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September 23, 2025

ShadowV2: An emerging DDoS for hire botnet

Darktrace exposed a cybercrime-as-a-service campaign using Python and Go-based malware, Docker containerization, and a full operator UI. With DDoS-as-a-service features, modular APIs, and advanced evasion, this platform highlights the need for defenders to monitor cloud workloads, container orchestration, and API activity to counter evolving threats.
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
Nate Bill
Threat Researcher
ShadowV2: An emerging DDoS for hire botnet Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
23
Sep 2025

Introduction: ShadowV2 DDoS

Darktrace's latest investigation uncovered a novel campaign that blends traditional malware with modern devops technology.

At the center of this campaign is a Python-based command-and-control (C2) framework hosted on GitHub CodeSpaces. This campaign also utilizes a Python based spreader with a multi-stage Docker deployment as the initial access vector.

The campaign further makes use of a Go-based Remote Access Trojan (RAT) that implements a RESTful registration and polling mechanism, enabling command execution and communication with its operators.

ShadowV2 attack techniques

What sets this campaign apart is the sophistication of its attack toolkit.

The threat actors employ advanced methods such as HTTP/2 rapid reset, a Cloudflare under attack mode (UAM) bypass, and large-scale HTTP floods, demonstrating a capability to combine distributed denial-of-service (DDoS) techniques with targeted exploitation.

With the inclusion of an OpenAPI specification, implemented with FastAPI and Pydantic and a fully developed login panel and operator interface, the infrastructure seems to resemble a “DDoS-as-a-service” platform rather than a traditional botnet, showing the extent to which modern malware increasingly mirrors legitimate cloud-native applications in both design and usability.

Analysis of a SadowV2 attack

Initial access

The initial compromise originates from a Python script hosted on GitHub CodeSpaces. This can be inferred from the observed headers:

User-Agent: docker-sdk-python/7.1.0

X-Meta-Source-Client: github/codespaces

The user agent shows that the attacker is using the Python Docker SDK, a library for Python programs that allows them to interact with Docker to create containers. The X-Meta-Source-Client appears to have been injected by GitHub into the request to allow for attribution, although there is no documentation online about this header.

The IP the connections originate from is 23.97.62[.]139, which is a Microsoft IP based in Singapore. This aligns with expectations as GitHub is owned by Microsoft.

This campaign targets exposed Docker daemons, specifically those running on AWS EC2. Darktrace runs a number of honeypots across multiple cloud providers and has only observed attacks against honeypots running on AWS EC2. By default, Docker is not accessible to the Internet, however, can be configured to allow external access. This can be useful for managing complex deployments where remote access to the Docker API is needed.

Typically, most campaigns targeting Docker will either take an existing image from Docker Hub and deploy their tools within it, or upload their own pre-prepared image to deploy. This campaign works slightly differently; it first spawns a generic “setup” container and installs a number of tools within it. This container is then imaged and deployed as a live container with the malware arguments passed in via environmental variables.

Attacker creates a blank container from an Ubuntu image.
Figure 1: Attacker creates a blank container from an Ubuntu image.
Attacker sets up their tools for the attack.
Figure 2: Attacker sets up their tools for the attack.
 Attacker deploys a new container using the image from the setup container.
Figure 3: Attacker deploys a new container using the image from the setup container.

It is unclear why the attackers chose this approach - one possibility is that the actor is attempting to avoid inadvertently leaving forensic artifacts by performing the build on the victim machine, rather than building it themselves and uploading it.

Malware analysis

The Docker container acts as a wrapper around a single binary, dropped in /app/deployment. This is an ELF binary written in Go, a popular choice for modern malware. Helpfully, the binary is unstripped, making analysis significantly easier.

The current version of the malware has not been reported by OSINT providers such as VirusTotal. Using the domain name from the MASTER_ADDR variable and other IoCs, we were able to locate two older versions of the malware that were submitted to VirusTotal on the June 25 and July 30 respectively [1] [2].  Neither of these had any detections and were only submitted once each using the web portal from the US and Canada respectively. Darktrace first observed the attack against its honeypot on June 24, so it could be a victim of this campaign submitting the malware to VirusTotal. Due to the proximity of the start of the attacks, it could also be the attacker testing for detections, however it is not possible to know for certain.

