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July 8, 2021

Minimizing the REvil Impact Delivered via Kaseya Servers

Ransomware group REvil recently infiltrated Managed Service Providers for 1,500+ companies. See how Darktrace's autonomous response protected customer data.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Max Heinemeyer
Global Field CISO
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08
Jul 2021

As the USA prepared for a holiday weekend ahead of the Fourth of July, the ransomware group REvil were leveraging a vulnerability in Kaseya software to attack Managed Service Providers (MSPs) and their downstream customers. At least 1,500 companies appear to have been affected, even ones with no direct relationship to Kaseya.

At the time of writing, it appears that a zero-day vulnerability was used to gain access to the Kaseya VSA servers, before deploying ransomware on the endpoints managed by those VSA servers. This modus operandi vastly differs from previous ransomware campaigns which have traditionally been human-operated, direct intrusions.

The analysis below offers Darktrace’s insights into the campaign by looking at a real-life example. It highlights how Self-Learning AI detected the ransomware attack, and how Antigena protected customer data on the network from being encrypted.

Dissecting REvil ransomware from the network perspective

Antigena detected the first signs of ransomware on the network as soon as encryption had begun. The graphic below illustrates the start of the ransomware encryption over SMB shares. When the graphic was taken, the attack was happening live and had never been seen before. As it was a novel threat, Darktrace stopped the network encryption without any static signatures or rules.

Figure 1: Darktrace detects encryption from the infected device

The ransomware began to take action at 11:08:32, shown by the ‘SMB Delete Success’ from the infected laptop to an SMB server. While the laptop sometimes reads files on that SMB server, it never deletes these types of files on this particular file share, so Darktrace detected this activity as new and unusual.

Simultaneously, the infected laptop created the ransom note ‘943860t-readme.txt’. Again, the ‘SMB Write Success’ to the SMB server was new activity – and crucially, Darktrace did not look for a static string or a known ransom note. Instead – by previously learning the ‘normal’ behavior of every entity, peer group, and the overall enterprise – it identified that the activity was unusual and new for this organization and device.

By detecting and correlating these subtle anomalies, Darktrace identified this as the earliest stages of ransomware encryption on the network and Antigena took immediate action.

Figure 2: Snapshot of Antigena’s actions

Antigena took two precise steps:

  1. Enforce ‘pattern of life’ for five minutes: This prevented the infected laptop from making any connections that were new or unusual. In this case, it prevented any further new SMB encryption activity.
  2. Quarantine device for 24 hours: Usually, Antigena would not take such drastic action, but it was clear that this activity closely resembled ransomware behavior, so Antigena decided to quarantine the device on the network completely to prevent it from doing any further damage.

For several minutes, the infected laptop kept trying to connect to other internal devices via SMB to continue the encryption activity. It was blocked by Antigena at every stage, limiting the spread of the attack and mitigating any damage posed via the network encryption.

Figure 3: End of the attack

On a technical level, Antigena delivered the blocking mechanisms via integrations with native security controls such as existing firewalls, or by taking action itself to disrupt the connections.

The below graphic shows the ‘pattern of life’ for all network connections for the infected laptop. The three red dots represent Darktrace’s detections and pinpoint the exact moment in time when REvil ransomware was installed on the laptop. The graphic also shows an abrupt stop to all network communication as Antigena quarantined the device.

Figure 4: Network connections from the compromised laptop

Attacks will always get in

During the incident, part of the encryption happened locally on the endpoint device, which Darktrace had no visibility over. Furthermore, the Internet-facing Kaseya VSA server that was initially compromised was not visible to Darktrace in this case.

Nevertheless, Self-Learning AI detected the infection as soon as it reached the network. This shows the importance of being able to defend against active ransomware within the enterprise. Organizations cannot rely solely on a single layer of defense to keep threats out. An attacker will always – eventually – breach your environment. Defense therefore needs to change its approach towards detecting and mitigating damage once an adversary is inside.

Many cyber-attacks succeed in bypassing endpoint controls and begin to spread aggressively in corporate environments. Autonomous Response can provide resilience in such cases, even for novel campaigns and new strains of malware.

Thanks to Self-Learning AI, ransomware from the REvil attack could not perform any encryption over the network, and files available on that network were saved. This included the organization’s critical file servers which did not have Kaseya installed and thus did not receive the initial payload via the malicious update directly. By interrupting the attack as it happened, Antigena prevented thousands of files on network shares from being encrypted.

Further observations

Data exfiltration

In contrast to other REvil intrusions Darktrace has caught in the past, no data exfiltration has been observed. This is interesting as it differs from the general trend this last year where cyber-criminal groups generally focus more on the exfiltration of data to hold their victims to ransom, in response to companies becoming better with backups.

Bitcoin

REvil has demanded a total payment of $70 million in Bitcoin. For a group that tries to maximize their profits, this seems odd for two reasons:

  1. How do they expect a single entity to collect $70 million from potentially thousands of affected organizations? They must be aware of the massive logistical challenges behind this, even if they do expect Kaseya to act as a focal point for collecting the money.
  2. Since DarkSide lost access to most of the Colonial Pipeline ransom, ransomware groups have shifted to demanding payments in Monero rather than Bitcoin. Monero appears to be more difficult to track for law enforcement agencies. The fact REvil are using Bitcoin, a more traceable cryptocurrency, appears counter-productive to their usual goal of maximizing profits.

Ransomware-as-a-Service (RaaS)

Darktrace also noticed that other, more traditional ‘big game hunting’ REvil ransomware operations took place over the same weekend. This is not surprising as REvil is running a RaaS model, so it is likely some affiliate groups continued their regular big game hunting attacks while the Kaseya supply chain attack was underway.

Unpredictable is not undefendable

The weekend of the Fourth of July experienced major supply chain attacks against Kaseya and separately, against California-based distributor Synnex. Threats are coming from every direction – leveraging zero-days, social engineering tactics, and other advanced tools.

The case study above demonstrates how self-learning technology detects such attacks and minimizes the damage. It functions as a crucial part of defense-in-depth when other layers – such as endpoint protection, threat intelligence or known signatures and rules – fail to detect unknown threats.

The attack happened in milliseconds, faster than any human security team could react. Autonomous Response has proven invaluable in outpacing this new generation of machine-speed threats. It keeps thousands of organizations safe around the world, around the clock, stopping an attack every second.

Darktrace model detections

  • Compromise / Ransomware / Suspicious SMB Activity
  • Compromise / Ransomware / Suspicious SMB File Extension
  • Compromise / Ransomware / Ransom or Offensive Words Written to SMB
  • Compromise / Ransomware / Ransom or Offensive Words Read from SMB
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Max Heinemeyer
Global Field CISO

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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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About the author
Ashanka Iddya
Senior Director, Product Marketing

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Cloud

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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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