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November 6, 2023

How PlugX Malware Has Evolved & Adapted

Discover how Darktrace effectively detected and thwarted the PlugX remote access trojan in 2023 despite its highly evasive and adaptive nature.
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
Nahisha Nobregas
SOC Analyst
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06
Nov 2023

What is PlugX Remote Access Trojan?

Understanding remote access trojans (RATs)

As malicious actors across the threat landscape continue to pursue more efficient and effective ways of compromising target networks, all while remaining undetected by security measures, it is unsurprising to see an increase in the use of remote access trojans (RATs) in recent years. RATs typically operate stealthily, evading security tools while offering threat actors remote control over infected devices, allowing attackers to execute a wide range of malicious activities like data theft or installing additional malware.

Definition and general functionality of RATs: A Remote Access Trojan (RAT) is a type of malware that enables unauthorized remote control of an infected computer. Once installed, RATs allow attackers to monitor user activities, steal sensitive information, manipulate files, and execute commands. RATs are typically distributed via phishing emails, malicious attachments, drive-by downloads, or exploiting software vulnerabilities. Due to their ability to provide comprehensive control over a compromised system, RATs pose a significant security threat to individuals and organizations.

Historical overview of PlugX

PlugX is one such example of a RAT that has attributed to Chinese threat actors such as Mustang Panda, since it first appeared in the wild back in 2008. It is known for its use in espionage, a modular and plug-in style approach to malware development. It has the ability to evolve with the latest tactics, techniques, and procedures (TTPs) that allow it to avoid the detection of traditional security tools as it implants itself target devices.

How does PlugX work?

The ultimate goal of any RAT is to remotely control affected devices with a wide range of capabilities, which in PlugX’s case has typically included rebooting systems, keylogging, managing critical system processes, and file upload/downloads. One technique PlugX heavily relies on is dynamic-link library (DLL) sideloading to infiltrate devices. This technique involves executing a malicious payload that is embedded within a benign executable found in a data link library (DLL) [1]. The embedded payload within the DLL is often encrypted or obfuscated to prevent detection.

What’s more, a new variant of PlugX was observed in the wild across Papua New Guinea, Ghana, Mongolia, Zimbabwe, and Nigeria in August 2022, that added several new capabilities to its toolbox.

Key capabilities of PlugX

The new variation is reported to continuously monitor affected environments for new USB devices to infect, allowing it to spread further through compromised networks [2]. It is then able to hide malicious files within a USB device by using a novel technique that prevents them from being viewed on Windows operating systems (OS). These hidden files can only be viewed on a Unix-like (.nix) OS, or by analyzing an affected USB devices with a forensic tool [2]. The new PlugX variant also has the ability to create a hidden directory, “RECYCLER.BIN”, containing a collection of stolen documents, likely in preparation for exfiltration via its command and control (C2) channels. [3]

Since December 2022, PlugX has been observed targeting networks in Europe through malware delivery via HTML smuggling campaigns, a technique that has been dubbed SmugX [4].

This evasive tactic allows threat actors to prepare and deploy malware via phishing campaigns by exploiting legitimate HTML5 and JavaScript features [5].

Darktrace Coverage of PlugX

Between January and March 2023, Darktrace observed activity relating to the PlugX RAT on multiple customers across the fleet. While PlugX’s TTPs may have bypassed traditional security tools, the anomaly-based detection capabilities of Darktrace allowed it to identify and alert the subtle deviations in the behavior of affected devices, while Darktrace was able to take immediate mitigative action against such anomalous activity and stop attackers in their tracks.  

C2 Communication

Between January and March 2023, Darktrace detected multiple suspicious connections related to the PlugX RAT within customer environments. When a device has been infected, it will typically communicate through C2 infrastructure established for the PlugX RAT. In most cases observed by Darktrace, affected devices exhibited suspicious C2 connections to rare endpoints that were assessed with moderate to high confidence to be linked to PlugX.

On the network of one Darktrace customer the observed communication was a mix of successful and unsuccessful connections at a high volume to rare endpoints on ports such as 110, 443, 5938, and 80. These ports are commonly associated with POP3, HTTPS, TeamViewer RDP / DynGate, and HTTP, respectively.  Figure 1 below showcases this pattern of activity.

Figure 1: Model Breach Event Log demonstrating various successful and unsuccessful connections to the PlugX C2 endpoint 103.56.53[.]46 via various destination ports.