The malware begins by phoning home, using the MASTER_ADDR and VPS_NAME identifiers passed in from the Docker run environmental variables. In addition, the malware derives a unique VPS_ID, which is the VPS_NAME concatenated with the current unix timestamp. The VPS_ID is used for all communications with the C2 server as the identifier for the specific implant. If the malware is restarted, or the victim is re-infected, the C2 server will inform the implant of its original VPS_ID to ensure continuity.

Snippet that performs the registration by sending a POST request to the C2 API with a JSON structure.
Figure 4: Snippet that performs the registration by sending a POST request to the C2 API with a JSON structure.

From there, the malware then spawns two main loops that will remain active for the lifetime of the implant. Every second, it sends a heartbeat to the C2 by sending the VPS_ID to hxxps://shadow.aurozacloud[.]xyz/api/vps/heartbeat via POST request. Every 5 seconds, it retrieves hxxps://shadow.aurozacloud[.]xyz/api/vps/poll/<VPS ID> via a GET request to poll for new commands.

The poll mechanism shadow v2
Figure 5: The poll mechanism.

At this stage, Darktrace security researchers wrote a custom client that ran on the server infected by the attacker that mimicked their implant. The goal was to intercept commands from the C2. Based on this, it was observed initiating an attack against chache08[.]werkecdn[.]me using a 120 thread HTTP2 rapid reset attack. This site appears to be hosted on an Amsterdam VPS provided by FDCServers, a server hosting company. It was not possible to identify what normally runs on this site, as it returns a 403 Forbidden error when visited.

Darktrace’s code analysis found that the returned commands contain the following fields:

  • Method (e.g. GET, POST)
  • A unique ID for the attack
  • A URL endpoint used to report attack statistics
  • The target URL & port
  • The duration of the attack
  • The number of threads to use
  • An optional proxy to send HTTP requests through

The malware then spins up several threads, each running a configurable number of HTTP clients using Valyala’s fasthttp library, an open source Go library for making high-performance HTTP requests. After this is complete, it uses these clients to perform an HTTP flood attack against the target.

A snippet showing the fasthttp client creation loop, as well as a function to report the worker count back to the C2.
Figure 6: A snippet showing the fasthttp client creation loop, as well as a function to report the worker count back to the C2.

In addition, it also features several flags to enable different bypass mechanisms to augment the malware:

  • WordPress bypass (does not appear to be implemented - the flag is not used anywhere)
  • Random query strings appended to the URL
  • Spoofed forwarding headers with random IP addresses
  • Cloudflare under-attack-mode (UAM) bypass
  • HTTP2 rapid reset

The most interesting of these is the Cloudflare UAM bypass mechanism. When this is enabled, the malware will attempt to use a bundled ChromeDP binary to solve the Cloudflare JavaScript challenge that is presented to new visitors. If this succeeds, the clearance cookie obtained is then included in subsequent requests. This is unlikely to work in most cases as headless Chrome browsers are often flagged, and a regular CAPTCHA is instead served.

The UAM bypass success snippet.
Figure 7: The UAM bypass success snippet.

Additionally, the malware has a flag to enable an HTTP2 rapid reset attack mode instead of a regular HTTP flood. In HTTP2, a client can create thousands of requests within a single connection using multiplexing, allowing sites to load faster. The number of request streams per connection is capped however, so in a rapid reset attack many requests are made and then immediately cancelled to allow more requests to be created. This allows a single client to execute vastly more requests per second and use more server resources than it otherwise would, allowing for more effective denial-of-service (DoS) attacks.

 The HTTP2 rapid reset snippet from the main attack function.
Figure 8: The HTTP2 rapid reset snippet from the main attack function.

API/C2 analysis

As mentioned throughout the malware analysis section, the malware communicates with a C2 server using HTTP. The server is behind Cloudflare, which obscures its hosting location and prevents analysis. However, based on analysis of the spreader, it's likely running on GitHub CodeSpaces.

When sending a malformed request to the API, an error generated by the Pydantic library is returned:

{"detail":[{"type":"missing","loc":["body","vps_id"],"msg":"Field required","input":{"vps_name":"xxxxx"},"url":"https://errors.pydantic.dev/2.11/v/missing"}]}

This shows they are using Python for the API, which is the same language that the spreader is written in.

