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August 25, 2020

Emotet Resurgence: Email & Network Defense Insights

Explore how Darktrace's defense in depth strategy combats Emotet's resurgence in email and network layers, ensuring robust cybersecurity.
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
Max Heinemeyer
Global Field CISO
Written by
Dan Fein
VP, Product
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25
Aug 2020

The Emotet banking malware first emerged in 2014, and has since undergone multiple iterations. Emotet seeks to financially profit from a range of organizations by spreading rapidly from device to device and stealing sensitive financial information.

Darktrace’s AI has detected the return of this botnet after a five month absence. The new Spamware campaign has hit multiple industries through highly sophisticated phishing emails, containing either URLs linking to the download of a macro-containing Microsoft Word document or an attachment of the document itself. This iteration uses new variants of infrastructure and malware that were unknown to threat intelligence lists – thus easily bypassing static, rule-based defenses.

In this blog post, we investigate the attack from two angles. The first documents a case where Emotet successfully infiltrated a company’s network, where it was promptly detected and alerted on by the Enterprise Immune System. We then explore two customers who had extended Darktrace’s Cyber AI coverage to the inbox. While these organizations were also targeted by this latest Emotet campaign, the malicious email containing the Emotet payload was identified and blocked by Antigena Email.

Case study one: Detecting Emotet in the network

Figure 1: A timeline of the attack

This first case study looks at a large European organization spanning multiple industries, including healthcare, pharmaceuticals, and manufacturing. Darktrace’s AI was monitoring over 2500 devices when the organization became a victim of this new wave of Emotet.

The attack entered the business via a phishing email that fell outside of Darktrace’s scope in this particular deployment, as the customer had not yet activated Antigena Email. Either a malicious link or a macro-embedded Word document in the email directed a device to the malicious payload.

Darktrace’s Enterprise Immune System witnessed SSL connections to a 100% rare external IP address, and detected a Kernel crash on the device shortly afterwards, indicating potential exploitation.

Following these actions, the desktop began to beacon to multiple external endpoints using self-signed or invalid SSL certificates. The observed endpoints had previously been associated with Trickbot C2 servers and the Emotet malware. The likely overall dwell time – that is the length of time an attacker has free reign in an environment before they are eradicated – was in this instance around 24 hours, with most of the activity taking place on July 23.

The device then made a large number of new and unusual internal connection attempts over SMB (port 445) to 97 internal devices during a one-hour period. The goal was likely lateral movement, possibly with the intention to infect other devices, download additional malware, and send out more spam emails.

Darktrace’s AI had promptly alerted the security team to the initial rare connections, but when the device attempted lateral movement it escalated the severity of the alert. The security team was able to remediate the situation before further damage was done, taking the desktop offline.

This overview of the infected device shows the extent of the anomalous behavior, with over a dozen Darktrace detections firing in quick succession.

Figure 2: A graph showing unusual activity in combination with the large number of model breaches on July 23

Figure 3: A list of all model breaches occurring over a small time on the compromised device

Case study two: Catching Emotet in the email environment

While Darktrace’s Enterprise Immune System allows us to visualize the attack within the network, Antigena Email has also identified the Emotet phishing campaign in many other customer environments and stopped the attack before the payload could be downloaded.

One European organization was hit by multiple phishing emails associated with Emotet. These emails use a number of tactics, including personalized subject lines, malicious attachments, and hidden malicious URLs. However, Darktrace’s AI recognized the emails as highly anomalous for the organization and prevented them from reaching employees’ inboxes.

Figure 4: A snapshot of Antigena Email’s user interface. The subject line reads ‘Notice of transfer.’

Despite claiming to be from CaixaBank, a Spanish financial services company, Antigena Email revealed that the email was actually sent from a Brazilian domain. The email also contained a link that was hidden behind text suggesting it would lead to a CaixaBank domain, but Darktrace recognized this as a deliberate attempt to mislead the recipient. Antigena Email is unique in its ability to gather insights from across the broader business, and it leveraged this ability to reveal that the link in fact led to a WordPress domain that Darktrace’s AI identified as 100% rare for the business. This would not have been possible without a unified security platform analyzing and comparing data across different parts of the organization.

Figure 5: The malicious links contained in the email

The three above links surfaced by Darktrace are all associated with the Emotet malware, and prompt the user to download a Word file. This document contains a macro with instructions for downloading the actual virus payload.

Another email targeting the same organization contained a header suggesting it was from Vietnam. The sender had never been in any previous correspondence across the business, and the single, isolated link within the email was also revealed to be a 100% rare domain. The website displayed when visiting the domain imitates a legitimate printing business, but appears hastily made and contained a similar malicious payload.

