ブログ
/
/
August 6, 2020

Ransomware-As-A-Service Threat: Eking Targets Government

Discover how Eking ransomware targeted a government organization in APAC. Learn about ransomware as a service & the cyber AI technology that stopped the 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
Max Heinemeyer
Global Field CISO
Default blog image
06
Aug 2020

Despite being widely recognized as a serious threat for a number of years, ransomware continues to persist. The total global cost of this threat vector is projected to reach $20 billion by 2021. With this level of financial return for attackers, it is no wonder that they continue to develop new strains of ransomware and advance their techniques to bypass security tools and ensure their campaigns are successful.

In the last few weeks, Darktrace’s AI has detected an attacker abusing off-the-shelf products to deploy ransomware at an African retailer, along with high-profile WastedLocker and Emotet attacks. Here, we look at Eking ransomware – a variant of the Phobos ransomware family – that targeted a government organization in the APAC region.

This attack was likely an example of Ransomware-as-a-Service (RaaS); a particularly concerning threat for security teams as it allows lower-level actors to get hold of sophisticated malware. This blog post breaks down Eking ransomware in detail, showing how Cyber AI enabled the defenders to recognize the anomalous behavior as soon as it occurred and stop the threat from advancing – and causing damage. It also shows how Darktrace’s Cyber AI Analyst autonomously investigated the broader security incident, generating an easy-to-understand and actionable report as the activity unfolded.

An overview of the attack

An internal server was infected with Eking ransomware via an attack vector outside of Darktrace’s visibility, most likely an employee clicking a malicious link within an email. Antigena Email would likely have identified suspicious characteristics of the email and stopped it from reaching employees’ inboxes, preventing the threat at the first hurdle. However, in this instance, the customer had only deployed Cyber AI across their network. This still enabled Darktrace’s Immune System to identify lateral movement and encryption activity indicative of ransomware.

The infected device began engaging in internal reconnaissance activity on a single internal subnet. This included SMB enumeration via the SRVSVC and winreg pipes, as well as extensive scanning over 10 commonly exploited ports. Indicators of Nmap were also detected during this phase of the attack.

About four and a half hours after this scanning concluded, the infected server began encrypting files on a second server. The device transitioned from making just a few internal connections per day to making thousands in less than an hour. This dramatic shift in behavior was immediately detected by Darktrace’s AI as highly threatening and the Cyber AI Analyst began autonomously investigating.

Figure 1: An overview of events

Internal reconnaissance and encryption – sometimes referred to as detonation – took place late at night local time. This may have been strategic on the part of the attackers, as the number of security professionals actively monitoring the network was probably lower, slowing the speed of the organization’s response. Endpoint defenses did not prevent the threat – likely indicating that this was a slightly modified strain of the Eking ransomware that was able to bypass these signature-based tools.

While Darktrace provides complete coverage across email, IoT, and cloud environments, business challenges or segmentation sometimes prevent security teams from obtaining full visibility across their organization. However, even when working with imperfect data and suboptimal coverage, Cyber AI still identified this threat as it emerged.

AI Analyst coverage

When the first model breach occurred, this triggered Darktrace’s Cyber AI Analyst to launch a real-time investigation into the events as they unfolded. Piecing together the lateral movement and the later encryption, the technology recognized that these separate events were part of a wider security narrative. It surfaced an incident summary and several key metrics for the security team to review and action a response.

Figure 2: Internal reconnaissance of the subnet over a number of sensitive ports

Figure 3: Encryption phase of the attack

Figure 4: A graph of connections and unusual activity demonstrating how significant of a deviation this activity was from normal device behavior

Off the shelf: The commercialization of cyber-crime

This incident demonstrates how the rise in Ransomware-as-a-Service is allowing lower-level threat actors to access sophisticated strains of ransomware as well as novel variants of well-known attacks. The cyber-crime market is estimated to be worth $1.6 billion, and this figure is only likely to rise as the relatively new ‘industry’ matures. As a result, the potential perpetrators of advanced cyber-attacks like the one detailed above are no longer confined to professional cyber-criminal rings, who have outsourced their tactics, techniques and procedures to a wider range of threat actors willing to pay the right price. As lower-level threat actors get access, more organizations will find themselves targeted by increasingly sophisticated threats.

