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August 22, 2022

Emotet Resurgence: Cross-Industry Analysis

Technical insights on the Emotet resurgence in 2022 across various client environments, industries, and regions.
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
Eugene Chua
Cyber Security Analyst
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22
Aug 2022

Introduction

Last year provided further evidence that the cyber threat landscape remains both complex and challenging to predict. Between uncertain attribution, novel exploits and rapid malware developments, it is becoming harder to know where to focus security efforts. One of the largest surprises of 2021 was the re-emergence of the infamous Emotet botnet. This is an example of a campaign that ignored industry verticals or regions and seemingly targeted companies indiscriminately. Only 10 months after the Emotet takedown by law enforcement agencies in January, new Emotet activities in November were discovered by security researchers. These continued into the first quarter of 2022, a period which this blog will explore through findings from the Darktrace Threat Intel Unit. 

Dating back to 2019, Emotet was known to deliver Trickbot payloads which ultimately deployed Ryuk ransomware strains on compromised devices. This interconnectivity highlighted the hydra-like nature of threat groups wherein eliminating one (even with full-scale law enforcement intervention) would not rule them out as a threat nor indicate that the threat landscape would be any more secure. 

When Emotet resurged, as expected, one of the initial infection vectors involved leveraging existing Trickbot infrastructure. However, unlike the original attacks, it featured a brand new phishing campaign.

Figure 1: Distribution of observed Emotet activities across Darktrace deployments

Although similar to the original Emotet infections, the new wave of infections has been classified into two categories: Epochs 4 and 5. These had several key differences compared to Epochs 1 to 3. Within Darktrace’s global deployments, Emotet compromises associated to Epoch 4 appeared to be the most prevalent. Affected customer environments were seen within a large range of countries (Figure 1) and industry verticals such as manufacturing and supply chain, hospitality and travel, public administration, technology and telecoms and healthcare. Company demographics and size did not appear to be a targeting factor as affected customers had varying employee counts ranging from less than 250, to over 5000.

Key differences between Epochs 1-3 vs 4-5

Based on wider security research into the innerworkings of the Emotet exploits, several key differences were identified between Epochs 4/5 and its predecessors. The newer epochs used:

·       A different Microsoft document format (OLE vs XML-based).

·       A different encryption algorithm for communication. The new epochs used Elliptic Curve Cryptograph (ECC) [1] with public encryption keys contained in the C2 configuration file [2]. This was different from the previous Rivest-Shamir-Adleman (RSA) key encryption method.

·       Control Flow Flattening was used as an obfuscation technique to make detection and reverse engineering more difficult. This is done by hiding a program’s control flow [3].

·       New C2 infrastructure was observed as C2 communications were directed to over 230 unique IPs all associated to the new Epochs 4 and 5.

In addition to the new Epoch 4 and 5 features, Darktrace detected unsurprising similarities in those deployments affected by the renewed campaign. This included self-signed SSL connections to Emotet’s new infrastructure as well as malware spam activities to multiple rare external endpoints. Preceding these outbound communications, devices across multiple deployments were detected downloading Emotet-associated payloads (algorithmically generated DLL files).

Emotet Resurgence Campaign

Figure 2: Darktrace’s Detection Timeline for Emotet Epoch 4 and 5 compromises

1. Initial Compromise

The initial point of entry for the resurgence activity was almost certainly via Trickbot infrastructure or a successful phishing attack (Figure 2). Following the initial intrusion, the malware strain begins to download payloads via macro-ladened files which are used to spawn PowerShell for subsequent malware downloads.

Following the downloads, malicious communication with Emotet’s C2 infrastructure was observed alongside activities from the spam module. Within Darktrace, key techniques were observed and documented below.