One of the larger frameworks that ships with Pydantic is FastAPI, which also ships with Swagger. The malware author left this publicly exposed, and Darktrace’s researchers were able to obtain a copy of their API documentation. The author appears to have noticed this however, as subsequent attempts to access it now returns a HTTP 404 Not Found error.

Swagger UI view based on the obtained OpenAPI spec.
Figure 9: Swagger UI view based on the obtained OpenAPI spec.

This is useful to have as it shows all the API endpoints, including the exact fields they take and return, along with comments on each endpoint written by the attacker themselves.

It is very likely a DDoS for hire platform (or at the very least, designed for multi-tenant use) based on the extensive user API, which features authentication, distinctions between privilege level (admin vs user), and limitations on what types of attack a user can execute. The screenshot below shows the admin-only user create endpoint, with the default limits.

The admin-only user create endpoint shadow v2
Figure 10: The admin-only user create endpoint.

The endpoint used to launch attacks can also be seen, which lines up with the options previously seen in the malware itself. Interestingly, this endpoint requires a list of zombie systems to launch the attack from. This is unusual as most DDoS for hire services will decide this internally or just launch the attack from every infected host (zombie). No endpoints that returned a list of zombies were found, however, it’s possible one exists as the return types are not documented for all the API endpoints.

The attack start endpoint shadow v2
Figure 11: The attack start endpoint.

There is also an endpoint to manage a blacklist of hosts that cannot be attacked. This could be to stop users from launching attacks against sites operated by the malware author, however it’s also possible the author could be attempting to sell protection to victims, which has been seen previously with other DDoS for hire services.

Blacklist endpoints shadow v2 DDoS
Figure 12: Blacklist endpoints.

Attempting to visit shadow[.]aurozacloud[.]xyz results in a seizure notice. It is most likely fake the same backend is still in use and all of the API endpoints continue to work. Appending /login to the end of the path instead brings up the login screen for the DDoS platform. It describes itself as an “advanced attack platform”, which highlights that it is almost certainly a DDoS for hire service. The UI is high quality, written in Tailwind, and even features animations.

The fake seizure notice.
Figure 13: The fake seizure notice.
The login UI at /login.
Figure 14: The login UI at /login.

Conclusion

By leveraging containerization, an extensive API, and with a full user interface, this campaign shows the continued development of cybercrime-as-a-service. The ability to deliver modular functionality through a Go-based RAT and expose a structured API for operator interaction highlights how sophisticated some threat actors are.

For defenders, the implications are significant. Effective defense requires deep visibility into containerized environments, continuous monitoring of cloud workloads, and behavioral analytics capable of identifying anomalous API usage and container orchestration patterns. The presence of a DDoS-as-a-service panel with full user functionality further emphasizes the need for defenders to think of these campaigns not as isolated tools but as evolving platforms.

Appendices

References

1. https://www.virustotal.com/gui/file/1b552d19a3083572bc433714dfbc2b75eb6930a644696dedd600f9bd755042f6

2. https://www.virustotal.com/gui/file/1f70c78c018175a3e4fa2b3822f1a3bd48a3b923d1fbdeaa5446960ca8133e9c

IoCs

Malware hashes (SHA256)

●      2462467c89b4a62619d0b2957b21876dc4871db41b5d5fe230aa7ad107504c99

●      1b552d19a3083572bc433714dfbc2b75eb6930a644696dedd600f9bd755042f6

●      1f70c78c018175a3e4fa2b3822f1a3bd48a3b923d1fbdeaa5446960ca8133e9c

C2 domain

●      shadow.aurozacloud[.]xyz

Spreader IPs

●      23.97.62[.]139

●      23.97.62[.]136

Yara rule

rule ShadowV2 {

meta:

author = "nathaniel.bill@darktrace.com"

description = "Detects ShadowV2 botnet implant"

strings:

$string1 = "shadow-go"

$string2 = "shadow.aurozacloud.xyz"

$string3 = "[SHADOW-NODE]"

$symbol1 = "main.registerWithMaster"

$symbol2 = "main.handleStartAttack"

