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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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July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

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Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician

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July 1, 2026

5 Ways AI is changing traditional security models according to modern CISOs

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The Reality of Securing AI in Motion

Traditional security tools were built for environments defined by fixed rules and predictable workflows. But AI behavior is non-deterministic. The same prompt can produce different outcomes, and risk often emerges gradually as AI behavior adapts, and permissions drift over time. This creates a constantly shifting environment where security teams are working to define control in a system that resists stability. “In AI security, yesterday's priorities can become tomorrow's blind spots. The landscape shifts that fast,” warned the SVP and Head of Technology and Cybersecurity of a real estate investment trust. Conventional approaches, which rely on establishing and maintaining a steady baseline, struggle to keep up with that level of change.

At the same time, AI adoption is accelerating across organizations, often faster than security teams can implement the controls needed to manage it. “The car is being built while it’s already on the road,” explained the CISO of a global private fund administrator. “The threats we're securing against today won't be the threats we're facing tomorrow. What kept us up three months ago looks nothing like what we're dealing with today.”

As businesses move quickly to unlock value from AI, security teams are left closing gaps in real time, while also facing adversaries who are using AI to make their attacks more scalable, adaptive, and difficult to detect. In this recent roundtable discussion of CISOs and security leaders, five themes emerged around AI cyber risk.  

1. AI agents with human access but no human judgment

In Darktrace’s 2026 State of AI Cybersecurity report, 96% of the surveyed security professionals agree that AI significantly improves the speed and efficiency with which they work. Yet, 92% admitted that they’re concerned with the security implications of the use of AI agents across their workforce.

AI agents now operate with human-level permissions across systems, acting at machine speed, orchestrating actions across platforms, and making decisions without the judgment or caution a person would apply. Unlike human users, they cannot be expected to pause and question whether a given action is appropriate.

Their identities are also difficult to inventory, govern, and audit. As agents become easier to deploy than legacy IT systems ever were, organizations are quickly losing track of what is running, what it has access to, and what it is doing. This creates a growing class of highly privileged, autonomous actors operating without the visibility or oversight that traditional identity and access controls were designed to provide.“While AI adoption is critical to running a modern business, AI alone can’t solve all our cybersecurity challenges,” said a global financial sector CISO. “We still need think critically and use human judgement. Those are two things AI can’t do.”

This lack of human judgment becomes especially risky as new architectures, such as Model Context Protocol (MCP), can expand how agents connect to data, tools, and external systems. By design, MCP enables agents to dynamically discover and interact with new resources, increasing flexibility but also introducing new pathways for unintended access, data exposure, or abuse if not properly governed.

The CISO of a fund administrator highlighted one emerging vector as an example: rogue MCP servers. “Our developers want to move quickly and bring value to the business, but technologies like these can unintentionally expose sensitive data in ways that would never have happened before.”

2. Increased digital complexity and expanded attack surface

AI activity rarely stays contained. A single prompt can trigger a chain of actions across networks, email, cloud infrastructure, SaaS platforms, endpoints, identity systems, and development environments, spanning systems that were never designed to be secured as a single, connected flow. This expands both the scale and complexity of what security teams need to monitor and defend.

Yet no single control has visibility across that entire chain. “You can’t defend effectively what you can’t see,” cautioned the private fund administrator CISO. As AI-driven activity moves fluidly across environments, gaps in coverage become inevitable, creating blind spots that attackers can exploit.

Threat actors are already capitalizing on this lack of visibility. “Threat actors have advanced their use of generative AI to launch more convincing phishing campaigns, automate social engineering, and scale attacks with greater precision down to the individual level,” said the SVP of Technology and Cybersecurity for the real estate investment trust. What was once manual and targeted can now be automated and personalized at scale, making attacks harder to detect and easier to execute.

At the same time, the pace of exploitation is accelerating. As a global CISO operating across 40+ countries described it: “Zero-day vulnerabilities are no longer zero day; it’s minus one day. By the time you get to it and address it, it’s already a problem.” By the time risk is identified, it has often already been realized.