Just this week, Darktrace observed a high-profile example of RaaS in a Sodinokibi ransomware attack that hit a retail organization in the US. The infected device engaged in anomalous administrative activities before writing an unusual executable file, sharing it with other internal locations and then encrypting multiple files on the network and writing its own ransom note files.

With ransomware attacks continuing to target organizations large and small, security teams are fundamentally changing their approach to cyber defense, turning to artificial intelligence to stop attacks that other tools miss. Without relying on pre-defined rules and signatures, Cyber AI learns a sense of ‘self’ for a unique organization to detect and respond to anomalous activity as soon as it occurs.

Fight back with Autonomous Response

Threat actors know that deploying ransomware at weekends or at night is more likely to succeed because an organization’s response time is typically slower. Darktrace’s Autonomous Response operates around the clock, taking a targeted and proportionate response to contain malicious activity wherever it occurs, whether in the network, email, or in cloud and SaaS applications.

Had Darktrace Antigena been deployed at this government in APAC, it would have taken action at the first stage of the attack – as the initial scanning took place – and prevented the malware from ever reaching the encryption stage. However, in this case, when the security team returned to the office the next morning, they were still able to act faster than they otherwise would have and limit the damage, thanks to the fully-investigated incident and actionable intelligence of the Cyber AI Analyst’s AI-powered investigations.

Thanks to Darktrace analyst Brian Evans for his insights on the above threat find.

Learn more about Autonomous Response

IoCs:

IoCComment.ekingEking encryption extension

Darktrace model detections:

  • Device / ICMP Address Scan
  • Unusual Activity / Unusual Internal Connections
  • Device / Network Scan - Low Anomaly Score
  • Device / Network Scan
  • Anomalous Connection / Unusual Internal Remote Desktop
  • Device / RDP Scan
  • Device / Suspicious Network Scan Activity
  • Anomalous Connection / SMB Enumeration
  • Anomalous Connection / Unusual Admin RDP Session
  • Device / Multiple Lateral Movement Model Breaches
  • Compromise / Ransomware / Suspicious SMB Activity
  • Compromise / Ransomware / Ransom or Offensive Words Written to SMB
  • Anomalous File / Internal / Additional Extension Appended to SMB File

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

More in this series

No items found.

Blog

/

AI

/

May 28, 2026

From Efficiency to Exposure: How AI Adoption Is Creating Unseen Vulnerabilities on the Factory Floor

Default blog imageDefault blog image

How AI agents impact the manufacturing industry

Security teams and IT personnel across the manufacturing industry are under constant pressure to protect production, maintain uptime, and safeguard critical assets but the rise of AI is bringing huge new opportunities alongside new cyber risks. Across manufacturing, AI is embedded into workflows, decision-making, and increasingly, autonomous AI agents are acting on behalf of employees and systems.  

Agentic systems are powerful because they can act independently, but that same autonomy also creates cyber and operational risk. Agents have extensive permissions and are capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with little to no human intervention.

Unlike traditional AI models that perform predefined tasks, AI agents use advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges, making decision and taking action based on their own judgement. They look like employees operationally but lack judgment, ethics, or fear of consequences like humans do. This means they can be easily manipulated by cybercriminals, and an AI agent embedded across an OT network creates threats that extend well beyond data exposure. For example, at BMW, AI identifies faults in welding processes as they occur. At its Spartanburg plant, AI monitors the weld of 300-400 metal studs onto every SUV frame to detect misplaced or faulty studs and correct them instantly. Corruption of BMW’s AI system could lead to catastrophic quality control errors.

