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May 18, 2021

The Dangers of Double Extortion Ransomware Attacks

Learn about the latest trend in ransomware attacks known as double extortion. Discover how Darktrace can help protect your organization from this threat.
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager
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18
May 2021

A year and a half ago, ‘double extortion’ ransomware was being used by only one known threat actor. Now, over 16 ransomware groups actively utilize this tactic. So, what is it, and why has it become so popular?

What is double extortion ransomware?

The traditional story of ransomware was one of malicious code rapidly encrypting files with public-key RSA encryption, and then deleting those files if the victim did not pay the ransom.

However, after the infamous WannaCry and NotPetya ransomware campaigns over 2017, companies ramped up their cyber defense. More emphasis was placed on backups and restoration processes, so that even if files were destroyed, organizations had copies in place and could easily restore their data.

Yet in turn, cyber-criminals have also adapted their techniques. Now, rather than just encrypting files, double extortion ransomware exfiltrates the data first. This means that if the company refuses to pay up, information can be leaked online or sold to the highest bidder. Suddenly, all those backups and data recovery plans became worthless.

Maze ransomware and friends

In late 2019, Maze ransomware emerged as the first high-profile case of double extortion. Other strains soon followed, with the Sodinokibi attack — which crippled foreign exchange company Travelex — occurring on the final day of that year.

By mid-2020, hundreds of organizations were falling victim to double extortion attacks, various websites on the dark net were leaking company data, and the Ransomware-as-a-Service business was booming as developers sold and rented new types of malware.

Furthermore, cyber security regulations started being weaponized by cyber-criminals who could leverage the threat of having to pay a hefty compliance fine (CCPA, GDPR, NYSDFS regulations) to encourage their victims to keep quiet by offering them a ransom smaller than the penalty fee.

There were 1,200 double extortion ransomware incidents in 2020, across 63 countries, with over 60% of these aimed at the US and the UK.

Despite new legislation being written regularly to try and mitigate these attacks, they aren’t slowing down. According to a recent study by RUSI, there were 1,200 double extortion ransomware incidents in 2020 alone, across 63 different countries. 60% of these were aimed at organizations headquartered in the US, and the UK suffered the second highest number of breaches.

Last month, the cyber-criminal gang known as REvil released details about Apple’s new Macbook Pro on their site ‘Happy Blog’, threatening to release more blueprints and demanding a ransom of $50 million. And last week, Colonial Pipeline purportedly paid $5 million in bitcoin to recover from a devastating OT ransomware attack.

Anatomy of a double extortion ransomware attack

Darktrace has detected a huge upsurge in double extortion ransomware threats in the last year, most recently at an energy company based in Canada. The hackers had clearly done their homework, tailoring the attack to the company and moving quickly and stealthily once inside. Below is a timeline of this real-world incident, which was mostly carried out in the space of 24 hours.

Figure 1: A timeline of the attack

Darktrace detected every stage of the intrusion and notified the security team with high-priority alerts. If Darktrace Antigena had been active in the environment, the compromised server would have been isolated as soon as it began to behave anomalously, preventing the infection from spreading.

Encryption and exfiltration

The initial infection vector is not known, but the admin account was compromised most likely from a phishing link or a vulnerability exploit. This is indicative of a trend away from the widespread ‘spray and pray’ ransomware campaigns of the last decade, towards a more targeted approach.

Cyber AI identified an internal server engaging in unusual network scanning and attempted lateral movement using the Remote Desktop Protocol (RDP). Compromised admin credentials were used to spread rapidly from the server to another internal device, ‘serverps’.

The device ‘serverps’ initiated an outbound connection to TeamViewer, a legitimate file storage service, which was active for nearly 21 hours. This connection was used for remote control of the device and to facilitate the further stages of attack. Although TeamViewer was not in wide operation in the company’s digital environment, it was not blocked by any of the legacy defenses.

The device then connected to an internal file server and downloaded 1.95 TB of data, and uploaded the same volume of data to pcloud[.]com. This exfiltration took place during work hours to blend in with regular admin activity.

The device was also seen downloading Rclone software – an open source tool, which was likely applied to sync data automatically to the legitimate file storage service pCloud.

The compromised admin credential allowed the threat actor to move laterally during this time. Following the completion of the data exfiltration, the device ‘serverps’ finally began encrypting files on 12 devices with the extension *.06d79000.

As with the majority of ransomware incidents, the encryption happened outside of office hours – overnight in local time – to minimize the chance of the security team responding quickly.

AI-powered investigation

Cyber AI Analyst reported on four incidents related to the attack, highlighting the suspicious behavior to the security team and providing a report on the affected devices for immediate remediation. Such concise reporting allowed the security team to quickly identify the scope of the infection and respond accordingly.

Figure 2: Cyber AI Analyst incident tray for a week

Cyber AI Analyst investigates on demand

Following further analysis on March 13, the security team employed Cyber AI Analyst to conduct on-demand investigations into the compromised admin credential in Microsoft 365, as well as another device which was identified as a potential threat.

Cyber AI Analyst created an incident for this other device, which resulted in the identification of unusual port scanning during the time period of infection. The device was promptly removed from the network.

Figure 3: Cyber AI Analyst incident for a compromised device, detailing an unusual internal download

Double trouble

The use of legitimate tools and ‘Living off the Land’ techniques (using RDP and a compromised admin credential) allowed the threat actors to carry out the bulk of the attack in less than 24 hours. By exploiting TeamViewer as a legitimate file storage solution for the data exfiltration, as opposed to relying on a known ‘bad’ or recently registered domain, the hackers easily circumvented all the existing signature-based defenses.

If Darktrace had not detected this intrusion and immediately alerted the security team, the attack could have resulted not only in a ‘denial of business’ with employees locked out of their files, but also in sensitive data loss. The AI went a step further in saving the team vital time with automatic investigation and on-demand reporting.

There is so much more to lose from double extortion ransomware. Exfiltration provides another layer of risk, leading to compromised intellectual property, reputational damage, and compliance fines. Once a threat group has your data, they might easily ask for more payments down the line. It is important therefore to defend against these attacks before they happen, proactively implementing cyber security measures that can detect and autonomously respond to threats as soon as they emerge.

Learn more about double extortion ransomware.

Darktrace model detections:

  • Device / Suspicious Network Scan Activity
  • Device / RDP Scan
  • Device / Network Scan
  • Anomalous Connection / Unusual Admin SMB Session
  • Anomalous Connection / Unusual Admin RDP Session
  • Device / Multiple Lateral Movement Model Breaches
  • User / New Admin Credentials on Client
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Low and Slow Exfiltration
  • Device / Anomalous SMB Followed By Multiple Model
  • Anomalous Connection / Download and Upload
  • Anomalous Connection / Suspicious Activity On High Risk Device
  • Anomalous File / Internal::Additional Extension Appended to SMB File
  • Compromise / Ransomware::Suspicious SMB Activity
  • Anomalous Connection / Sustained MIME Type Conversion
  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Connection / Suspicious Read Write Ratio
  • Device / Large Number of Model Breaches
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager

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May 21, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

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Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

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Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

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About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response

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May 21, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

Sign up today to stay informed about innovations across securing AI.

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About the author
Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
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