$symbol3 = "attacker.bypassUAM"

$symbol4 = "attacker.performHTTP2RapidReset"

$code1 = { 48 8B 05 ?? ?? ?? ?? 48 8B 1D ?? ?? ?? ?? E8 ?? ?? ?? ?? 48 8D 0D ?? ?? ?? ?? 48 89 8C 24 38 01 00 00 48 89 84 24 40 01 00 00 48 8B 4C 24 40 48 BA 00 09 6E 88 F1 FF FF FF 48 8D 04 0A E8 ?? ?? ?? ?? 48 8D 0D ?? ?? ?? ?? 48 89 8C 24 48 01 00 00 48 89 84 24 50 01 00 00 48 8D 05 ?? ?? ?? ?? BB 05 00 00 00 48 8D 8C 24 38 01 00 00 BF 02 00 00 00 48 89 FE E8 ?? ?? ?? ?? }

$code2 = { 48 89 35 ?? ?? ?? ?? 0F B6 94 24 80 02 00 00 88 15 ?? ?? ?? ?? 0F B6 94 24 81 02 00 00 88 15 ?? ?? ?? ?? 0F B6 94 24 82 02 00 00 88 15 ?? ?? ?? ?? 0F B6 94 24 83 02 00 00 88 15 ?? ?? ?? ?? 48 8B 05 ?? ?? ?? ?? }

$code3 = { 48 8D 15 ?? ?? ?? ?? 48 89 94 24 68 04 00 00 48 C7 84 24 78 04 00 00 15 00 00 00 48 8D 15 ?? ?? ?? ?? 48 89 94 24 70 04 00 00 48 8D 15 ?? ?? ?? ?? 48 89 94 24 80 04 00 00 48 8D 35 ?? ?? ?? ?? 48 89 B4 24 88 04 00 00 90 }

condition:

uint16(0) == 0x457f and (2 of ($string*) or 2 of ($symbol*) or any of ($code*))

}

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.

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
Nate Bill
Threat Researcher

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September 25, 2025

Announcing Unified Real-Time CDR and Automated Investigations to Transform Cloud Security Operations

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Fragmented Tools are Failing SOC Teams in the Cloud Era

The cloud has transformed how businesses operate, reshaping everything from infrastructure to application delivery. But cloud security has not kept pace. Most tools still rely on traditional models of logging, policy enforcement, and posture management; approaches that provide surface-level visibility but lack the depth to detect or investigate active attacks.

Meanwhile, attackers are exploiting vulnerabilities, delivering cloud-native exploits, and moving laterally in ways that posture management alone cannot catch fast enough. Critical evidence is often missed, and alerts lack the forensic depth SOC analysts need to separate noise from true risk. As a result, organizations remain exposed: research shows that nearly nine in ten organizations have suffered a critical cloud breach despite investing in existing security tools [1].

SOC teams are left buried in alerts without actionable context, while ephemeral workloads like containers and serverless functions vanish before evidence can be preserved. Point tools for logging or forensics only add complexity, with 82% of organizations using multiple platforms to investigate cloud incidents [2].

The result is a broken security model: posture tools surface risks but don’t connect them to active attacker behaviors, while investigation tools are too slow and fragmented to provide timely clarity. Security teams are left reactive, juggling multiple point solutions and still missing critical signals. What’s needed is a unified approach that combines real-time detection and response for active threats with automated investigation and cloud posture management in a single workflow.

Just as security teams once had to evolve beyond basic firewalls and antivirus into network and endpoint detection, response, and forensics, cloud security now requires its own next era: one that unifies detection, response, and investigation at the speed and scale of the cloud.

A Powerful Combination: Real-Time CDR + Automated Cloud Forensics

Darktrace / CLOUD now uniquely unites detection, investigation, and response into one workflow, powered by Self-Learning AI. This means every alert, from any tool in your stack, can instantly become actionable evidence and a complete investigation in minutes.

With this release, Darktrace / CLOUD delivers a more holistic approach to cloud defense, uniting real-time detection, response, and investigation with proactive risk reduction. The result is a single solution that helps security teams stay ahead of attackers while reducing complexity and blind spots.