Adopting agentic AI systems across manufacturing raises some concerns across security teams. New data from our State of AI Cybersecurity survey shows that 78% of manufacturing security professionals are worried about employee use of AI agents – their top concern. That’s followed by employee use of generative AI tools like CoPilot and ChatGPT, a worry for 76% of security professionals at manufacturing organizations. As these tools gain more access to business data and processes, and more autonomy within organizations, security teams, who today have minimal visibility of agent activity in their environments, increasingly have sensitive data exposure (a worry for 60%) and accidental policy and regulatory violations (59%) on their minds.

External AI-powered threats are evolving just as quickly

The same capabilities transforming manufacturing are also reshaping cyberattacks.

AI is enabling attackers to automate reconnaissance, refine targeting, and adapt in real time. What once required time and manual effort can now be executed continuously and at scale. Manufacturers are already seeing the impact. According to manufacturing security professionals we surveyed, 76% are already being impacted by AI-powered threats and 90% see AI increasing the success of social engineering attacks.

And the techniques themselves are evolving. Concerns across the manufacturing sector show growing anxiety about the range of AI-powered attack routes, most pressingly of adaptive malware that evolves in real-time – a prospect half (49%) of manufacturing security professionals we surveyed are worried by, a full 9% more than the average across industries. AI adaptive malware is followed by:

  • Automated vulnerability scanning and exploit chaining (48%) which has become even more pressing as Anthropic’s new Mythos AI Model supercharges vulnerability discovery
  • Hyper-personalized phishing campaigns (46%), which remain a mainstay in hackers’ arsenals, and AI has amplified their effectiveness by making phishing emails more convincing and harder to detect.

This is not just an increase in volume, it is a shift toward threats that evolve as they unfold - often faster than static defenses can respond.

Despite rising awareness, many manufacturers are not yet equipped to manage this shift. More than half (51%) say they are not adequately prepared for AI-driven threats, and only 37% have formal policies governing AI deployment.  

Securing AI through visibility, context, and guardrails

Addressing this challenge does not require manufacturers to slow innovation. It requires a different approach to security, one that can operate at the same speed and scale as AI. Three specific priorities are emerging for manufacturers looking to take advantage of the power of AI.

Visibility is foundational.  

Organizations need to understand where AI is being used, what it can access, and how it behaves across both IT and OT environments. Without that, risk cannot be measured or managed. It is no surprise that Darktrace’s research found that 91% of manufacturing security professionals said that they need to understand how AI makes decisions before trusting it. This is even more critical in operational settings where disruption has safety, environmental, financial, and reputational impacts.

Context is what turns visibility into action.  

In environments shaped by AI, normal behavior is constantly shifting. Detecting threats requires a behavioral approach; understanding patterns of life across the organization and identifying subtle deviations in real time – a step change in organizations’ traditional approach to security and risk management.

Guardrails ensure that agency does not become exposure  

As AI systems take on greater responsibility, organizations need clear boundaries around what they can do and when they can act independently. These controls must be embedded into systems themselves, not applied after the fact.  

Securing AI Agents Across Manufacturing IT and OT

The rise of agentic AI is transforming manufacturing - powering next-generation operations while reshaping the security landscape. This is not just an increase in threats, but a shift to autonomous systems, continuously evolving behaviors, and risks moving at machine speed. For organizations trying to grapple with the challenge of enabling AI while managing the risk, visibility, context and guardrails should be foundational.

Darktrace helps manufacturers build secure AI approaches by making those foundations possible. It provides visibility and real-time detection and response to unusual activity across IT and OT environments and allows organizations to understand AI activity from the prompts employees use and the agents they build to how those agents are behaving across the environment. For manufacturers scaling AI, this delivers a foundation for innovation without sacrificing control.

Continue reading
About the author
Oakley Cox
Director of Product

Blog

/

AI

/

May 28, 2026

How to Evaluate AI Vendors: 5 Key categories for AI Adoption

Default blog imageDefault blog image

Understanding the AI buyers’ market

AI adoption has become a central topic of discussion in boardrooms, drawing growing interest from business leaders. Ultimately, organizations hope that an investment in AI technology will have tremendous returns. However, the process of buying an AI solution is not as straight forward as it appears on the surface.  