In both cases, Darktrace’s AI recognized these as phishing attempts due to its understanding of normal communication patterns and behavior for the business and held the emails back from the inbox, preventing Emotet from entering the next phase of the attack life cycle.

Case study three: A truly global campaign

Darktrace has seen Emotet in attacks targeting customers around the world, with one of the most recent campaigns aimed at a food production and distribution company in Japan. This customer received six Emotet emails across July 29 and July 30. The senders spoofed Japanese names and some existing Japanese companies, including Mitsubishi. Antigena Email successfully detected and actioned these emails, recognizing the spoofing indicators, ‘unspoofing’ the emails, and converting the attachments.

Figure 6: A second Emotet email targeting an organization in Japan

Revealing a phish

Both the subject line and the filename translate to “Regarding the invoice,” followed by a number and the date. The email imitated a well-known Japanese company (三菱食品(株)), with ‘藤沢 昭彦’ as a common Japanese name and the appended ‘様’ serving a similar function to ‘Sir’ or ‘Dr,’ in a clear attempt to mimic a legitimate business email.

A subsequent investigation revealed that the sender’s location was actually Portugal, and the hash values of Microsoft Word attachments were consistent with Emotet. Crucially, at the time of the attack, these file hashes were not publicly associated with any malicious behavior and so could not have been used for initial detection.

Figure 7: Antigena Email shows critical metrics revealing the true source of the email

Surfacing further key metrics behind the email, Antigena Email revealed that the true sender was using a GMO domain name. GMO is a Japanese cloud-hosting company that offers cheap web email services.

Figure 8: Antigena Email reveals the anomalous extensions and mimes

The details of the attachment show that both the extension and mime type is anomalous in comparison to documents this customer commonly exchanges by email.

Figure 9: Antigena Email detects the attempt at inducement

Antigena Email’s models are able to recognize topic anomalies and inducement attempts in emails, regardless of the language they are written in. Despite this email being written in Japanese, Darktrace’s AI was still able to reveal the attempt at inducement, giving the email a high score of 85.

Figure 10: The six successive Emotet emails

The close proximity in which these emails were sent and the fact they all contained URLs consistent with Emotet suggests that they are likely part of the same campaign. Different recipients received the emails from different senders in an attempt to bypass traditional security tools, which are trained to deny-list an individual sender once it is recognized as bad.

A defense in depth

This new campaign and the comeback of the Emotet malware has shown the need for defense in depth – or having multiple layers of security across the different areas of a business, including email, network, cloud and SaaS, and beyond.

Historically, defense in depth has led companies to adopt myriad point solutions, which can be both expensive and challenging to manage. Security leaders are increasingly abandoning point solutions in favor of a single security platform, which not only makes handling the security stack easier and more efficient, but creates synergies between different parts of the platform. Data can be analyzed across different sources and insights drawn from different areas of the organization, helping detect sophisticated attacks that might attempt to exploit a business’ siloed approach to security.

A single platform ultimately reduces the friction for security teams while allowing for effective, company-wide incident investigation. And when a platform approach leverages AI to understand normal behavior rather than looking for ‘known bad’, it can detect unknown and emerging threats – and help prevent damage from being done.

Thanks to Darktrace analyst Beverly McCann for her insights on the above threat find.

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
Max Heinemeyer
Global Field CISO
Written by
Dan Fein
VP, Product

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September 22, 2026

Darktrace / SECURE AI: Extending Behavioral Security for the Age of Agentic AI

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AI is moving faster than the controls built to secure it

Over 80% of Darktrace’s customers now use generative AI services, and the average organization interacted with five different AI provides in August 20261. That level of adoption reflects how quickly AI has become embedded in day-to-day business operations. But as adoption accelerates, so do the opportunities for new forms of exposure that often operate outside traditional security controls. Prompts can expose sensitive information, an over-permissioned agent can turn a routine task into a serious security concern, and an employee using an unsanctioned AI tool to get work done can quietly introduce risk long before the security team is aware of its existence.

Securing AI isn’t a nice-to-have. It's an urgent, board-level requirement for any business that wants AI adoption to be an advantage rather than a liability.

Why traditional security controls fall short

The challenge isn’t a lack of AI security tools, it’s that most security solutions weren’t built for the way AI behaves.  

AI systems are adaptive and, by nature, unpredictable. A prompt can be entirely benign in one context and a serious risk in another. An agent's permissions can look reasonable in isolation and dangerous the moment they're combined with what that agent is actually doing. Rule-based controls, built to catch known signatures and static policy violations, simply aren't designed to catch this kind of subtlety. They might be able to tell you what happened, but they can’t tell you if it mattered.  

Effective AI security requires something different: a deep understanding of what normal looks like across every human, system, and agent in an environment, so the earliest signs of drift, misuse, or compromise stand out as they emerge.

