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October 11, 2017

Stealth Attacks: The ‘Matrix Banker’ Reloaded

Over the last few weeks, Darktrace has confidently identified traces of the resurgence of a stealthy attack targeting Latin American companies. Learn more!
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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11
Oct 2017

Overview

Over the last few weeks, Darktrace has confidently identified traces of the resurgence of a stealthy attack targeting Latin American companies. This targeted campaign was first observed between March and June this year. Arbor Networks initially labelled the malware used in the campaign ‘Matrix Banker’. The name used by Proofpoint is ‘Win32/RediModiUpd’. The malware used by the attackers appeared to be still under development when the last report came out in June 2017.

Darktrace has observed an attack wave targeting Mexican companies in August and September 2017. Some of the TTPs (tools, techniques, procedures) observed bear close resemblance to those seen in the ‘Matrix Banker’ attacks earlier this year. The campaign is crafted to be particularly stealthy and to blend into certain networks in Latin America, confirming the suspicion of its targeted nature. Darktrace’s machine learning and AI algorithms were able to identify the infected devices almost instantaneously, despite apparent efforts by the malware author to be covert and stealthy.

Between August and October 2017, Darktrace detected highly anomalous behavior on five seemingly unrelated networks in Mexico. Unlike the original strain of this attack, which was believed to target financial institutions almost exclusively, this latest variant affected customers across a number of industry verticals, suggesting that the threat actors are diversifying their targets. Darktrace has seen the attack hit companies in the healthcare, telecommunications, food and retail sectors.

Infection process

The initial infection vector appears to be phishing emails. The users downloaded the initial piece of malware from compromised Mexican websites. The infected files were Windows executables masqueraded as .mp3 and .gif files. Example downloads are listed below. Darktrace instantly detected the highly anomalous behavior of these downloads, which occurred from 100% rare external domains for the networks, and alerted the respective security teams.

hxxp://gorrasbaratas.com[.]mx/images/sss/sound.mp3 [1]
hxxp://inseltech.com[.]mx/inicio/wp-includes/kk/sound.mp3 [2]

The actual file names of the downloads are ‘logo.gif’.

The ‘Matrix Bankers’ attack tried to conceal malware downloads using masqueraded files in previous attacks. What is interesting about the hacked websites serving the malware is that they are using the .mx top level domain. This localised and targeted technique is used to conceal the traffic and make it blend in with normal network traffic on networks in Mexico.

Following the initial infection, in some cases a second stage malware was downloaded. Darktrace detected this as more anomalous activity since the downloads took place from more 100% rare external destinations:

hxxp://dackdack[.]club/APIv3/modules/nn_grabber_x64.dll [3]
hxxp://dackdack[.]club/APIv3/modules/nn_grabber_x32.dll [4]

Successful second stage downloads were seen to be followed by suspicious HTTP POST beaconing behavior, resembling command and control communication to various domains:

hxxp://kuxkux[.]bit/APIv3/api.php
hxxp://drdrfdd[.]cat/forum/logout.php
hxxp://eaxsess[.]cat/forum/logout.php

Not all targeted companies were seen to receive a second-stage malware download. This might indicate a sophisticated attack plan where the initial generic, covert backdoor is followed by a targeted second-stage payload that is chosen based on the victim and its potential value to the cyber criminals (long term data exfiltration, ransomware, banking Trojan…). Customers reported that infected devices had their anti-virus disabled, or removed by the malware. This showcases that companies cannot solely rely on signature based systems to catch novel, evolving threats.

The beaconing behavior to these 100% unusual external domains was immediately detected as it represented a strong deviation from the devices’ normal ‘pattern of life’. The use of domains hosted on .cat (top level domain used for the Catalan culture and language) indicates that the attackers are highly aware of the cultural context of their target victims and try to make the malware communication blend in with network traffic.