2. Establish Foothold: Binary Dynamic-link library (.dll) with algorithmically generated filenames 

Emotet payloads are polymorphic and contain algorithmically generated filenames . Within deployments, HTTP GET requests involving a suspicious hostname, www[.]arkpp[.]com, and Emotet related samples such as those seen below were observed:

·       hpixQfCoJb0fS1.dll (SHA256 hash: 859a41b911688b00e104e9c474fc7aaf7b1f2d6e885e8d7fbf11347bc2e21eaa)

·       M0uZ6kd8hnzVUt2BNbRzRFjRoz08WFYfPj2.dll (SHA256 hash: 9fbd590cf65cbfb2b842d46d82e886e3acb5bfecfdb82afc22a5f95bda7dd804)

·       TpipJHHy7P.dll (SHA256 hash: 40060259d583b8cf83336bc50cc7a7d9e0a4de22b9a04e62ddc6ca5dedd6754b)

These DLL files likely represent the distribution of Emotet loaders which depends on windows processes such as rundll32[.]exe and regsvr32[.]exe to execute. 

3. Establish Foothold: Outbound SSL connections to Emotet C2 servers 

A clear network indicator of compromise for Emotet’s C2 communication involved self-signed SSL using certificate issuers and subjects which matched ‘CN=example[.]com,OU=IT Department,O=Global Security,L=London,ST=London,C=GB’ , and a common JA3 client fingerprint (72a589da586844d7f0818ce684948eea). The primary C2 communications were seen involving infrastructures classified as Epoch 4 rather than 5. Despite encryption in the communication content, network contextual connection details were sufficient for the detection of the C2 activities (Figure 3).

Figure 3: UI Model Breach logs on download and outbound SSL activities.

Outbound SSL and SMTP connections on TCP ports 25, 465, 587 

An anomalous user agent such as, ‘Microsoft Outlook 15.0’, was observed being used for SMTP connections with some subject lines of the outbound emails containing Base64-encoded strings. In addition, this JA3 client fingerprint (37cdab6ff1bd1c195bacb776c5213bf2) was commonly seen from the SSL connections. Based on the set of malware spam hostnames observed across at least 10 deployments, the majority of the TLDs were .jp, .com, .net, .mx, with the Japanese TLD being the most common (Figure 4).

Figure 4: Malware Spam TLDs observed in outbound SSL and SMTP

 Plaintext spam content generated from the spam module were seen in PCAPs (Figure 5). Examples of clear phishing or spam indicators included 1) mismatched personal header and email headers, 2) unusual reply chain and recipient references in the subject line, and 3) suspicious compressed file attachments, e.g. Electronic form[.]zip.

Figure 5: Example of PCAP associated to SPAM Module

4. Accomplish Mission

 The Emotet resurgence also showed through secondary compromises involving anomalous SMB drive writes related to CobaltStrike. This consistently included the following JA3 hash (72a589da586844d7f0818ce684948eea) seen in SSL activities as well as SMB writes involving the svchost.exe file.

Darktrace Detection

 The key DETECT models used to identify Emotet Resurgence activities were focused on determining possible C2. These included:

·       Suspicious SSL Activity

·       Suspicious Self-Signed SSL

·       Rare External SSL Self-Signed

·       Possible Outbound Spam

File-focused models were also beneficial and included:

·       Zip or Gzip from Rare External Location

·       EXE from Rare External Location

Darktrace’s detection capabilities can also be shown through a sample of case studies identified during the Threat Research team’s investigations.

Case Studies 

Darktrace’s detection of Emotet activities was not limited by industry verticals or company sizing. Although there were many similar features seen across the new epoch, each incident displayed varying techniques from the campaign. This is shown in two client environments below:

When investigating a large customer environment within the public administration sector, 16 different devices were detected making 52,536 SSL connections with the example[.]com issuer. Devices associated with this issuer were mainly seen breaching the same Self-Signed and Spam DETECT models. Although anomalous incoming octet-streams were observed prior to this SSL, there was no clear relation between the downloads and the Emotet C2 connections. Despite the total affected devices occupying only a small portion of the total network, Darktrace analysts were able to filter against the much larger network ‘noise’ and locate detailed evidence of compromise to notify the customer.

Darktrace also identified new Emotet activities in much smaller customer environments. Looking at a company in the healthcare and pharmaceutical sector, from mid-March 2022 a single internal device was detected making an HTTP GET request to the host arkpp[.]com involving the algorithmically-generated DLL, TpipJHHy7P.dll with the SHA256 hash: 40060259d583b8cf83336bc50cc7a7d9e0a4de22b9a04e62ddc6ca5dedd6754b (Figure 6). 

Figure 6: A screenshot from VirusTotal, showing that the SHA256 hash has been flagged as malicious by other security vendors.