Behavioral understanding is our foundation

Darktrace isn’t reinventing itself to secure AI. We’re extending what we already do. For over a decade, Darktrace has been built on a single premise – that every organization has its own unique, evolving way of operating, shaped by how its systems, users, and devices behave. Understanding that is the only reliable way to catch what rules and signatures miss. Our Adaptive AI™ continuously learns the distinct behaviors, relationships, and operational patterns of every enterprise it protects, building a Unique Behavioral Profile that no other vendor can replicate. It's how we've spent years interpreting ambiguity, uncovering subtle intent, and spotting drift before it becomes dangerous across networks, cloud, identities, email, OT, and endpoints. AI is a new domain, but behavioral security isn't a new discipline for us.

Introducing Darktrace / SECURE AI

Darktrace / SECURE AI helps organizations embrace AI innovation without losing control of how it is used. Delivered through the Darktrace Behavioral Defense Platform™, it provides visibility into AI activity across employees, agents, and AI systems, helping security teams understand how AI is being used throughout their business.

Darktrace then applies behavioral understanding to that activity, adding the context needed to distinguish routine usage from genuine risk. By understanding the relationships between users, agents, systems, and data, / SECURE AI helps organizations move beyond simply observing AI usage to understanding risk, governing behavior against policy, and enabling AI adoption with confidence. The result is greater oversight, stronger governance, and the ability to confidently accelerate AI innovation without creating unmanaged risk.

Securing your AI ecosystem

AI risk doesn't originate from a single source, and effective AI security can't focus on just one layer of the problem. Organizations need visibility, understanding, and governance across the entire AI ecosystem, from the prompts users submit and the agents they create, to the development environments where AI is built and the unsanctioned tools operating outside approved channels. Here's how Darktrace / SECURE AI helps organizations secure each of these areas.

Shadow AI management

Darktrace / SECURE AI helps security teams discover unsanctioned AI services and track usage trends over time using Darktrace telemetry and supported SASE integrations such as Microsoft Entra Global Secure Access. It also extends visibility to Model Context Protocol (MCP) usage, helping teams understand where AI tools and agents are connecting to external services. Through Darktrace platform integrations, organizations can block unsanctioned AI usage or quarantine affected devices when needed, helping them reduce exposure and guide users toward approved options.

AI prompt analysis

Prompts reveal what users are asking AI to do, what information they are sharing, and the outcomes they are trying to produce. Darktrace / SECURE AI provides visibility into prompts, sessions, and responses across supported platforms, including Microsoft Copilot, Copilot Studio, ChatGPT Enterprise, Claude, Salesforce, and AWS Bedrock2. Behavioral analysis detects activity such as attempted jailbreaks, sensitive data exposure, and potential indirect prompt injection, while risk scoring helps analysts prioritize the sessions that need attention. Policy Manager maps organizational AI policies against prompt activity and surfaces potential violations, helping teams govern AI use with direct evidence instead of relying on fragmented logs or inferred intent.

AI agent identities and actions

AI agents have their own permissions, roles, relationships, and access to systems and data. Darktrace / SECURE AI brings agent identities together with the users interacting with them, giving security teams visibility into access, sessions, and connections across supported environments. A real-time audit trail helps teams continuously evaluate whether an agent’s activity remains aligned with its intended purpose, so they can identify excessive access or behavioral drift before it becomes a security issue.

AI agent development risk management

Many AI risks are introduced during development, when agents are created, permissions are assigned, and connections to data sources are established. Darktrace / SECURE AI provides visibility across both low-code and high-code environments. In platforms such as AWS Bedrock, teams can examine AI architecture and the agent identities involved in development and deployment. In low-code environments, such as Copilot Studio, they can observe agents as they are created, monitor whether behavior stays aligned with their intended purpose, and connect prompt activity during development with behavior in production. This helps organizations address misconfigurations and excessive permissions before they reach production.

The promise of AI, secured

The question isn't whether to adopt AI, it's whether security teams can enable secure adoption that allows every business to benefit from AI’s potential.

Darktrace / SECURE AI is built to solve this problem – extending a decade of behavioral understanding to the newest, fastest-moving part of the enterprise, using the same principles which already protect people and hybrid infrastructure.

Darktrace / SECURE AI is generally available now. Discover the product, or get a demo today.

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[1] Based on aggregated Darktrace product telemetry across a fleet of ~8,200 observed deployments, measuring generative AI service usage across accounts monitored in August 2026. The 80%+ figure reflects the share of monitored accounts with any generative AI service usage during the same period.

[2] Microsoft, Copilot, ChatGPT, Claude, Salesforce, and Amazon Bedrock are trademarks of their respective owners.

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Brittany Woodsmall
Senior Product Marketing Manager, AI

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September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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Jason Lusted
AI Governance Advisor
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