  • Automated Cloud Forensic Investigations: Instantly capture and analyze volatile evidence from cloud assets, reducing investigation times from days to minutes and eliminating blind spots
  • Enhanced Cloud-Native Threat Detection: Detect advanced attacker behaviors such as lateral movement, privilege escalation, and command-and-control in real time
  • Enhanced Live Cloud Topology Mapping: Gain continuous insight into cloud environments, including ephemeral workloads, with live topology views that simplify investigations and expose anomalous activity
  • Agentless Scanning for Proactive Risk Reduction: Continuously monitor for misconfigurations, vulnerabilities, and risky exposures to reduce attack surface and stop threats before they escalate.

Automated Cloud Forensic Investigations

Darktrace / CLOUD now includes capabilities introduced with Darktrace / Forensic Acquisition & Investigation, triggering automated forensic acquisition the moment a threat is detected. This ensures ephemeral evidence, from disks and memory to containers and serverless workloads can be preserved instantly and analyzed in minutes, not days. The integration unites detection, response, and forensic investigation in a way that eliminates blind spots and reduces manual effort.

Figure 1: Easily view Forensic Investigation of a cloud resource within the Darktrace / CLOUD architecture map

Enhanced Cloud-Native Threat Detection

Darktrace / CLOUD strengthens its real-time behavioral detection to expose early attacker behaviors that logs alone cannot reveal. Enhanced cloud-native detection capabilities include:

• Reconnaissance & Discovery – Detects enumeration and probing activity post-compromise.

• Privilege Escalation via Role Assumption – Identifies suspicious attempts to gain elevated access.

• Malicious Compute Resource Usage – Flags threats such as crypto mining or spam operations.

These enhancements ensure active attacks are detected earlier, before adversaries can escalate or move laterally through cloud environments.

Figure 2: Cyber AI Analyst summary of anomalous behavior for privilege escalation and establishing persistence.

Enhanced Live Cloud Topology Mapping

New enhancements to live topology provide real-time mapping of cloud environments, attacker movement, and anomalous behavior. This dynamic visibility helps SOC teams quickly understand complex environments, trace attack paths, and prioritize response. By integrating with Darktrace / Proactive Exposure Management (PEM), these insights extend beyond the cloud, offering a unified view of risks across networks, endpoints, SaaS, and identity — giving teams the context needed to act with confidence.

Figure 3: Enhanced live topology maps unify visibility across architectures, identities, network connections and more.

Agentless Scanning for Proactive Risk Reduction

Darktrace / CLOUD now introduces agentless scanning to uncover malware and vulnerabilities in cloud assets without impacting performance. This lightweight, non-disruptive approach provides deep visibility into cloud workloads and surfaces risks before attackers can exploit them. By continuously monitoring for misconfigurations and exposures, the solution strengthens posture management and reduces attack surface across hybrid and multi-cloud environments.

Figure 4: Agentless scanning of cloud assets reveals vulnerabilities, which are prioritized by severity.

Together, these capabilities move cloud security operations from reactive to proactive, empowering security teams to detect novel threats in real time, reduce exposures before they are exploited, and accelerate investigations with forensic depth. The result is faster triage, shorter MTTR, and reduced business risk — all delivered in a single, AI-native solution built for hybrid and multi-cloud environments.

Accelerating the Evolution of Cloud Security

Cloud security has long been fragmented, forcing teams to stitch together posture tools, log-based monitoring, and external forensics to get even partial coverage. With this release, Darktrace / CLOUD delivers a holistic, unified approach that covers every stage of the cloud lifecycle, from proactive posture management and risk identification to real-time detection, to automated investigation and response.

By bringing these capabilities together in a single AI-native solution, Darktrace is advancing cloud security beyond incremental change and setting a new standard for how organizations protect their hybrid and multi-cloud environments.

With Darktrace / CLOUD, security teams finally gain end-to-end visibility, response, and investigation at the speed of the cloud, transforming cloud defense from fragmented and reactive to unified and proactive.