On another customer’s network, Darktrace observed C2 communication involving multiple failed connection attempts to another rare external endpoint associated with PlugX. The device in this case was detected attempting connections to the endpoint, 45.142.166[.]112 on ports 110, 80, and 443 which caused the DETECT model ‘Anomalous Connection / Multiple Failed Connections to Rare Endpoint’ to breach. This model examines devices attempting connections to a rare external endpoint over a short period of time, and it breached in response to almost all PlugX C2 related activity detected by Darktrace. This highlights Darktrace DETECT’s unique ability to identify anomalous activity which appears benign or uncertain, rather than relying on traditional signature-based detections.

Figure 2: Device Event Log demonstrating various successful and unsuccessful connections to the PlugX C2 endpoint 45.142.166[.]112 via various destination on January 27, 2023.

New User Agent

Darktrace's Self-Learning AI approach to threat detection also allowed it to recognize connections to PlugX associated endpoints that utilized a new user agent. In almost all connections to PlugX endpoints detected by Darktrace, the same user agent, Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36, was observed, illustrating a clear pattern in PlugX-related activity

In one example from February 2023, an affected device successfully connected to an endpoint associated with PlugX, 45.142.166[.]112, while using the aforementioned new user agent, as depicted in Figure 3.

Figure 3: The Device Event log above showcases a successful connection to the PlugX associated IP address, 45.142.166[.]112 using the new user agent ‘Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36’.

On March 21, 2023, Darktrace observed similar activity on a separate customer’s network affected by connections to PlugX. This activity included connections to the same endpoint, 45.142.166[.]112. The connection was an HTTP POST request made via proxy with the same new user agent, ‘Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36’. When investigated further this user agent actually reveals very little about itself and appears to be missing a couple of common features that are typically contained in a user agent string, such as a web browser and its version or the mention of Safari before its build ID (‘537.36’).

Additionally, for this connection the URI observed consisted of a random string of 8 hexadecimal characters, namely ‘d819f07a’. This is a technique often used by malware to communicate with its C2 servers, while evading the detection of signature-based detection tools. Darktrace, however, recognized that this external connection to an endpoint with no hostname constituted anomalous behavior, and could have been indicative of a threat actor communicating with malicious infrastructure, thus the ‘Anomalous Connection / Possible Callback URI’ model was breached.

Figure 4: An affected device was detected using the new user agent, ‘Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36’ while connecting to the rare external endpoint 45.142.166[.]112 via proxy.

Numeric File Download

Darktrace’s detection of PlugX activity on another customer’s network, in February 2023, helped to demonstrate related patterns of activity within the C2 communication and tooling attack phases. Observed PlugX activity on this network followed the subsequent pattern; a connection to a PlugX endpoints is made, followed by a HTTP POST request to a numeric URI with a random string of 8 hexadecimal characters, as previously highlighted. Darktrace identified that this activity represented unusual ‘New Activity’ for this device, and thus treated it with suspicion.

Figure 5: New activity was identified by Darktrace in the Device Event Log shown above for connections to the endpoint 45.142.166[.]112 followed by HTTP POSTs to URIs “/8891431c” and “/ba12b866” on February 15, 2023.

The device in question continued to connect to the endpoint and make HTTP POST connections to various URIs relating to PlugX. Additionally, the user agent `Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36` was again detected for these connections. Figure 6 details the activity captured by Darktrace’s Cyber AI Analyst.

Figure 6: The image above showcases activity captured by Darktrace’s AI Analyst for PlugX connections made on February 15, 2023.

Darktrace detected that during these connections, the device in question attempted to download a suspicious file named only with numbers. The use of numeric file names is a technique often used by threat actors to obfuscate the download of malicious files or programs and bypass traditional security tools. Darktrace understood that the download of a numeric file, coupled with the use of an anomalous new user agent, mean the incident should be treated with suspicion. Fortunately, Darktrace RESPOND was enabled in autonomous response mode during this attack, meaning it was able to automatically block the device from downloading the file, or any other files, from the suspicious external location for a two-hour period, potentially preventing the download of PlugX’s malicious tooling.

Conclusion

Amid the continued evolution of PlugX from an espionage tool to a more widely available malware, it is essential that threat detection does not rely on a set of characteristics or indicators, but rather is focused on anomalies. Throughout these cases, Darktrace demonstrated the efficacy of its detection and alerting on emerging activity pertaining to a particularly stealthy and versatile RAT. Over the years, PlugX has continually looked to evolve and survive in the ever-changing threat landscape by adapting new capabilities and TTPs through which it can infect a system and spread to new devices without being noticed by security teams and their tools.

