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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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August 8, 2025

Ivanti Under Siege: Investigating the Ivanti Endpoint Manager Mobile Vulnerabilities (CVE-2025-4427 & CVE-2025-4428)

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Ivanti & Edge infrastructure exploitation

Edge infrastructure exploitations continue to prevail in today’s cyber threat landscape; therefore, it was no surprise that recent Ivanti Endpoint Manager Mobile (EPMM) vulnerabilities CVE-2025-4427 and CVE-2025-4428 were exploited targeting organizations in critical sectors such as healthcare, telecommunications, and finance across the globe, including across the Darktrace customer base in May 2025.

Exploiting these types of vulnerabilities remains a popular choice for threat actors seeking to enter an organization’s network to perform malicious activity such as cyber espionage, data exfiltration and ransomware detonation.

Vulnerabilities in Ivanti EPMM

Ivanti EPMM allows organizations to manage and configure enterprise mobile devices. On May 13, 2025, Ivanti published a security advisory [1] for their Ivanti Endpoint Manager Mobile (EPMM) devices addressing a medium and high severity vulnerability:

  • CVE-2025-4427, CVSS: 5.6: An authentication bypass vulnerability
  • CVE-2025-4428, CVSS: 7.2: Remote code execution vulnerability

Successfully exploiting both vulnerabilities at the same time could lead to unauthenticated remote code execution from an unauthenticated threat actor, which could allow them to control, manipulate, and compromise managed devices on a network [2].

Shortly after the disclosure of these vulnerabilities, external researchers uncovered evidence that they were being actively exploited in the wild and identified multiple indicators of compromise (IoCs) related to post-exploitation activities for these vulnerabilities [2] [3]. Research drew particular attention to the infrastructure utilized in ongoing exploitation activity, such as leveraging the two vulnerabilities to eventually deliver malware contained within ELF files from Amazon Web Services (AWS) S3 bucket endpoints and to deliver KrustyLoader malware for persistence. KrustyLoader is a Rust based malware that was discovered being downloaded in compromised Ivanti Connect Secure systems back in January 2024 when the zero-day critical vulnerabilities; CVE-2024-21887 and CVE-2023-46805 [10].

This suggests the involvement of the threat actor UNC5221, a suspected China-nexus espionage actor [3].

In addition to exploring the post-exploit tactics, techniques, and procedures (TTPs) observed for these vulnerabilities across Darktrace’s customer base, this blog will also examine the subtle changes and similarities in the exploitation of earlier Ivanti vulnerabilities—specifically Ivanti Connect Secure (CS) and Policy Secure (PS) vulnerabilities CVE-2023-46805 and CVE-2024-21887 in early 2024, as well as CVE-2025-0282 and CVE-2025-0283, which affected CS, PS, and Zero Trust Access (ZTA) in January 2025.

Darktrace Coverage

In May 2025, shortly after Ivanti disclosed vulnerabilities in their EPMM product, Darktrace’s Threat Research team identified attack patterns potentially linked to the exploitation of these vulnerabilities across multiple customer environments. The most noteworthy attack chain activity observed included exploit validation, payload delivery via AWS S3 bucket endpoints, subsequent delivery of script-based payloads, and connections to dpaste[.]com, possibly for dynamic payload retrieval. In a limited number of cases, connections were also made to an IP address associated with infrastructure linked to SAP NetWeaver vulnerability CVE-2025-31324, which has been investigated by Darktrace in an earlier case.

Exploit Validation

Darktrace observed devices within multiple customer environments making connections related to Out-of-Band Application Security Testing (OAST). These included a range of DNS requests and connections, most of which featured a user agent associated with the command-line tool cURL, directed toward associated endpoints. The hostnames of these endpoints consisted of a string of randomly generated characters followed by an OAST domain, such as 'oast[.]live', 'oast[.]pro', 'oast[.]fun', 'oast[.]site', 'oast[.]online', or 'oast[.]me'. OAST endpoints can be leveraged by malicious actors to trigger callbacks from targeted systems, such as for exploit validation. This activity, likely representing the initial phase of the attack chain observed across multiple environments, was also seen in the early stages of previous investigations into the exploitation of Ivanti vulnerabilities [4]. Darktrace also observed similar exploit validation activity during investigations conducted in January 2024 into the Ivanti CS vulnerabilities CVE-2023-46805 and CVE-2024-21887.