Compromised machines made further repeated DNS requests to the domains below:

dackdack[.]tech
dackdack[.]online
kuykuy[.]bit

At the time of our investigation, the domains below resolved to the following IP address:

142.44.188[.]42
dackdack[.]club
eaxsess[.]cat
kuxkux[.]bit
drdrfdd[.]cat

Closing thoughts

Although final attribution is impossible, the evidence strongly suggests that the campaign described here is similar to the ‘Matrix Banker’ campaign observed in March and June 2017 and might be a continuation of it.

The initial malware was concealing its file types by using different file extensions than their MIME type. More precisely, the use of ‘logo.gif’ has been seen in previous ‘Matrix Banker’ attacks.

There are 3,000 deployments of Darktrace’s AI technology across 70 countries, but all identified instances of this type of compromise are in Latin American organizations.

The ‘Matrix Bankers’ have used Catalan top-level domains in past attacks. In fact, some of the domains used previously are very similar to domains observed here. One domain seen in September was the exact same domain as seen in an earlier attack – just with an additional ‘s’ appended:

Example domains from March/June 2017

trtr44[.]cat
lalax[.]cat
eaxses[.]cat

Example domains from August/October 2017

drdrfdd[.]cat
kuxkux[.]bit
eaxsess[.]cat
kuykuy[.]bit
dackdack[.]tech

Although the domains appear to be randomly generated, a closer look reveals that the ‘Matrix Bankers’ seem to favor generating domain names by using keys that are physically close together on a keyboard, or by repeating phrases one might type in a hurry, when lacking creativity for naming a temporary download (e.g. asdasd.jpeg). We saw this pattern for domain name generation in the March - June ‘Matrix Bankers’ campaign as well as here.

Darktrace’s AI technology was able to detect these stealthy and sophisticated attacks because the way in which they manifest themselves represents a sharp deviation from the normal ‘pattern of life’ within an organization. The threat actors applied a number of techniques to blend into the normal noise of networks, but the self-learning algorithms were quick in detecting the anomalous behavior automatically and in real time.

Footnotes

List of IoCs

dackdack[.]club
dackdack[.]tech
dackdack[.]online
eaxsess[.]cat
kuxkux[.]bit
kuykuy[.]bit
drdrfdd[.]cat
inseltech.com[.]mx
gorrasbaratas.com[.]mx
142.44.188[.]42

[1] VirusTotal analysis of this file
[2] SHA-1: 88f3bdc84908c1fb844b337c535eef2d2b31e1dc
[3] VirusTotal analysis of this file
[4] VirusTotal analysis of this file

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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July 17, 2025

Introducing the AI Maturity Model for Cybersecurity

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AI adoption in cybersecurity: Beyond the hype

Security operations today face a paradox. On one hand, artificial intelligence (AI) promises sweeping transformation from automating routine tasks to augmenting threat detection and response. On the other hand, security leaders are under immense pressure to separate meaningful innovation from vendor hype.

To help CISOs and security teams navigate this landscape, we’ve developed the most in-depth and actionable AI Maturity Model in the industry. Built in collaboration with AI and cybersecurity experts, this framework provides a structured path to understanding, measuring, and advancing AI adoption across the security lifecycle.

Overview of AI maturity levels in cybersecurity

Why a maturity model? And why now?

In our conversations and research with security leaders, a recurring theme has emerged:

There’s no shortage of AI solutions, but there is a shortage of clarity and understanding of AI uses cases.

In fact, Gartner estimates that “by 2027, over 40% of Agentic AI projects will be canceled due to escalating costs, unclear business value, or inadequate risk controls. Teams are experimenting, but many aren’t seeing meaningful outcomes. The need for a standardized way to evaluate progress and make informed investments has never been greater.

That’s why we created the AI Security Maturity Model, a strategic framework that:

  • Defines five clear levels of AI maturity, from manual processes (L0) to full AI Delegation (L4)
  • Delineating the outcomes derived between Agentic GenAI and Specialized AI Agent Systems
  • Applies across core functions such as risk management, threat detection, alert triage, and incident response
  • Links AI maturity to real-world outcomes like reduced risk, improved efficiency, and scalable operations

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How is maturity assessed in this model?

