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January 9, 2019

Insider Analysis of Emotet Malware

Uncover the secrets of Emotet with our latest Darktrace expert analysis. Learn how to identify and understand trojan horse attacks.
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
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09
Jan 2019

While both traditional security tools and the attacks against them continue to improve, advanced cyber-criminals are increasingly exploiting the weakness inherent to any organization’s security posture: its employees. Designed to mislead such employees into compromising their devices, computer trojans are now rapidly on the rise. In 2018, Darktrace detected a 239% year-on-year uptick in incidents related specifically to banking trojans, which use deception to harvest the credentials of online banking customers from infected machines. And one banking trojan in particular, Emotet, is among the costliest and most destructive malware variants currently imperilling governments and companies worldwide.

Emotet is a highly sophisticated malware with a modular architecture, installing its main component first before delivering additional payloads. Further increasing its subtlety is the fact that Emotet is considered to be ‘polymorphic malware’, since it constantly changes its identifiable features to evade detection by antivirus products. And, as will be subsequently discussed in greater detail, Emotet has advanced persistence techniques and worm-like self-propagation abilities, which render it uniquely resilient and dangerous.

Since its launch in 2014, Emotet has been adapted and repurposed on numerous occasions as its targets have diversified. Initially, Emotet’s primary victims were German banks, from which the malware was designed to steal financial information by intercepting network traffic. By this past year’s end, Emotet had spread far and wide while shifting focus to U.S. targets, resulting in permanently lost files, costly business interruptions, and serious reputational harm.

How Emotet works

(Image courtesy of US-CERT)

Emotet is spread by targeting Windows-based systems via sophisticated phishing campaigns, employing social engineering techniques to fool users into believing that the malware-laden emails are legitimate. For instance, the latest versions of Emotet were delivered by way of Thanksgiving-related emails, which invited their American recipients to open an apparently innocuous Thanksgiving card:

These emails contain Microsoft Word documents that are either linked or attached directly. The Word files, in turn, act as vectors for malicious macros, which must be explicitly enabled by the user to be executed. For security reasons, running macros by default is disabled in most of the latest Microsoft application versions, meaning that the cyber-criminals responsible must resort to tricking users in order to enable them — in this case, by enticing them with the Thanksgiving card.

Once the macros are enabled, the Word file is executed and a PowerShell command is activated to retrieve the main Emotet component from compromised servers. The trojan payload is then downloaded and executed into the victim’s system. As mentioned above, Emotet payloads are polymorphic, often allowing them to slip past conventional security tools undetected.

How Emotet persists and propagates

Once Emotet has been executed on the victim’s device, it begins deploying itself with two main objectives: (1) achieving persistence and (2) spreading to more machines. To achieve the first aim, which involves resisting a reboot and various attempts at removal, Emotet does the following:

  • Creates scheduled tasks and registry key entries, ensuring its automatic execution during every system start-up.
  • Registers itself by creating files that have randomly generated names in system root directories, which are run as Windows services.
  • Typically stores payloads in paths located off AppData\Local and AppData\Roaming directories that it masks with names that appear legitimate, such as ‘flashplayer.exe’.

Emotet’s second key goal is that of spreading across local networks and beyond in order to infect as many machines as possible. To this end, Emotet first gathers information on both the victim’s system itself and the operating system it uses. Following this reconnaissance stage, it establishes encrypted command and control communications (C2) with its parent infrastructure before determining which payloads it will deliver. After reporting a new infection, Emotet downloads modules from the C2 servers, including:

  • WebBrowserPassView: A tool that steals passwords from most common web browsers like Chrome, Safari, Firefox and Internet Explorer.
  • NetPass.exe: A legitimate tool that recovers all the network passwords stored on the system for the current logged-on user.
  • MailPassView: A tool that reveals passwords and account details for popular email clients, such as Hotmail, Gmail, Microsoft Outlook, and Yahoo! Mail.
  • Outlook PST scraper: A module that searches Outlook’s messages to obtain names and email addresses from the victim’s Outlook account.
  • Credential enumerator: A module that enumerates network resources and attempts to gain access to other machines via SMB enumeration and brute-forcing connections.
  • Banking trojans: These include Dridex, IceID, Zeus Panda, Trickbot and Qakbot, all of which harvest banking account information via browser monitoring routines.

Whilst the WebBrowserPassView, NetPass.exe and MailPassView modules are able to steal the compromised user’s credentials, the PST scraper module can ransack the user’s contact list of friends, family members, colleagues and clients, enabling Emotet to self-propagate by sending phishing emails to those contacts. And because such emails are sent from the hijacked accounts of known acquaintances and loved ones, their recipients are more likely to open their infected attachments and links.