However, Darktrace’s Self-Learning AI allows it to gain a strong understanding of customer networks, learning what constitutes expected network behavior which in turn allows it to recognize the subtle deviations indicative of an ongoing compromise.

Darktrace’s ability to identify emerging threats through anomaly-based detection, rather than relying on established threat intelligence, uniquely positions it to detect and respond to highly adaptable and dynamic threats, like the PlugX malware, regardless of how it may evolve in the future.

Credit to: Nahisha Nobregas, SOC Analyst & Dylan Hinz, Cyber Analyst

Appendices

MITRE ATT&CK Framework

Execution

  • T1059.003 Command and Scripting Interpreter: Windows Command Shell

Persistence and Privilege Escalation

  • T1547.001 Boot or Logon AutoStart Execution: Registry Run Keys / Startup Folder
  • T1574.001 Hijack Execution Flow: DLL Search Order Hijacking
  • T1574.002 Hijack Execution Flow: DLL Side-Loading
  • T1543.003 Create or Modify System Process: Windows Service
  • T1140 Deobfuscate / Decode Files or Information
  • T1083 File and Directory Discovery

Defense Evasion

  • T1564.001 Hide Artifacts: Hidden Files and Directories
  • T1036.004 Masquerading: Task or Service
  • T1036.005 Masquerading: Match Legitimate Name or Location
  • T1027.006 Obfuscated Files or Information: HTML Smuggling

Credential Access

  • T1056.001 Input Capture: Keylogging

Collection

  • T1105 Ingress Tool Transfer

Command and Control

  • T1573.001 Encrypted Channel: Symmetric Cryptography
  • T1070.003 Mail Protocols
  • T1071.001 Web Protocol

DETECT Model Breaches

  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / New User Agent Followed By Numeric File Download
  • Anomalous Connection / Possible Callback URL

Indicators of Compromise (IoCs)

IoC - Type - Description + Confidence

45.142.166[.]112 - IP - PlugX C2 Endpoint / moderate - high

103.56.53[.]46 - IP - PlugX C2 Endpoint / moderate - high

Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36 - User Agent - PlugX User Agent / moderate – high

/8891431c - URI - PlugX URI / moderate-high

/ba12b866 - URI - PlugX URI / moderate -high

References

1. https://www.crowdstrike.com/blog/dll-side-loading-how-to-combat-threat-actor-evasion-techniques/

2. https://unit42.paloaltonetworks.com/plugx-variants-in-usbs/

3. https://news.sophos.com/en-us/2023/03/09/border-hopping-plugx-usb-worm/

4. https://thehackernews.com/2023/07/chinese-hackers-use-html-smuggling-to.html

5. https://www.cyfirma.com/outofband/html-smuggling-a-stealthier-approach-to-deliver-malware/

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
Nahisha Nobregas
SOC Analyst

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July 17, 2025

Introducing the AI Maturity Model for Cybersecurity

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AI adoption in cybersecurity: Beyond the hype

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

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

Overview of AI maturity levels in cybersecurity

Why a maturity model? And why now?

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

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

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

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

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

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How is maturity assessed in this model?

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

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

Why Darktrace?

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

Security teams today are asking big, important questions:

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

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

The structure: From experimentation to autonomy

The model outlines five levels of maturity :

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

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

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

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

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

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

Strategic benefits for security leaders

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

SOC fatigue is real, and AI can help

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

GenAI is error prone, requiring strong human oversight

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

AI’s real value lies in progression

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

Trust and oversight are key initially but evolves in later levels

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

People’s roles shift meaningfully

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

Outcome, not hype, defines maturity

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

[related-resource]

Outcomes across the AI Security Maturity Model

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

L0 – Manual Operations

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

L1 – Automation Rules

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

L2 – AI Assistance

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

L3 – AI Collaboration

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

L4 – AI Delegation

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

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

Join the webinar

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

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July 17, 2025

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

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

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

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

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

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

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1. Depth of data

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

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

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

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

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

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

2. Chain of custody

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

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

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

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

3. Automated collection and isolation

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

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

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

4. Ease of use

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

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

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

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

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

5. Incident preparedness

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

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

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

Cloud forensics with Darktrace

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

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

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