[related-resource]

Sources: [1], [2] Darktrace Report: Organizations Require a New Approach to Handle Investigations in the Cloud

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About the author
Adam Stevens
Senior Director of Product, Cloud | Darktrace

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September 25, 2025

Introducing the Industry’s First Truly Automated Cloud Forensics Solution

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Why Cloud Investigations Fail Today

Cloud investigations have become one of the hardest problems in modern cybersecurity. Traditional DFIR tools were built for static, on-prem environments, rather than dynamic and highly scalable cloud environments, containing ephemeral workloads that disappear in minutes. SOC analysts are flooded with cloud security alerts with one-third lacking actionable data to confirm or dismiss a threat[1], while DFIR teams waste 3-5 days requesting access and performing manual collection, or relying on external responders.

These delays leave organizations vulnerable. Research shows that nearly 90% of organizations suffer some level of damage before they can fully investigate and contain a cloud incident [2]. The result is a broken model: alerts are closed without a complete understanding of the threat due to a lack of visibility and control, investigations drag on, and attackers retain the upper hand.

For SOC teams, the challenge is scale and clarity. Analysts are inundated with alerts but lack the forensic depth to quickly distinguish real threats from noise. Manual triage wastes valuable time, creates alert fatigue, and often forces teams to escalate or dismiss incidents without confidence — leaving adversaries with room to maneuver.

For DFIR teams, the challenge is depth and speed. Traditional forensics tools were built for static, on-premises environments and cannot keep pace with ephemeral workloads that vanish in minutes. Investigators are left chasing snapshots, requesting access from cloud teams, or depending on external responders, leading to blind spots and delayed response.

That’s why we built Darktrace / Forensic Acquisition & Investigation, the first automated forensic solution designed specifically for the speed, scale, and complexities of the cloud. It addresses both sets of challenges by combining automated forensic evidence capture, attacker timeline reconstruction, and cross-cloud scale. The solution empowers SOC analysts with instant clarity and DFIR teams with forensic depth, all in minutes, not days. By leveraging the very nature of the cloud, Darktrace makes these advanced capabilities accessible to security teams of all sizes, regardless of expertise or resources.

Introducing Automated Forensics at the Speed and Scale of Cloud

Darktrace / Forensic Acquisition & Investigation transforms cloud investigations by capturing, processing, and analyzing forensic evidence of cloud workloads, instantly, even from time-restricted ephemeral resources. Triggered by a detection from any cloud security tool, the entire process is automated, providing accurate root cause analysis and deep insights into attacker behavior in minutes rather than days or weeks. SOC and DFIR teams no longer have to rely on manual processes, snapshots, or external responders, they can now leverage the scale and elasticity of the cloud to accelerate triage and investigations.

Seamless Integration with Existing Detection Tools

Darktrace / Forensic Acquisition & Investigation does not require customers to replace their detection stack. Instead, it integrates with cloud-native providers, XDR platforms, and SIEM/SOAR tools, automatically initiating forensic capture whenever an alert is raised. This means teams can continue leveraging their existing investments while gaining the forensic depth required to validate alerts, confirm root cause, and accelerate response.

Most importantly, the solution is natively integrated with Darktrace / CLOUD, turning real-time detections of novel attacker behaviors into full forensic investigations instantly. When Darktrace / CLOUD identifies suspicious activity such as lateral movement, privilege escalation, or abnormal usage of compute resources, Darktrace / Forensic Acquisition & Investigation automatically preserves the underlying forensic evidence before it disappears. This seamless workflow unites detection, response, and investigation in a way that eliminates gaps, accelerates triage, and gives teams confidence that every critical cloud alert can be investigated to completion.

Figure 1: Integration with Darktrace / CLOUD – this example is showing the ability to pivot into the forensic investigation associated with a compromised cloud asset

Automated Evidence Collection Across Hybrid and Multi-Cloud

The solution provides automated forensic acquisition across AWS, Microsoft Azure, GCP, and on-prem environments. It supports both full volume capture, creating a bit-by-bit copy of an entire storage device for the most comprehensive preservation of evidence, and triage collection, which prioritizes speed by gathering only the most essential forensic artifacts such as process data, logs, network connections, and open file contents. This flexibility allows teams to strike the right balance between speed and depth depending on the investigation at hand.