Emotet’s other self-propagation method is via brute-forcing credentials using various password lists, with the intent of gaining access to other machines within the network. When unsuccessful, the malware’s repeated failed login attempts can cause users to become locked out of their accounts, and when successful, the victims may become infected without even clicking on a malicious link or attachment. These tactics have collectively made Emotet remarkably durable and widespread. Indeed, in line with Darktrace’s discovery that incidents related to banking trojans have increased by 239% from 2017 to 2018, Emotet alone recorded a 39% increase, and the worst may be yet to come.

How AI fights back

Emotet presents significant challenges for traditional security tools, both because it exploits the ubiquitous vulnerability of human error, and because it is designed specifically to bypass endpoint solutions. Yet unlike such traditional tools, Darktrace leverages unsupervised machine learning algorithms to detect cyber-threats that have already infiltrated the network. Modelled after the human immune system, Darktrace AI works by learning the individual ‘pattern of life’ of every user, device, and network that it safeguards. From this ever-evolving sense of ‘self,’ Darktrace can differentiate between normal and anomalous behavior, allowing it to identify cyber-attacks in much the same way that our immune system spots harmful germs.

Recently, Darktrace’s AI models managed to detect a machine on a clients’ network that was experiencing active signs of an Emotet infection. The device was observed downloading a suspicious file and, shortly thereafter, began beaconing to a rare external destination, likely reporting the infection to a C2 server.

The device was then observed moving laterally across the network by performing brute force activities. In fact, Darktrace detected thousands of Kerberos failed logins, including to administrative accounts, as well as multiple SMB session failures that used a range of common usernames, such as ‘admin’ and ‘exchange’. Below is a graph showing the SMB and Kerberos brute-force activity on the breached device:

In addition to the brute-forcing activity performed by the credential enumerator module, Darktrace also detected another payload that was potentially functioning as an email spammer. The infected machine started to make a high number of outgoing connections over common email ports. This activity is consistent with Emotet’s typical spreading behavior, which revolves around sending emails to the victim’s hijacked email contacts. Below is an image of Darktrace models breached during the reported Emotet infection:

By forming a comprehensive understanding of normalcy, Darktrace can flag even the most minute anomalies in real time, thwarting subtle threats like Emotet that have already circumvented the network perimeter. To counter such advanced banking trojans, cyber AI defenses like Darktrace have become an organizational necessity.

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

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January 6, 2026

How a leading bank is prioritizing risk management to power a resilient future

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As one of the region’s most established financial institutions, this bank sits at the heart of its community’s economic life – powering everything from daily transactions to business growth and long-term wealth planning. Its blend of physical branches and advanced digital services gives customers the convenience they expect and the personal trust they rely on. But as the financial world becomes more interconnected and adversaries more sophisticated, safeguarding that trust requires more than traditional cybersecurity. It demands a resilient, forward-leaning approach that keeps pace with rising threats and tightening regulatory standards.

A complex risk landscape demands a new approach

The bank faced a challenge familiar across the financial sector: too many tools, not enough clarity. Vulnerability scans, pen tests, and risk reports all produced data, yet none worked together to show how exposures connected across systems or what they meant for day-to-day operations. Without a central platform to link and contextualize this data, teams struggled to see how individual findings translated into real exposure across the business.

  • Fragmented risk assessments: Cyber and operational risks were evaluated in silos, often duplicated across teams, and lacked the context needed to prioritize what truly mattered.
  • Limited executive visibility: Leadership struggled to gain a complete, real-time view of trends or progress, making risk ownership difficult to enforce.
  • Emerging compliance pressure: This gap also posed compliance challenges under the EU’s Digital Operational Resilience Act (DORA), which requires financial institutions to demonstrate continuous oversight, effective reporting, and the ability to withstand and recover from cyber and IT disruptions.
“The issue wasn’t the lack of data,” recalls the bank’s Chief Technology Officer. “The challenge was transforming that data into a unified, contextualized picture we could act on quickly and decisively.”

As the bank advanced its digital capabilities and embraced cloud services, its risk environment became more intricate. New pathways for exploitation emerged, human factors grew harder to quantify, and manual processes hindered timely decision-making. To maintain resilience, the security team sought a proactive, AI-powered platform that could consolidate exposures, deliver continuous insight, and ensure high-value risks were addressed before they escalated.

