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August 16, 2021

What is Remote Desktop Protocol (RDP)? RDP Attack Analysis

In this case study, Darktrace analyzes how a rapid Remote Desktop Protocol (RDP) attack evolved to lateral movement just seven hours within an exposed server.
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
Oakley Cox
Director of Product
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16
Aug 2021

Late on a Saturday evening, a physical security company in the US was targeted by an attack after cyber-criminals exploited an exposed RDP server. By Sunday, all the organization’s internal services had become unusable. This blog will unpack the attack and the dangers of open RDP ports.

What is RDP?

With the shift to remote working, IT teams have relied on remote access tools to manage corporate devices and keep the show running. Remote Desktop Protocol (RDP) is a Microsoft protocol which enables administrators to access desktop computers. Since it gives the user complete control over the device, it is a valuable entry point for threat actors.

‘RDP shops’ selling credentials on the Dark Web have been around for years. xDedic, one of the most notorious crime forums which once boasted over 80,000 hacked servers for sale, was finally shut down by the FBI and Europol in 2019, five years after it had been founded. Selling RDP access is a booming industry because it provides immediate entry into an organization, removing the need to design a phishing email, develop malware, or manually search for zero-days and open ports. For less than $5, an attacker can purchase direct access to their target organization.

In the months following the COVID-19 outbreak, the number of exposed RDP endpoints increased by 127%. RDP usage surged as companies adapted to teleworking conditions, and it became almost impossible for traditional security tools to distinguish between the daily legitimate application of RDP and its exploitation. This led to a dramatic spike in successful server-side attacks. According to the UK’s National Cyber Security Centre, RDP is now the single most common attack vector used by cyber-criminals – particularly ransomware gangs.

Breakdown of an RDP compromise

Initial intrusion

In this real-world attack, the target organization had around 7,500 devices active, one of which was an Internet-facing server with TCP port 3389 – the default port for RDP – open. In other words, the port was configured to accept network packets.

Darktrace detected a successful incoming RDP connection from a rare external endpoint, which utilized a suspicious authentication cookie. Given that the device was subject to a large volume of external RDP connections, it is likely the attacker brute-forced their way in, though they could have used an exploit or bought credentials off the Dark Web.

As incoming connections on port 3389 to this service were commonplace and expected as part of normal business, the connection was not flagged by any other security tool.

Figure 1: Timeline of the attack — the total dwell time was one day

Internal reconnaissance

Following the initial compromise, the device was seen engaging in network scanning activity within its own subnet to escalate access. After the scan, the device made Windows Management Instrumentation (WMI) connections to multiple devices over DCE-RPC, which triggered multiple Darktrace alerts.

Figure 2: The graph highlights spikes in unusual activity events along with an accompanying large volume of model breaches

Command and control (C2)

The device then made a new RDP connection on a non-standard port, using an administrative authentication cookie to an endpoint which had never been seen on the network. Tor connections were observed after this point, indicating potential C2 communication.

Figure 3: Cyber AI Analyst - Darktrace's AI investigation tool - breaks down the different stages of the incident

Lateral movement

The attacker then attempted lateral movement via SMB service control pipes and PsExec to five devices within the breach device’s subnet, which were likely identified during the network scan.

By using native Windows admin tools (PsExec, WMI, and svcctl) for lateral movement, the attacker managed to ‘live off the land’, evading detection from the rest of the security stack.

Ask the Expert

The organization’s own internal services were unavailable, so they reached out to Darktrace’s 24/7 Ask the Expert service. Darktrace’s cyber experts quickly determined the scope and nature of the compromise using the AI and began the remediation process. As a result, the threat was neutralized before the attacker could achieve their objectives, which may have included crypto-mining, deploying ransomware, or exfiltrating sensitive data.

RDP vulnerability: Dangers of exposed servers

Prior to the events described above, Darktrace had observed incoming connections on RDP and SQL from a large variety of rare external endpoints, suggesting that the server had been probed many times before. When unnecessary services are left open to the Internet, compromise is inevitable – it is simply a matter of time.

This is especially true of RDP. In this case, the attacker managed to successfully carry out reconnaissance and open external communication all through their initial access to the RDP port. Threat actors are always looking for a way in, so what could be considered a compliance issue can easily, and quickly, devolve into compromise.

Out of control remote control

The attack happened out of hours – at a time when the security team were off work enjoying their Saturday evenings – and it progressed at remarkable speed, escalating from initial intrusion to lateral movement in less than seven hours. It is very common for attackers to exploit these human vulnerabilities, moving fast and remaining undetected until the IT team are back at their desks on Monday morning.

It is for this reason that a security solution which does not sleep – and which can detect and autonomously respond to threats around the clock – is critical. Self-Learning AI can keep up with threats which escalate at machine speed, stopping them at every turn.

Thanks to Darktrace analyst Steven Sosa for his insights on the above threat find.

Learn how an RDP attack led to the deployment of ransomware

Darktrace model detections:

  • Compliance / Incoming Remote Desktop
  • Device / Network Scan
  • Device / New or Uncommon WMI Activity
  • Device / Suspicious Network Scan Activity
  • Device / RDP Scan
  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Connection / Outbound RDP to Unusual Port
  • Compliance / Possible Tor Usage
  • Compliance / High Priority Compliance Model Breach
  • Device / New or Unusual Remote Command Execution
  • Anomalous Connection / New or Uncommon Service Control
  • Device / New or Uncommon SMB Named Pipe
  • Device / Multiple Lateral Movement Model Breaches
  • Anomalous Connection / High Volume of New or Uncommon Service Control
  • Compliance / Outbound RDP
  • Anomalous Server Activity / Domain Controller Initiated to Client

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
Oakley Cox
Director of Product

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AI

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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

people working and walking in officeDefault blog imageDefault blog image

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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