The AI Maturity Model for Cybersecurity is grounded in operational insights from nearly 10,000 global deployments of Darktrace's Self-Learning AI and Cyber AI Analyst. Rather than relying on abstract theory or vendor benchmarks, the model reflects what security teams are actually doing, where AI is being adopted, how it's being used, and what outcomes it’s delivering.

This real-world foundation allows the model to offer a practical, experience-based view of AI maturity. It helps teams assess their current state and identify realistic next steps based on how organizations like theirs are evolving.

Why Darktrace?

AI has been central to Darktrace’s mission since its inception in 2013, not just as a feature, but the foundation. With over a decade of experience building and deploying AI in real-world security environments, we’ve learned where it works, where it doesn’t, and how to get the most value from it. This model reflects that insight, helping security leaders find the right path forward for their people, processes, and tools

Security teams today are asking big, important questions:

  • What should we actually use AI for?
  • How are other teams using it — and what’s working?
  • What are vendors offering, and what’s just hype?
  • Will AI ever replace people in the SOC?

These questions are valid, and they’re not always easy to answer. That’s why we created this model: to help security leaders move past buzzwords and build a clear, realistic plan for applying AI across the SOC.

The structure: From experimentation to autonomy

The model outlines five levels of maturity :

L0 – Manual Operations: Processes are mostly manual with limited automation of some tasks.

L1 – Automation Rules: Manually maintained or externally-sourced automation rules and logic are used wherever possible.

L2 – AI Assistance: AI assists research but is not trusted to make good decisions. This includes GenAI agents requiring manual oversight for errors.

L3 – AI Collaboration: Specialized cybersecurity AI agent systems  with business technology context are trusted with specific tasks and decisions. GenAI has limited uses where errors are acceptable.

L4 – AI Delegation: Specialized AI agent systems with far wider business operations and impact context perform most cybersecurity tasks and decisions independently, with only high-level oversight needed.

Each level reflects a shift, not only in technology, but in people and processes. As AI matures, analysts evolve from executors to strategic overseers.

Strategic benefits for security leaders

The maturity model isn’t just about technology adoption it’s about aligning AI investments with measurable operational outcomes. Here’s what it enables:

SOC fatigue is real, and AI can help

Most teams still struggle with alert volume, investigation delays, and reactive processes. AI adoption is inconsistent and often siloed. When integrated well, AI can make a meaningful difference in making security teams more effective

GenAI is error prone, requiring strong human oversight

While there is a lot of hype around GenAI agentic systems, teams will need to account for inaccuracy and hallucination in Agentic GenAI systems.

AI’s real value lies in progression

The biggest gains don’t come from isolated use cases, but from integrating AI across the lifecycle, from preparation through detection to containment and recovery.

Trust and oversight are key initially but evolves in later levels

Early-stage adoption keeps humans fully in control. By L3 and L4, AI systems act independently within defined bounds, freeing humans for strategic oversight.

People’s roles shift meaningfully

As AI matures, analyst roles consolidate and elevate from labor intensive task execution to high-value decision-making, focusing on critical, high business impact activities, improving processes and AI governance.

Outcome, not hype, defines maturity

AI maturity isn’t about tech presence, it’s about measurable impact on risk reduction, response time, and operational resilience.

[related-resource]

Outcomes across the AI Security Maturity Model

The Security Organization experiences an evolution of cybersecurity outcomes as teams progress from manual operations to AI delegation. Each level represents a step-change in efficiency, accuracy, and strategic value.

L0 – Manual Operations

At this stage, analysts manually handle triage, investigation, patching, and reporting manually using basic, non-automated tools. The result is reactive, labor-intensive operations where most alerts go uninvestigated and risk management remains inconsistent.