While business leaders may be eager to improve productivity across their operations, practitioners responsible for evaluating and selecting AI solutions may not always have the visibility or technical understanding needed to make the right decisions for their business. What is typically marketed as a holistic solution to their most critical problems is usually followed by uncertainty when AI tools are finally operationalized in real environments.

This guide is intended to support security leaders who are under growing pressure to adopt AI tools while navigating complex terminology, vendor claims, and increasingly crowded buying cycles. Ultimately, the goal is to help organizations evaluate and adopt AI in a safe, effective, and well-governed way. To support this, we’ve structured the evaluation framework across five key categories:

  1. Governance, safety, and data controls
  1. Data gathering and training
  1. Model and technique choice
  1. Performance and accuracy validation    
  1. Interpretability, adjustability, and transparency    

What buying AI looks like in cybersecurity

While investing in AI can bring immense benefits to your security team, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.  

With acceleration in AI adoption, accompanied by the recent boom in agentic AI and autonomous agents, CISOs must look “beneath the hood" of these tools to understand how they work, how they are governed, and to ensure the system is secure and compliant with internal policies.

Challenges in the AI buyers’ marketplace  

The AI security software market is buzzing with hype and flashy promises, which, understandably, needs to be addressed with due diligence. Potential buyers, especially in the cybersecurity space, are hesitant when it comes to allowing AI autonomous capabilities across their workflows, and a lack of vendor transparency can exacerbate those feelings.  

Reinforcing this sentiment, research from this year's Darktrace’s State of AI Cybersecurity report shows where confidence and hesitancy emerge amongst potential buyers. On the one hand, security professionals agree that they have good visibility into the logic and reasoning processes their AI solutions use. However, they lack the explainability and trust to allow AI to take independent remedial action.

  • 89% say they have good visibility into the reasoning behind the outputs generated by AI solutions
  • 92% say they need to understand how a defensive AI tool makes decisions before they can trust it
  • Only 14% say they allow AI to act independently, performing autonomous actions without human approval
  • 74% say they are limiting the autonomy of AI taking action in their SOC until explainability improves

Given the desire for trust and explainability we are seeing from buyers, it's important for them to be equipped with the right questions to ask vendors during an assessment or POV of AI tools in order to demystify marketing hype from real operational outcomes.

Below is a list of categories in which buyers can assess AI vendors or AI Service Providers (AISPs) to help reach safe adoption and maximize their ROI.  

5 categories of AI vendor assessment

Darktrace groups these AI-related questions into 5 categories: governance, data and training, model and technique choice, performance validation, and interpretability and adjustability. By asking questions regarding each of these 5 categories, buyers can gain a deeper understanding of how an AISP’s systems work and whether they suit their business requirements.

Governance, safety, and data controls

Governance of AI systems is critical for all AISPs. Whether their platform is based around a single model, or is a more complex, composite AI solution, strong governance is essential to ensure the system is safe, robust, and reliable.

A simple question you could ask is:

What AI governance policies and frameworks do you follow, and/or certifications do you currently maintain?

For more questions you can ask vendors, download the full guide here.

Darktrace is certified to the ISO/IEC 42001 standard, the world’s first AI Management System (AIMS) standard. ISO/IEC 42001 addresses the unique ethical and technical challenges AI poses by setting out a structured way to manage risks such as transparency, accuracy, and misuse. This includes a commitment to ethical AI development, and effective management and monitoring of AI systems both prior to and continually after release.

Data gathering and training

Accurate, meaningful, and unbiased data gathering is the first important step in producing any AI system. An AI model trained using inaccurate, unbalanced, or poor-quality training data will fail to perform optimally.

To alleviate concerns regarding training data quality, a question you could ask is:

What steps do you take to prevent bias in your AI models and training data?

For more questions, download the full guide here.