Payload Delivery via AWS

Devices across multiple customer environments were subsequently observed downloading malicious ELF files—often with randomly generated filenames such as 'NVGAoZDmEe'—from AWS S3 bucket endpoints like 's3[.]amazonaws[.]com'. These downloads occurred over HTTP connections, typically using wget or cURL user agents. Some of the ELF files were later identified to be KrustyLoader payloads using open-source intelligence (OSINT). External researchers have reported that the KrustyLoader malware is executed in cases of Ivanti EPMM exploitation to gain and maintain a foothold in target networks [2].

In one customer environment, after connections were made to the endpoint fconnect[.]s3[.]amazonaws[.]com, Darktrace observed the target system downloading the ELF file mnQDqysNrlg via the user agent Wget/1.14 (linux-gnu). Further investigation of the file’s SHA1 hash (1dec9191606f8fc86e4ae4fdf07f09822f8a94f2) linked it to the KrustyLoader malware [5]. In another customer environment, connections were instead made to tnegadge[.]s3[.]amazonaws[.]com using the same user agent, from which the ELF file “/dfuJ8t1uhG” was downloaded. This file was also linked to KrustyLoader through its SHA1 hash (c47abdb1651f9f6d96d34313872e68fb132f39f5) [6].

The pattern of activity observed so far closely mirrors previous exploits associated with the Ivanti vulnerabilities CVE-2023-46805 and CVE-2024-21887 [4]. As in those cases, Darktrace observed exploit validation using OAST domains and services, along with the use of AWS endpoints to deliver ELF file payloads. However, in this instance, the delivered payload was identified as KrustyLoader malware.

Later-stage script file payload delivery

In addition to the ELF file downloads, Darktrace also detected other file downloads across several customer environments, potentially representing the delivery of later-stage payloads.

The downloaded files included script files with the .sh extension, featuring randomly generated alphanumeric filenames. One such example is “4l4md4r.sh”, which was retrieved during a connection to the IP address 15.188.246[.]198 using a cURL-associated user agent. This IP address was also linked to infrastructure associated with the SAP NetWeaver remote code execution vulnerability CVE-2025-31324, which enables remote code execution on NetWeaver Visual Composer. External reporting has attributed this infrastructure to a China-nexus state actor [7][8][9].

In addition to the script file downloads, devices on some customer networks were also observed making connections to pastebin[.]com and dpaste[.]com, two sites commonly used to host or share malicious payloads or exploitation instructions [2]. Exploits, including those targeting Ivanti EPMM vulnerabilities, can dynamically fetch malicious commands from sites like dpaste[.]com, enabling threat actors to update payloads. Unlike the previously detailed activity, this behavior was not identified in any prior Darktrace investigations into Ivanti-related vulnerabilities, suggesting a potential shift in the tactics used in post-exploitation stages of Ivanti attacks.

Conclusion

Edge infrastructure vulnerabilities, such as those found in Ivanti EPMM and investigated across customer environments with Darktrace / NETWORK, have become a key tool in the arsenal of attackers in today’s threat landscape. As highlighted in this investigation, while many of the tactics employed by threat actors following successful exploitation of vulnerabilities remain the same, subtle shifts in their methods can also be seen.

These subtle and often overlooked changes enable threat actors to remain undetected within networks, highlighting the critical need for organizations to maintain continuous extended visibility, leverage anomaly based behavioral analysis, and deploy machine speed intervention across their environments.

Credit to Nahisha Nobregas (Senior Cyber Analyst) and Anna Gilbertson (Senior Cyber Analyst)

Appendices

Mid-High Confidence IoCs

(IoC – Type - Description)