After the sample was downloaded, the device contacted a large number of endpoints that had never been contacted by devices on the network. The endpoints were contacted over ports 443, 8080, and 7080 involving Emotet related IOCs and the same SSL certificate mentioned previously. Malware spam activities were also observed during a similar timeframe.

 The Emotet case studies above demonstrate how autonomous detection of an anomalous sequence of activities - without depending on conventional rules and signatures - can reveal significant threat activities. Though possible staged payloads were only seen in a proportion of the affected environments, the following outbound C2 and malware spam activities involving many endpoints and ports were sufficient for the detection of Emotet.

 If present, in both instances Darktrace’s Autonomous Response technology, RESPOND, would recommend or implement surgical actions to precisely target activities associated with the staged payload downloads, outgoing C2 communications, and malware spam activities. Additionally, restriction to the devices’ normal pattern of life will prevent simultaneously occurring malicious activities while enabling the continuity of normal business operations.

 Conclusion 

·       The technical differences between past and present Emotet strains emphasizes the versatility of malicious threat actors and the need for a security solution that is not reliant on signatures.

·       Darktrace’s visibility and unique behavioral detection continues to provide visibility to network activities related to the novel Emotet strain without reliance on rules and signatures. Key examples include the C2 connections to new Emotet infrastructure.

·       Looking ahead, detection of C2 establishment using suspicious DLLs will prevent further propagation of the Emotet strains across networks.

·       Darktrace’s AI detection and response will outpace conventional post compromise research involving the analysis of Emotet strains through static and dynamic code analysis, followed by the implementation of rules and signatures.

Thanks to Paul Jennings and Hanah Darley for their contributions to this blog.

Appendices

Model breaches

·       Anomalous Connection / Anomalous SSL without SNI to New External 

·       Anomalous Connection / Application Protocol on Uncommon Port 

·       Anomalous Connection / Multiple Connections to New External TCP Port 

·       Anomalous Connection / Multiple Failed Connections to Rare Endpoint 

·       Anomalous Connection / Multiple HTTP POSTs to Rare Hostname 

·       Anomalous Connection / Possible Outbound Spam 

·       Anomalous Connection / Rare External SSL Self-Signed 

·       Anomalous Connection / Repeated Rare External SSL Self-Signed      

·       Anomalous Connection / Suspicious Expired SSL 

·       Anomalous Connection / Suspicious Self-Signed SSL

·       Anomalous File / Anomalous Octet Stream (No User Agent) 

·       Anomalous File / Zip or Gzip from Rare External Location 

·       Anomalous File / EXE from Rare External Location

·       Compromise / Agent Beacon to New Endpoint 

·       Compromise / Beacon to Young Endpoint 

·       Compromise / Beaconing Activity To External Rare 

·       Compromise / New or Repeated to Unusual SSL Port 

·       Compromise / Repeating Connections Over 4 Days 

·       Compromise / Slow Beaconing Activity To External Rare 

·       Compromise / SSL Beaconing to Rare Destination 

·       Compromise / Suspicious Beaconing Behaviour 

·       Compromise / Suspicious Spam Activity 

·       Compromise / Suspicious SSL Activity 

·       Compromise / Sustained SSL or HTTP Increase 

·       Device / Initial Breach Chain Compromise 

·       Device / Large Number of Connections to New Endpoints 

·       Device / Long Agent Connection to New Endpoint 

·       Device / New User Agent 

·       Device / New User Agent and New IP 

·       Device / SMB Session Bruteforce 

·       Device / Suspicious Domain 

·       Device / Suspicious SMB Scanning Activity 

For Darktrace customers who want to know more about using Darktrace to triage Emotet, refer here for an exclusive supplement to this blog.

References

[1] https://blog.lumen.com/emotet-redux/

[2] https://blogs.vmware.com/security/2022/03/emotet-c2-configuration-extraction-and-analysis.html

[3] https://news.sophos.com/en-us/2022/05/04/attacking-emotets-control-flow-flattening/

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
Eugene Chua
Cyber Security 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.

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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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Ashanka Iddya
Senior Director, Product Marketing

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July 17, 2025

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

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

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

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

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

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

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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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About the author
Calum Hall
Technical Content Researcher
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