L1 – Automation Rules

At this stage, analysts manage rule-based automation tools like SOAR and XDR, which offer some efficiency gains but still require constant tuning. Operations remain constrained by human bandwidth and predefined workflows.

L2 – AI Assistance

At this stage, AI assists with research, summarization, and triage, reducing analyst workload but requiring close oversight due to potential errors. Detection improves, but trust in autonomous decision-making remains limited.

L3 – AI Collaboration

At this stage, AI performs full investigations and recommends actions, while analysts focus on high-risk decisions and refining detection strategies. Purpose-built agentic AI systems with business context are trusted with specific tasks, improving precision and prioritization.

L4 – AI Delegation

At this stage, Specialized AI Agent Systems performs most security tasks independently at machine speed, while human teams provide high-level strategic oversight. This means the highest time and effort commitment activities by the human security team is focused on proactive activities while AI handles routine cybersecurity tasks

Specialized AI Agent Systems operate with deep business context including impact context to drive fast, effective decisions.

Join the webinar

Get a look at the minds shaping this model by joining our upcoming webinar using this link. We’ll walk through real use cases, share lessons learned from the field, and show how security teams are navigating the path to operational AI safely, strategically, and successfully.

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About the author
Ashanka Iddya
Senior Director, Product Marketing

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July 17, 2025

Forensics or Fauxrensics: Five Core Capabilities for Cloud Forensics and Incident Response

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The speed and scale at which new cloud resources can be spun up has resulted in uncontrolled deployments, misconfigurations, and security risks. It has had security teams racing to secure their business’ rapid migration from traditional on-premises environments to the cloud.

While many organizations have successfully extended their prevention and detection capabilities to the cloud, they are now experiencing another major gap: forensics and incident response.

Once something bad has been identified, understanding its true scope and impact is nearly impossible at times. The proliferation of cloud resources across a multitude of cloud providers, and the addition of container and serverless capabilities all add to the complexities. It’s clear that organizations need a better way to manage cloud incident response.

Security teams are looking to move past their homegrown solutions and open-source tools to incorporate real cloud forensics capabilities. However, with the increased buzz around cloud forensics, it can be challenging to decipher what is real cloud forensics, and what is “fauxrensics.”

This blog covers the five core capabilities that security teams should consider when evaluating a cloud forensics and incident response solution.

[related-resource]

1. Depth of data

There have been many conversations among the security community about whether cloud forensics is just log analysis. The reality, however, is that cloud forensics necessitates access to a robust dataset that extends far beyond traditional log data sources.

While logs provide valuable insights, a forensics investigation demands a deeper understanding derived from multiple data sources, including disk, network, and memory, within the cloud infrastructure. Full disk analysis complements log analysis, offering crucial context for identifying the root cause and scope of an incident.

For instance, when investigating an incident involving a Kubernetes cluster running on an EC2 instance, access to bash history can provide insights into the commands executed by attackers on the affected instance, which would not be available through cloud logs alone.

Having all of the evidence in one place is also a capability that can significantly streamline investigations, unifying your evidence be it disk images, memory captures or cloud logs, into a single timeline allowing security teams to reconstruct an attacks origin, path and impact far more easily. Multi–cloud environments also require platforms that can support aggregating data from many providers and services into one place. Doing this enables more holistic investigations and reduces security blind spots.

There is also the importance of collecting data from ephemeral resources in modern cloud and containerized environments. Critical evidence can be lost in seconds as resources are constantly spinning up and down, so having the ability to capture this data before its gone can be a huge advantage to security teams, rather than having to figure out what happened after the affected service is long gone.

darktrace / cloud, cado, cloud logs, ost, and memory information. value of cloud combined analysis

2. Chain of custody

Chain of custody is extremely critical in the context of legal proceedings and is an essential component of forensics and incident response. However, chain of custody in the cloud can be extremely complex with the number of people who have access and the rise of multi-cloud environments.