The result is a rapidly expanding and increasingly interconnected attack surface that challenges security teams to maintain visibility, context, and control across AI-driven activity.

3. Shadow AI is already everywhere

76% of organizations now cite shadow AI as a problem, one that is spreading through organizations in ways that are hard to track and even harder to control.

Employees are experimenting with publicly available Gen AI tools. Teams are spinning up low-code automations on their own. SaaS providers are quietly embedding AI into existing products. Developers are plugging AI services directly into workflows, often without pausing to consider what that exposure means.

The result is a lack of visibility into:

  • What AI tools are being used
  • What data those tools can access
  • Where prompts and outputs are going
  • Which AI agents are interacting with enterprise systems

The SVP of Cybersecurity at a real estate investment trust described the shift: “Before, I was worried about someone sending data erroneously to their personal email. Now we have all these agents online that people are utilizing, and we’re looking at those vectors as well.” For security teams, this means operating without a complete view of how AI is being used, what it can access, and where risk may already be emerging.

4. Built-in guardrails are not enough

Organizations often assume that native AI guardrails or provider-level controls are sufficient to manage AI risk. But securing AI requires ongoing visibility, oversight, and governance, not just controls configured at deployment. "It’s a misconception that adopting AI is going to solve all your problems,” warns a global financial services CISO.

Security leaders are increasingly recognizing the limitations of these controls as:

  • Fragmented and difficult to enforce consistently across multiple AI systems, workflows, and environments
  • Ambiguous in terms of accountability due to shared responsibility for AI governance between IT, security, developers, business teams, and third-party providers
  • Limited in end-to-end oversight, leaving gaps that stretch from the initial prompt all the way through to the downstream impact of an agent's actions

Securing AI demands more than simple prompt filtering or static policy enforcement. It requires understanding intent, behavior, and context across both human and AI activity.

The next phase of cybersecurity: securing AI

To safely and responsibly adopt AI at scale, organizations need a new operational model for cybersecurity that’s capable of:

• Understanding AI behavior

• Identifying risk in real time

• Maintaining governance without slowing innovation

The CSO of a $10 billion municipal utility organization described the challenge with precision: “We have to move at the speed of innovation and risk, because both are accelerating faster than ever.”

Embrace AI with confidence with Darktrace / SECURE AI

Darktrace has introduced Darktrace / SECURE AI™, a new product within the Darktrace ActiveAI Security Platform™  ,designed to provide enterprise-wide security for AI by applying industry leading behavioral analysis to how prompts, agents, and AI systems are used.

Darktrace / SECURE AITM delivers real-time visibility and control across Enterprise and SaaS GenAI prompts, AI agent identities, development and production environments, and Shadow AI - detecting even subtle misuse, misconfiguration, and drift that traditional, rule-based controls simply do not understand. By interpreting context and intent across humans and machines, Darktrace enables organizations to adopt AI at scale without introducing unmanaged risk

What makes this possible is Darktrace’s decade-long maturity and expertise in behavioral understanding and AI-native cybersecurity. Achieved with Self-Learning AI that has been proven across more than 10,000 organizations, Darktrace understands what “normal” looks like for a business, across its users, systems, and now AI, so that meaningful deviations can be detected and acted on before they become incidents.

With one CISO describing Darktrace’s Self-Learning AI as “a leap forward compared to other tools” and another as a “force multiplier,” the technology can interpret ambiguous interactions, understand how access accumulates over time, and recognize when behavior, human or machine, begins to drift.

“Strategically, we’re looking to gain more visibility into how AI is operating across the environment and achieve greater control over what AI should be allowed to access and do,” shared the CISO at a private fund administrator.  

“What I’ve seen from Darktrace / SECURE AI is extremely promising. I have tremendous confidence in Darktrace’s vision for where this is headed and its ability to execute on this new solution.”

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