Choosing Darktrace to unlock proactive cyber resilience

To reclaim control over its fragmented risk landscape, the bank selected Darktrace / Proactive Exposure Management™ for cyber risk insight. The solution’s ability to consolidate scanner outputs, pen test results, CVE data, and operational context into one AI-powered view made it the clear choice. Darktrace delivered comprehensive visibility the team had long been missing.

By shifting from a reactive model to proactive security, the bank aimed to:

  • Improve resilience and compliance with DORA
  • Prioritize remediation efforts with greater accuracy
  • Eliminate duplicated work across teams
  • Provide leadership with a complete view of risk, updated continuously
  • Reduce the overall likelihood of attack or disruption

The CTO explains: “We needed a solution that didn’t just list vulnerabilities but showed us what mattered most for our business – how risks connected, how they could be exploited, and what actions would create the biggest reduction in exposure. Darktrace gave us that clarity.”

Targeting the risks that matter most

Darktrace / Proactive Exposure Management offered the bank a new level of visibility and control by continuously analyzing misconfigurations, critical attack paths, human communication patterns, and high-value assets. Its AI-driven risk scoring allowed the team to understand which vulnerabilities had meaningful business impact, not just which were technically severe.

Unifying exposure across architectures

Darktrace aggregates and contextualizes data from across the bank’s security stack, eliminating the need to manually compile or correlate findings. What once required hours of cross-team coordination now appears in a single, continuously updated dashboard.

Revealing an adversarial view of risk

The solution maps multi-stage, complex attack paths across network, cloud, identity systems, email environments, and endpoints – highlighting risks that traditional CVE lists overlook.

Identifying misconfigurations and controlling gaps

Using Self-Learning AI, Darktrace / Proactive Exposure Management spots misconfigurations and prioritizes them based on MITRE adversary techniques, business context, and the bank’s unique digital environment.

Enhancing red-team and pen test effectiveness

By directing testers to the highest-value targets, Darktrace removes guesswork and validates whether defenses hold up against realistic adversarial behavior.

Supporting DORA compliance

From continuous monitoring to executive-ready reporting, the solution provides the transparency and accountability the bank needs to demonstrate operational resilience frameworks.

Proactive security delivers tangible outcomes

Since deploying Darktrace / Proactive Exposure Management, the bank has significantly strengthened its cybersecurity posture while improving operational efficiency.

Greater insight, smarter prioritization, stronger defensee

Security teams are now saving more than four hours per week previously spent aggregating and analyzing risk data. With a unified view of their exposure, they can focus directly on remediation instead of manually correlating multiple reports.

Because risks are now prioritized based on business impact and real-time operational context, they no longer waste time on low-value tasks. Instead, critical issues are identified and resolved sooner, reducing potential windows for exploitation and strengthening the bank’s ongoing resilience against both known and emerging threats.

“Our goal was to move from reactive to proactive security,” the CTO says. “Darktrace didn’t just help us achieve that, it accelerated our roadmap. We now understand our environment with a level of clarity we simply didn’t have before.”

Leadership clarity and stronger governance

Executives and board stakeholders now receive clear, organization-wide visibility into the bank’s risk posture, supported by consistent reporting that highlights trends, progress, and areas requiring attention. This transparency has strengthened confidence in the bank’s cyber resilience and enabled leadership to take true ownership of risk across the institution.

Beyond improved visibility, the bank has also deepened its overall governance maturity. Continuous monitoring and structured oversight allow leaders to make faster, more informed decisions that strategically align security efforts with business priorities. With a more predictable understanding of exposure and risk movement over time, the organization can maintain operational continuity, demonstrate accountability, and adapt more effectively as regulatory expectations evolve.

Trading stress for control

With Darktrace, leaders now have the clarity and confidence they need to report to executives and regulators with accuracy. The ability to see organization-wide risk in context provides assurance that the right issues are being addressed at the right time. That clarity is also empowering security analysts who no longer shoulder the anxiety of wondering which risks matter most or whether something critical has slipped through the cracks. Instead, they’re working with focus and intention, redirecting hours of manual effort into strategic initiatives that strengthen the bank’s overall resilience.

Prioritizing risk to power a resilient future

For this leading financial institution, Darktrace / Proactive Exposure Management has become the foundation for a more unified, data-driven, and resilient cybersecurity program. With clearer, business-relevant priorities, stronger oversight, and measurable efficiency gains, the bank has strengthened its resilience and met demanding regulatory expectations without adding operational strain.

Most importantly, it shifted the bank’s security posture from a reactive stance to a proactive, continuous program. Giving teams the confidence and intelligence to anticipate threats and safeguard the people and services that depend on them.

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About the author
Kelland Goodin
Product Marketing Specialist

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January 5, 2026

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

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Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

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About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface
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