-       trkbucket.s3.amazonaws[.]com – Hostname – C2 endpoint

-       trkbucket.s3.amazonaws[.]com/NVGAoZDmEe – URL – Payload

-       tnegadge.s3.amazonaws[.]com – Hostname – C2 endpoint

-       tnegadge.s3.amazonaws[.]com/dfuJ8t1uhG – URL – Payload

-       c47abdb1651f9f6d96d34313872e68fb132f39f5 - SHA1 File Hash – Payload

-       4abfaeadcd5ab5f2c3acfac6454d1176 - MD5 File Hash - Payload

-       fconnect.s3.amazonaws[.]com – Hostname – C2 endpoint

-       fconnect.s3.amazonaws[.]com/mnQDqysNrlg – URL - Payload

-       15.188.246[.]198 – IP address – C2 endpoint

-       15.188.246[.]198/4l4md4r.sh?grep – URL – Payload

-       185.193.125[.]65 – IP address – C2 endpoint

-       185.193.125[.]65/c4qDsztEW6/TIGHT_UNIVERSITY – URL – C2 endpoint

-       d8d6fe1a268374088fb6a5dc7e5cbb54 – MD5 File Hash – Payload

-       64.52.80[.]21 – IP address – C2 endpoint

-       0d8da2d1.digimg[.]store – Hostname – C2 endpoint

-       134.209.107[.]209 – IP address – C2 endpoint

Darktrace Model Detections

-       Compromise / High Priority Tunnelling to Bin Services (Enhanced Monitoring Model)

-       Compromise / Possible Tunnelling to Bin Services

-       Anomalous Server Activity / New User Agent from Internet Facing System

-       Compliance / Pastebin

-       Device / Internet Facing Device with High Priority Alert

-       Anomalous Connection / Callback on Web Facing Device

-       Anomalous File / Script from Rare External Location

-       Anomalous File / Incoming ELF File

-       Device / Suspicious Domain

-       Device / New User Agent

-       Anomalous Connection / Multiple Connections to New External TCP Port

-       Anomalous Connection / New User Agent to IP Without Hostname

-       Anomalous File / EXE from Rare External Location

-       Anomalous File / Internet Facing System File Download

-       Anomalous File / Multiple EXE from Rare External Locations

-       Compromise / Suspicious HTTP and Anomalous Activity

-       Device / Attack and Recon Tools

-       Device / Initial Attack Chain Activity

-       Device / Large Number of Model Alerts

-       Device / Large Number of Model Alerts from Critical Network Device

References

1.     https://forums.ivanti.com/s/article/Security-Advisory-Ivanti-Endpoint-Manager-Mobile-EPMM?language=en_US

2.     https://blog.eclecticiq.com/china-nexus-threat-actor-actively-exploiting-ivanti-endpoint-manager-mobile-cve-2025-4428-vulnerability

3.     https://www.wiz.io/blog/ivanti-epmm-rce-vulnerability-chain-cve-2025-4427-cve-2025-4428

4.     https://www.darktrace.com/blog/the-unknown-unknowns-post-exploitation-activities-of-ivanti-cs-ps-appliances

5.     https://www.virustotal.com/gui/file/ac91c2c777c9e8638ec1628a199e396907fbb7dcf9c430ca712ec64a6f1fcbc9/community

6.     https://www.virustotal.com/gui/file/f3e0147d359f217e2aa0a3060d166f12e68314da84a4ecb5cb205bd711c71998/community

7.     https://www.virustotal.com/gui/ip-address/15.188.246.198

8.     https://blog.eclecticiq.com/china-nexus-nation-state-actors-exploit-sap-netweaver-cve-2025-31324-to-target-critical-infrastructures

9.     https://www.darktrace.com/blog/tracking-cve-2025-31324-darktraces-detection-of-sap-netweaver-exploitation-before-and-after-disclosure

10.  https://www.synacktiv.com/en/publications/krustyloader-rust-malware-linked-to-ivanti-connectsecure-compromises

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein.

Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

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About the author
Nahisha Nobregas
SOC Analyst

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August 7, 2025

How CDR & Automated Forensics Transform Cloud Incident Response

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Introduction: Cloud investigations

In cloud security, speed, automation and clarity are everything. However, for many SOC teams, responding to incidents in the cloud is often very difficult especially when attackers move fast, infrastructure is ephemeral, and forensic skills are scarce.

In this blog we will walk through an example that shows exactly how Darktrace Cloud Detection and Response (CDR) and automated cloud forensics together, solve these challenges, automating cloud detection, and deep forensic investigation in a way that’s fast, scalable, and deeply insightful.

The Problem: Cloud incidents are hard to investigate

Security teams often face three major hurdles when investigating cloud detections:

Lack of forensic expertise: Most SOCs and security teams aren’t natively staffed with forensics specialists.

