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July 9, 2019

Insights on Shamoon 3 Data-Wiping Malware

Gain insights into Shamoon 3 and learn how to protect your organization from its destructive capabilities.
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
Jul 2019

Responsible for some of the “most damaging cyber-attacks in history” since 2012, the Shamoon malware wipes compromised hard drives and overwrites key system processes, intending to render infected machines unusable. During a trial period in the network of a global company, Darktrace observed a Shamoon-powered cyber-attack on December 10, 2018 — when several Middle Eastern firms were impacted by a new variant of the malware.

While there has been detailed reporting on the malware files and wiper modules that these latest Shamoon attacks employed, the complete cyber kill chain involved remains poorly understood, while the intrusions that led to the malware’s eventual “detonation” last December has not received nearly as much coverage. As a consequence, this blog post will focus on the insights that Darktrace’s cyber AI generated regarding (a) the activity of the infected devices during the “detonation” and (b) the indicators of compromise that most likely represent lateral movement activity during the weeks prior.

A high-level overview of major events leading up to the detonation on December 10th.

In the following, we will dive into that timeline more deeply in reverse chronological order, going back in time to trace the origins of the attack. Let’s begin with zero hour.

December 10: 42 devices “detonate”

A bird's-eye perspective of how Darktrace identified the alerts in December 2018.

What immediately strikes the analyst’s eye is the fact that a large accumulation of alerts, indicated by the red rectangle above, took place on December 10, followed by complete network silence over the subsequent four days.

These highlighted alerts represent Darktrace’s detection of unusual network scans on remote port 445 that were conducted by 42 infected devices. These devices proceeded to scan more machines — none of which were among those already infected. Such behavior indicates that the compromised devices started scanning and were wiped independently from each other, instead of conducting worming-style activity during the detonation of the malware. The initial scanning device started its scan at 12:56 p.m. UTC, while the last scanning device started its scan at 2:07 p.m. UTC.

Not only was this activity readily apparent from the bird’s-eye perspective shown above, the detonating devices also created the highest-priority Darktrace alerts over a several day period: “Device / Network Scan” and “Device / Expanded Network Scan”:

Moreover, when investigating “Devices — Overall Score,” the detonating devices rank as the most critical assets for the time period December 8–11:

Darktrace AI generated all of the above alerts because they represented significant anomalies from the normal ‘pattern of life’ that the AI had learned for each user and device on the company’s network. Crucially, none of the alerts were the product of predefined ‘rules and signatures’ — the mechanism that conventional security tools rely on to detect cyber-threats. Rather, the AI revealed the activity because the scans were unusual for the devices given their precise nature and timing, demonstrating the necessity of the such a nuanced approach in catching elusive threats like Shamoon. Of further importance is that the company’s network consists of around 15,000 devices, meaning that a rules-based approach without the ability to prioritize the most serious threats would have drowned out the Shamoon alerts in noise.

Now that we’ve seen how cyber AI sounded the alarms during the detonation itself, let’s investigate the various indicators of suspicious lateral movement that precipitated the events of December 10. Most of this activity happened in brief bursts, each of which could have been spotted and remediated if Darktrace had been closely monitored.

November 19: Unusual Remote Powershell Usage (WinRM)

One such burst of unusual activity occurred on November 19, when Darktrace detected 14 devices — desktops and servers alike — that all successfully used the WinRM protocol. None of these devices had previously used WinRM, which is also unusual for the organization’s environment as a whole. Conversely, Remote PowerShell is quite often abused in intrusions during lateral movement. The devices involved did not classify as traditional administrative devices, making their use of WinRM even more suspicious.

Note the clustering of the WinRM activity as indicated by the timestamp on the left.

October 29–31: Scanning, Unusual PsExec & RDP Brute Forcing

Another burst of likely lateral movement occurred between October 29 and 31, when two servers were seen using PsExec in an unusual fashion. No PsExec activity had been observed in the network before or after these detections, prompting Darktrace to flag the behavior. One of the servers conducted an ICMP Ping sweep shortly before the lateral movement. Not only did both servers start using PsExec on the same day, they also used SMBv1 — which, again, was very unusual for the network.