Figure 2: Ability to acquire forensic data from Cloud, SaaS and on-prem environments

Automated Investigations, Root Cause Analysis and Attacker Timelines

Once evidence is collected, Darktrace applies automation to reconstruct attacker activity into a unified timeline. This includes correlating commands, files, lateral movement, and network activity into a single investigative view enriched with custom threat intelligence such as IOCs. Detailed investigation reporting including an investigation summary, an overview of the attacker timeline, and key events. Analysts can pivot into detailed views such as the filesystem view, traversing directories or inspecting file content, or filter and search using faceted options to quickly narrow the scope of an investigation.

Figure 3: Automated Investigation view surfacing the most significant attacker activity, which is contextualized with Alarm information

Forensics for Containers and Ephemeral Assets

Investigating containers and serverless workloads has historically been one of the hardest challenges for DFIR teams, as these assets often disappear before evidence can be preserved. Darktrace / Forensic Acquisition & Investigation captures forensic evidence across managed Kubernetes cloud services, even from distroless or no-shell containers, AWS ECS and other environments, ensuring that ephemeral activity is no longer a blind spot. For hybrid organizations, this extends to on-premises Kubernetes and OpenShift deployments, bringing consistency across environments.

Figure 4: Container investigations – this example is showing the ability to capture containers from managed Kubernetes cloud services

SaaS Log Collection for Modern Investigations

Beyond infrastructure-level data, the solution collects logs from SaaS providers such as Microsoft 365, Entra ID, and Google Workspace. This enables investigations into common attack types like business email compromise (BEC), account takeover (ATO), and insider threats — giving teams visibility into both infrastructure-level and SaaS-driven compromise from a single platform.

Figure 5: Ability to import logs from SaaS providers including Microsoft 365, Entra ID, and Google Workspace

Proactive Vulnerability and Malware Discovery

Finally, the solution surfaces risk proactively with vulnerability and malware discovery for Linux-based cloud resources. Vulnerabilities are presented in a searchable table and correlated with the attacker timeline, enabling teams to quickly understand not just which packages are exposed, but whether they have been targeted or exploited in the context of an incident.

Figure 6: Vulnerability data with pivot points into the attacker timeline

Cloud-Native Scale and Performance

Darktrace / Forensic Acquisition & Investigation uses a cloud-native parallel processing architecture that spins up compute resources on demand, ensuring that investigations run at scale without bottlenecks. Detailed reporting and summaries are automatically generated, giving teams a clear record of the investigation process and supporting compliance, litigation readiness, and executive reporting needs.

Scalable and Flexible Deployment Options

Every organization has different requirements for speed, control, and integration. Darktrace / Forensic Acquisition & Investigation is designed to meet those needs with two flexible deployment models.

  • Self-Hosted Virtual Appliance delivers deep integration and control across hybrid environments, preserving forensic data for compliance and litigation while scaling to the largest enterprise investigations.
  • SaaS-Delivered Deployment provides fast time-to-value out of the box, enabling automated forensic response without requiring deep cloud expertise or heavy setup.

Both models are built to scale across regions and accounts, ensuring organizations of any size can achieve rapid value and adapt the solution to their unique operational and compliance needs. This flexibility makes advanced cloud forensics accessible to every security team — whether they are optimizing for speed, integration depth, or regulatory alignment

Delivering Advanced Cloud Forensics for Every Team

Until now, forensic investigations were slow, manual, and reserved for only the largest organizations with specialized DFIR expertise. Darktrace / Forensic Acquisition & Investigation changes that by leveraging the scale and elasticity of the cloud itself to automate the entire investigation process. From capturing full disk and memory at detection to reconstructing attacker timelines in minutes, the solution turns fragmented workflows into streamlined investigations available to every team.

Whether deployed as a SaaS-delivered service for fast time-to-value or as a self-hosted appliance for deep integration, Darktrace / Forensic Acquisition & Investigation provides the features that matter most: automated evidence capture, cross-cloud investigations, forensic depth for ephemeral assets, and root cause clarity without manual effort.

With Darktrace / Forensic Acquisition & Investigation, what once took days now takes minutes. Now, forensic investigations in the cloud are faster, more scalable, and finally accessible to every security team, no matter their size or expertise.

[related-resource]

Sources: [1], [2] Darktrace Report: Organizations Require a New Approach to Handle Investigations in the Cloud

Additional Resources

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About the author
Paul Bottomley
Director of Product Management | Darktrace
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