst & Dylan Hinz, Cyber Analyst

Appendices

MITRE ATT&CK Framework

Execution

  • T1059.003 Command and Scripting Interpreter: Windows Command Shell

Persistence and Privilege Escalation

  • T1547.001 Boot or Logon AutoStart Execution: Registry Run Keys / Startup Folder
  • T1574.001 Hijack Execution Flow: DLL Search Order Hijacking
  • T1574.002 Hijack Execution Flow: DLL Side-Loading
  • T1543.003 Create or Modify System Process: Windows Service
  • T1140 Deobfuscate / Decode Files or Information
  • T1083 File and Directory Discovery

Defense Evasion

  • T1564.001 Hide Artifacts: Hidden Files and Directories
  • T1036.004 Masquerading: Task or Service
  • T1036.005 Masquerading: Match Legitimate Name or Location
  • T1027.006 Obfuscated Files or Information: HTML Smuggling

Credential Access

  • T1056.001 Input Capture: Keylogging

Collection

  • T1105 Ingress Tool Transfer

Command and Control

  • T1573.001 Encrypted Channel: Symmetric Cryptography
  • T1070.003 Mail Protocols
  • T1071.001 Web Protocol

DETECT Model Breaches

  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / New User Agent Followed By Numeric File Download
  • Anomalous Connection / Possible Callback URL

Indicators of Compromise (IoCs)

IoC - Type - Description + Confidence

45.142.166[.]112 - IP - PlugX C2 Endpoint / moderate - high

103.56.53[.]46 - IP - PlugX C2 Endpoint / moderate - high

Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36 - User Agent - PlugX User Agent / moderate – high

/8891431c - URI - PlugX URI / moderate-high

/ba12b866 - URI - PlugX URI / moderate -high

References

1. https://www.crowdstrike.com/blog/dll-side-loading-how-to-combat-threat-actor-evasion-techniques/

2. https://unit42.paloaltonetworks.com/plugx-variants-in-usbs/

3. https://news.sophos.com/en-us/2023/03/09/border-hopping-plugx-usb-worm/

4. https://thehackernews.com/2023/07/chinese-hackers-use-html-smuggling-to.html

5. https://www.cyfirma.com/outofband/html-smuggling-a-stealthier-approach-to-deliver-malware/

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
Nahisha Nobregas
SOC Analyst

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May 27, 2026

How to Evaluate AI Vendors: 5 Key categories for AI Adoption

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Understanding the AI buyers’ market

AI adoption has become a central topic of discussion in boardrooms, drawing growing interest from business leaders. Ultimately, organizations hope that an investment in AI technology will have tremendous returns. However, the process of buying an AI solution is not as straight forward as it appears on the surface.  

While business leaders may be eager to improve productivity across their operations, practitioners responsible for evaluating and selecting AI solutions may not always have the visibility or technical understanding needed to make the right decisions for their business. What is typically marketed as a holistic solution to their most critical problems is usually followed by uncertainty when AI tools are finally operationalized in real environments.

This guide is intended to support security leaders who are under growing pressure to adopt AI tools while navigating complex terminology, vendor claims, and increasingly crowded buying cycles. Ultimately, the goal is to help organizations evaluate and adopt AI in a safe, effective, and well-governed way. To support this, we’ve structured the evaluation framework across five key categories:

  1. Governance, safety, and data controls
  1. Data gathering and training
  1. Model and technique choice
  1. Performance and accuracy validation    
  1. Interpretability, adjustability, and transparency    

What buying AI looks like in cybersecurity

While investing in AI can bring immense benefits to your security team, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.  

With acceleration in AI adoption, accompanied by the recent boom in agentic AI and autonomous agents, CISOs must look “beneath the hood" of these tools to understand how they work, how they are governed, and to ensure the system is secure and compliant with internal policies.

Challenges in the AI buyers’ marketplace  

The AI security software market is buzzing with hype and flashy promises, which, understandably, needs to be addressed with due diligence. Potential buyers, especially in the cybersecurity space, are hesitant when it comes to allowing AI autonomous capabilities across their workflows, and a lack of vendor transparency can exacerbate those feelings.  

Reinforcing this sentiment, research from this year's Darktrace’s State of AI Cybersecurity report shows where confidence and hesitancy emerge amongst potential buyers. On the one hand, security professionals agree that they have good visibility into the logic and reasoning processes their AI solutions use. However, they lack the explainability and trust to allow AI to take independent remedial action.

  • 89% say they have good visibility into the reasoning behind the outputs generated by AI solutions
  • 92% say they need to understand how a defensive AI tool makes decisions before they can trust it
  • Only 14% say they allow AI to act independently, performing autonomous actions without human approval
  • 74% say they are limiting the autonomy of AI taking action in their SOC until explainability improves

Given the desire for trust and explainability we are seeing from buyers, it's important for them to be equipped with the right questions to ask vendors during an assessment or POV of AI tools in order to demystify marketing hype from real operational outcomes.

Below is a list of categories in which buyers can assess AI vendors or AI Service Providers (AISPs) to help reach safe adoption and maximize their ROI.  