Most legitimate administrative activity involving PsExec these days uses SMBv2. The graphic below shows several Darktrace alerts on one of the involved servers — take note of the chronology of detections at the bottom of the graphic. This clearly reads like an attacker’s diary: ICMP scan, SMBv1 usage, and unusual PsExec usage, followed by new remote service controls. This server was among the top five highest ranking devices during the analyzed time period and was easy to identify.

Following the PsExec use, the servers also started an anomalous amount of remote services via the srvsvc and svcctl pipes over SMB. They did so by starting services on remote devices with which they usually did not communicate — using SMBv1, of course. Some of the attempted communication failed due to access violation and access permission errors. Both are often seen during malicious lateral movement.

Additional context around the SMBv1 and remote srvsvc pipe activity. Note the access failure.

Thanks to Darktrace’s deep packet inspection, we can see exactly what happened on the application layer. Darktrace highlights any unusual or new activity in italics below the connections — we can easily see that the SMB activity is not only unusual because of SMBv1 being used, but also because this server had never used this type of SMB activity remotely to those particular destinations before. We can also observe remote access to the winreg pipe — likely indicating more lateral movement and persistence mechanisms being established.

The other server conducted some targeted address scanning on the network on October 29, employing typical lateral movement ports 135, 139 and 445:

Another device was observed to conduct RDP brute forcing on October 29 around the same time as the above address scan. The desktop made an unusual amount of RDP connections to another internal server.

A clear plateau in increased internal connections (blue) can be seen. Every colored dot on top represents an RDP brute force detection. This was again a clear-cut detection not drowned in other noise — these were the only RDP brute force detections for a several-month monitoring time window.

October 9–11: Unusual Credential Usage

Darktrace identifies the unusual use of credentials — for instance, if administrative credentials are used on client device on which they are not commonly used. This might indicate lateral movement where service accounts or local admin accounts have been compromised.

Darktrace identified another cluster of activity that is likely representing lateral movement, this time involving unusual credential usage. Between October 9 and 11, Darktrace identified 17 cases of new administrative credentials being used on client devices. While new administrative credentials were being used from time to time on devices as part of normal administrative activity, this strong clustering of unusual admin credential usage was outstanding. Additionally, Darktrace also identified the source of some of the credentials being used as unusual.

Conclusion

Having observed a live Shamoon infection within Darktrace, there are a few key takeaways. While the actual detonation on December 10 was automated, the intrusion that built up to it was most likely manual. The fact that all detonating devices started their malicious activity roughly at the same time — without scanning each other — indicates that the payload went off based on a trigger like a scheduled task. This is in line with other reporting on Shamoon 3.

In the weeks leading up to December 10, there were various significant signs of lateral movement that occurred in disparate bursts — indicating a ‘low-and-slow’ manual intrusion.

The adversaries used classic lateral movement techniques like RDP brute forcing, PsExec, WinRM usage, and the abuse of stolen administrative credentials.

While the organization in question had a robust security posture, an attacker only needs to exploit one vulnerability to bring down an entire system. During the lifecycle of the attack, the Darktrace Enterprise Immune System identified the threatening activity in real time and provided numerous suggested actions that could have prevented the Shamoon attack at various stages. However, human action was not taken, while the organization had yet to activate Antigena, Darktrace’s autonomous response solution, which could have acted in the security team’s stead.

Despite having limited scope during the trial period, the Enterprise Immune System was able to detect the lateral movement and detonation of the payload, which was indicative of the malicious Shamoon virus activity. A junior analyst could have easily identified the activity, as high-severity alerts were consistently generated, and the likely infected devices were at the top of the suspicious devices list.

Darktrace Antigena would have prevented the movement responsible for the spread of the virus, while also sending high-severity alerts to the security team to investigate the activity. Even the scanning on port 445 from the detonating devices would have been shut down, as it presented a significant deviation from the known behavior of all scanning devices, which would have further limited the virus’s spread, and ultimately, spared the company and its devices from attack.


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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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

cloud security investigation guy on computer doing workDefault blog imageDefault blog image

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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