Ephemeral infrastructure: Cloud assets spin up and down quickly, leaving little time to capture evidence.

Lack of existing automation: Gathering forensic-level data often requires manual effort and leaves teams scrambling around during incidents — accessing logs, snapshots, and system states before they disappear. This process is slow and often blocked by permissions, tooling gaps, or lack of visibility.

How Darktrace augments cloud investigations

1. Darktrace’s CDR finds anomalous activity in the cloud

An alert is generated for a large outbound data transfer from an externally facing EC2 instance to a rare external endpoint. It’s anomalous, unexpected, and potentially serious.

2. AI-led investigation stitches together the incident for a SOC analyst to look into

When a security incident unfolds, Darktrace’s Cyber AI Analyst TM is the first to surface it, automatically correlating behaviors, surfacing anomalies, and presenting a cohesive incident summary. It’s fast, detailed, and invaluable.

Once the incident is created, more questions are raised.

  • How were the impacted resources compromised?
  • How did the attack unfold over time – what tools and malware were used?
  • What data was accessed and exfiltrated?

What you’ll see as a SOC analyst: The incident begins in Darktrace’s Threat Visualizer, where a Cyber AI Analyst incident has been generated automatically highlighting large anomalous data transfer to a suspicious external IP. This isn’t just another alert, it’s a high-fidelity signal backed by Darktrace’s Self-Learning AI.

Cyber AI Analyst incident created for anomalous outbound data transfer
Figure 1: Cyber AI Analyst incident created for anomalous outbound data transfer

The analyst can then immediately pivot to Darktrace / CLOUD’s architecture view (see below), gaining context on the asset’s environment, ingress/egress points, connected systems, potential attack paths and whether there are any current misconfigurations detected on the asset.

Darktrace / CLOUD architecture view providing critical cloud context
Figure 2: Darktrace / CLOUD architecture view providing critical cloud context

3. Automated forensic capture — No expertise required

Then comes the game-changer, Darktrace’s recent acquisition of Cado enhances its cloud forensics capabilities. From the first alert triggered, Darktrace has already kicked in and automatically processed and analyzed a full volume capture of the EC2. Everything, past and present, is preserved. No need for manual snapshots, CLI commands, or specialist intervention.

Darktrace then provides a clear timeline highlighting the evidence and preserving it. In our example we identify:

  • A brute-force attempt on a file management app, followed by a successful login
  • A reverse shell used to gain unauthorized remote access to the EC2
  • A reverse TCP connection to the same suspicious IP flagged by Darktrace
  • Attacker commands showing how the data was split and prepared for exfiltration
  • A file (a.tar) created from two sensitive archives: product_plans.zip and research_data.zip

All of this is surfaced through the timeline view, ranked by significance using machine learning. The analyst can pivot through time, correlate events, and build a complete picture of the attack — without needing cloud forensics expertise.

Darktrace even gives the ability to:

  • Download and inspect gathered files in full detail, enabling teams to verify exactly what data was accessed or exfiltrated.
  • Interact with the file system as if it were live, allowing investigators to explore directories, uncover hidden artifacts, and understand attacker movement with precision.
Figure 3 Cado critical forensic investigation automated insights
Figure 3: Cado critical forensic investigation automated insights
Figure 4: Cado forensic file analysis of reverse shell and download option
Figure 5: a.tar created from two sensitive archives: product_plans.zip and research_data.zip
Figure 6: Traverse the full file system of the asset

Why this matters?

This workflow solves the hardest parts of cloud investigation:

  1. Capturing evidence before it disappears
  2. Understanding attacker behavior in detail - automatically
  3. Linking detections to impact with full incident visibility

This kind of insight is invaluable for organizations especially regulated industries, where knowing exactly what data was affected is critical for compliance and reporting. It’s also a powerful tool for detecting insider threats, not just external attackers.

Darktrace / CLOUD and Cado together acts as a force multiplier helping with:

  • Reducing investigation time from hours to minutes
  • Preserving ephemeral evidence automatically
  • Empowering analysts with forensic-level visibility

Cloud threats aren’t slowing down. Your response shouldn’t either. Darktrace / CLOUD + Cado gives your SOC the tools to detect, contain, and investigate cloud incidents — automatically, accurately, and at scale.

[related-resource]

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About the author
Adam Stevens
Director of Product, Cloud Security
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