In the cloud, maintaining a reliable chain of custody becomes even more complex than it already is, due to having to account for multiple access points, service providers and third parties. Having automated evidence tracking is a must. It means that all actions are logged, from collection to storage to access. Automation also minimizes the chance of human error, reducing the risk of mistakes or gaps in evidence handling, especially in high pressure fast moving investigations.

The ability to preserve unaltered copies of forensic evidence in a secure manner is required to ensure integrity throughout an investigation. It is not just a technical concern, its a legal one, ensuring that your evidence handling is documented and time stamped allows it to stand up to court or regulatory review.

Real cloud forensics platforms should autonomously handle chain of custody in the background, recording and safeguarding evidence without human intervention.

3. Automated collection and isolation

When malicious activity is detected, the speed at which security teams can determine root cause and scope is essential to reducing Mean Time to Response (MTTR).

Automated forensic data collection and system isolation ensures that evidence is collected and compromised resources are isolated at the first sign of malicious activity. This can often be before an attacker has had the change to move latterly or cover their tracks. This enables security teams to prevent potential damage and spread while a deeper-dive forensics investigation takes place. This method also ensures critical incident evidence residing in ephemeral environments is preserved in the event it is needed for an investigation. This evidence may only exist for minutes, leaving no time for a human analyst to capture it.

Cloud forensics and incident response platforms should offer the ability to natively integrate with incident detection and alerting systems and/or built-in product automation rules to trigger evidence capture and resource isolation.

4. Ease of use

Security teams shouldn’t require deep cloud or incident response knowledge to perform forensic investigations of cloud resources. They already have enough on their plates.

While traditional forensics tools and approaches have made investigation and response extremely tedious and complex, modern forensics platforms prioritize usability at their core, and leverage automation to drastically simplify the end-to-end incident response process, even when an incident spans multiple Cloud Service Providers (CSPs).

Useability is a core requirement for any modern forensics platform. Security teams should not need to have indepth knowledge of every system and resource in a given estate. Workflows, automation and guidance should make it possible for an analyst to investigate whatever resource they need to.

Unifying the workflow across multiple clouds can also save security teams a huge amount of time and resources. Investigations can often span multiple CSP’s. A good security platform should provide a single place to search, correlate and analyze evidence across all environments.

Offering features such as cross cloud support, data enrichment, a single timeline view, saved search, and faceted search can help advanced analysts achieve greater efficiency, and novice analysts are able to participate in more complex investigations.

5. Incident preparedness

Incident response shouldn't just be reactive. Modern security teams need to regularly test their ability to acquire new evidence, triage assets and respond to threats across both new and existing resources, ensuring readiness even in the rapidly changing environments of the cloud.  Having the ability to continuously assess your incident response and forensics workflows enables you to rapidly improve your processes and identify and mitigate any gaps identified that could prevent the organization from being able to effectively respond to potential threats.

Real forensics platforms deliver features that enable security teams to prepare extensively and understand their shortcomings before they are in the heat of an incident. For example, cloud forensics platforms can provide the ability to:

  • Run readiness checks and see readiness trends over time
  • Identify and mitigate issues that could prevent rapid investigation and response
  • Ensure the correct logging, management agents, and other cloud-native tools are appropriately configured and operational
  • Ensure that data gathered during an investigation can be decrypted
  • Verify that permissions are aligned with best practices and are capable of supporting incident response efforts

Cloud forensics with Darktrace

Darktrace delivers a proactive approach to cyber resilience in a single cybersecurity platform, including cloud coverage. Darktrace / CLOUD is a real time Cloud Detection and Response (CDR) solution built with advanced AI to make cloud security accessible to all security teams and SOCs. By using multiple machine learning techniques, Darktrace brings unprecedented visibility, threat detection, investigation, and incident response to hybrid and multi-cloud environments.

Darktrace’s cloud offerings have been bolstered with the acquisition of Cado Security Ltd., which enables security teams to gain immediate access to forensic-level data in multi-cloud, container, serverless, SaaS, and on-premises environments.

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About the author
Calum Hall
Technical Content Researcher
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