5 categories of AI vendor assessment

Darktrace groups these AI-related questions into 5 categories: governance, data and training, model and technique choice, performance validation, and interpretability and adjustability. By asking questions regarding each of these 5 categories, buyers can gain a deeper understanding of how an AISP’s systems work and whether they suit their business requirements.

Governance, safety, and data controls

Governance of AI systems is critical for all AISPs. Whether their platform is based around a single model, or is a more complex, composite AI solution, strong governance is essential to ensure the system is safe, robust, and reliable.

A simple question you could ask is:

What AI governance policies and frameworks do you follow, and/or certifications do you currently maintain?

For more questions you can ask vendors, download the full guide here.

Darktrace is certified to the ISO/IEC 42001 standard, the world’s first AI Management System (AIMS) standard. ISO/IEC 42001 addresses the unique ethical and technical challenges AI poses by setting out a structured way to manage risks such as transparency, accuracy, and misuse. This includes a commitment to ethical AI development, and effective management and monitoring of AI systems both prior to and continually after release.

Data gathering and training

Accurate, meaningful, and unbiased data gathering is the first important step in producing any AI system. An AI model trained using inaccurate, unbalanced, or poor-quality training data will fail to perform optimally.

To alleviate concerns regarding training data quality, a question you could ask is:

What steps do you take to prevent bias in your AI models and training data?

For more questions, download the full guide here.

AISPs should be able to provide information about the steps taken, workflows followed, and auditing performed to reduce AI bias where appropriate. While it’s sometimes impossible to fully remove bias from an AI model, appropriate actions should be taken to mitigate or reduce bias where relevant.

Model and technique choice

Different AI techniques are optimal for different tasks. For example, research from Gartner suggests that relying on a single “one-size-fits-all" model can lead to data gaps, especially in highly specialized domains.

To achieve more accurate and robust AI solutions, AI leaders should move beyond using just one model or technique, embrace composite AI practices, and adopt a holistic AI system perspective.

A straightforward question you could ask is simply:

What type(s) of AI model(s) do you utilize in your solution?

For more questions, download the full guide here.

While specific detailed information about custom systems used by AISPs is likely proprietary, buyers should expect vendors to be able to provide an overview of the broad techniques used. This will allow you as a buyer to determine if the type of model is appropriate for your use case.

Performance and accuracy validation  

Testing and evaluation of performance is essential for all AI systems. Performance analysis should be performed both before release and continually after release to identify potential data or model drift.  

A question you could ask to understand an AISPs testing workflow is:

How do you audit, test, evaluate, verify, and validate your AI model outputs?

For more questions, download the full guide here.

Testing workflows will likely vary depending on the type of model – measurements relevant to one system may not always be relevant to others. Assessment of systems should also extend beyond these standard accuracy and robustness tests, and should also feature physical performance, such as latency and resource consumption.  

Interpretability, adjustability, and transparency  

AI systems are typically a black box, simply providing an output without an explanation of how that output was attained. Interpretability and transparency are critical to ensure that both SOC teams and end-users trust the outputs of a system to be accurate and meaningful.

A question you could ask is:

How do you promote a trust relationship between human analysts and AI outputs?

For more questions, download the full guide here.

In the context of cybersecurity, trust and interpretability are even more essential. This is particularly relevant for generative AI-based systems (including most AI Agents), where the risk of hallucination can reduce trust in responses.

Cybersecurity systems often need to perform autonomous actions to block incoming threats – an email filtering system may hold potentially dangerous emails; a firewall may block malicious inbound connections. If SOC teams can’t trust these systems to perform accurately, these systems may be limited or disabled, critically reducing their defensive power.

Darktrace as an AI-native cybersecurity vendor

Darktrace has been building and applying AI in cybersecurity for over a decade, developing its capabilities alongside an increasingly complex and fast‑moving threat landscape. This experience has resulted in a mature, multi-layered approach to AI, which continuously learns the normal patterns of each organization to understand behavior, interpret context, and identify meaningful deviations — without relying on predefined rules or known attack signatures. Over time, this has enabled a proven behavioral understanding that helps uncover subtle signals of risk that may otherwise be missed.

With the backing of our ISO/IEC 42001 certification, stakeholders, customers, and partners can be confident that Darktrace is responsibly, ethically, and safely developing its AI systems, and managing the use of AI in day-to-day operations in a compliant and secure manner.  

Explore the principles behind Darktrace’s responsible AI approach, informed by collaboration with global experts in academia and governments, detailing how accountability, explainability, and continuous validation are built into its cybersecurity technology.

How Darktrace secures AI systems

Darktrace now brings these capabilities to monitor and respond to risk generated from AI systems across organizations with Darktrace / SECURE AI. This solution analyzes how prompts, agents, and systems are used within the context of each organization, bringing every AI interaction into a single view. This unique approach helps teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI agent activity.

Stay up to date

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

Further Reading on AI in cybersecurity

When deciding to invest in an AI solution, it’s important to understand what this means for you and your organization. The questions presented here are only a starting point in understanding an AI solution and whether it is appropriate for your use case.  

Gain deeper knowledge on applications of AI in cybersecurity and Darktrace’s multi-layered AI in the AI Arsenal White Paper.

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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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