AISPs should be able to provide information about the steps taken, workflows followed, and auditing performed to reduce AI bias where appropriate. While it’s sometimes impossible to fully remove bias from an AI model, appropriate actions should be taken to mitigate or reduce bias where relevant.

Model and technique choice

Different AI techniques are optimal for different tasks. For example, research from Gartner suggests that relying on a single “one-size-fits-all" model can lead to data gaps, especially in highly specialized domains.

To achieve more accurate and robust AI solutions, AI leaders should move beyond using just one model or technique, embrace composite AI practices, and adopt a holistic AI system perspective.

A straightforward question you could ask is simply:

What type(s) of AI model(s) do you utilize in your solution?

For more questions, download the full guide here.

While specific detailed information about custom systems used by AISPs is likely proprietary, buyers should expect vendors to be able to provide an overview of the broad techniques used. This will allow you as a buyer to determine if the type of model is appropriate for your use case.

Performance and accuracy validation  

Testing and evaluation of performance is essential for all AI systems. Performance analysis should be performed both before release and continually after release to identify potential data or model drift.  

A question you could ask to understand an AISPs testing workflow is:

How do you audit, test, evaluate, verify, and validate your AI model outputs?

For more questions, download the full guide here.

Testing workflows will likely vary depending on the type of model – measurements relevant to one system may not always be relevant to others. Assessment of systems should also extend beyond these standard accuracy and robustness tests, and should also feature physical performance, such as latency and resource consumption.  

Interpretability, adjustability, and transparency  

AI systems are typically a black box, simply providing an output without an explanation of how that output was attained. Interpretability and transparency are critical to ensure that both SOC teams and end-users trust the outputs of a system to be accurate and meaningful.

A question you could ask is:

How do you promote a trust relationship between human analysts and AI outputs?

For more questions, download the full guide here.

In the context of cybersecurity, trust and interpretability are even more essential. This is particularly relevant for generative AI-based systems (including most AI Agents), where the risk of hallucination can reduce trust in responses.

Cybersecurity systems often need to perform autonomous actions to block incoming threats – an email filtering system may hold potentially dangerous emails; a firewall may block malicious inbound connections. If SOC teams can’t trust these systems to perform accurately, these systems may be limited or disabled, critically reducing their defensive power.

Darktrace as an AI-native cybersecurity vendor

Darktrace has been building and applying AI in cybersecurity for over a decade, developing its capabilities alongside an increasingly complex and fast‑moving threat landscape. This experience has resulted in a mature, multi-layered approach to AI, which continuously learns the normal patterns of each organization to understand behavior, interpret context, and identify meaningful deviations — without relying on predefined rules or known attack signatures. Over time, this has enabled a proven behavioral understanding that helps uncover subtle signals of risk that may otherwise be missed.

With the backing of our ISO/IEC 42001 certification, stakeholders, customers, and partners can be confident that Darktrace is responsibly, ethically, and safely developing its AI systems, and managing the use of AI in day-to-day operations in a compliant and secure manner.  

Explore the principles behind Darktrace’s responsible AI approach, informed by collaboration with global experts in academia and governments, detailing how accountability, explainability, and continuous validation are built into its cybersecurity technology.

How Darktrace secures AI systems

Darktrace now brings these capabilities to monitor and respond to risk generated from AI systems across organizations with Darktrace / SECURE AI. This solution analyzes how prompts, agents, and systems are used within the context of each organization, bringing every AI interaction into a single view. This unique approach helps teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI agent activity.

Stay up to date

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

Further Reading on AI in cybersecurity

When deciding to invest in an AI solution, it’s important to understand what this means for you and your organization. The questions presented here are only a starting point in understanding an AI solution and whether it is appropriate for your use case.  

Gain deeper knowledge on applications of AI in cybersecurity and Darktrace’s multi-layered AI in the AI Arsenal White Paper.

[related-resource]

Continue reading
About the author
Jamie Bali
Technical Author (AI) Developer
あなたのデータ × DarktraceのAI
唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