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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
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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

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June 1, 2026

Defend What You Trust: Stories from the Front Lines of Modern Cyber Defense

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Modern attacks don’t always announce themselves, follow obvious patterns, or rely on known malware. Often, they move quietly inside trusted systems, authenticated sessions, and everyday behavior.

They don’t break in. They blend in.

That’s why an AI-powered defense is essential. It turns invisible signals into actionable insights at a scale neither analysts nor traditional tools can achieve alone.

Confidence is creating risk

One of the most dangerous assumptions in cybersecurity today is that strong controls equal strong protection.

Multi-factor authentication (MFA), for example, is widely viewed as a foundational safeguard. But as the CISO for a professional sports organization explains, that confidence can be misplaced. “A lot of organizations assume that once you have MFA, those accounts are safe. That’s not true.”

In one instance, his team identified a sophisticated attack where a threat actor bypassed MFA entirely, not by breaking it, but by going around it. A user’s authenticated session was hijacked and re-used, allowing the attacker to impersonate them without triggering traditional controls.

“Darktrace picked up that a session had been re-injected by the hacker, and we were able to block it right away,” he explains.

Attackers anticipate what we miss

Even well-trained users can become entry points.

“An email bypassed our existing security tools,” shares the VP of IT at a U.S.-based risk management services provider.  “The user missed one signal and entered their credentials into a malicious site. That’s what the bad guys count on.”

The organization responded quickly, but not before damage was done. Crucially, this occurred while Darktrace was in “watch mode,” before autonomous response was fully enabled. “Darktrace would have seen that and shut it down immediately,” he notes.

Mistakes and oversights like misconfigurations, forgotten machines, and missed patches can create serious vulnerabilities.

The CIO of a utility services organization shares an instance when Darktrace detected a breach to a client’s network via their ZTNA VPN due to misconfigured MFA. “Darktrace alerted us and autonomously blocked the scanning, preventing what could have been a ransomware-type incident.”  

The most dangerous threats are already inside

The Head of Security at a global business services provider knows firsthand how blind spots can persist inside environments. His team uncovered evidence of dormant ransomware artifacts sitting unnoticed within a company’s environment ¬¬– long before modern detection was in place.

“During a routine file transfer, Darktrace flagged the suspicious activity, identified the ransomware, and immediately quarantined the server,” he recalls.  While the attack was never executed, the implication was significant: the risk existed long before it was finally detected.

Cyber threats are also successful because they take advantage of normal human behavior, exploiting moments of cognitive overload, urgency, and trust.

The Executive Director of IT and Business Applications at a pharmaceutical lab describes the time Darktrace flagged an employee logging into Microsoft 365 from Singapore, despite him being physically located in the U.S. Darktrace immediately cut off his access and within minutes revealed that the employee’s son was using a VPN to play a video game.

While the threat was benign, it demonstrated the strength of AI to use contextual information to detect threats other tools miss. The information also saved security analysts hours of investigation and minimized downtime for the employee. “That level of precision and speed isn’t just convenient, it’s game changing.”

“Unusual” behavior is the new red flag

Detecting modern threats requires an understanding of what “normal” looks like and recognizing when something subtly deviates.

One security leader  at an AI technology enterprise described a scenario in which an employee connected to a proxy service in China. The service itself was legitimate, and although traditional tools didn’t flag it, the behavior was unusual for that user specifically.

“That’s what Darktrace picked up on. The activity turned out to be benign, but without visibility into behavioral deviations, it could just as easily have been something more serious.”

AI shifts defense from reaction to anticipation

These stories point to a fundamental shift by cyber attackers, both tactically and strategically. Because traditional security tools were built to detect what’s already known, modern attacks are often:

  • Credential-based, not malware-based
  • Behavioral, not signature-based
  • Subtle, not overt

They may operate within the boundaries of what appears normal, exploiting what organizations trust, not what they block:

  • Trusted sessions
  • Legitimate services
  • Human error

This is where AI is changing the equation. Rather than relying on predefined rules or known threat signatures, AI can:

  • Establish a baseline of normal behavior
  • Detect subtle anomalies in real time
  • Act autonomously to contain potential threats

Resilience, not perfection, is the new security standard

As these frontline experiences show, the organizations that lead are those that move beyond reactive defense and embrace AI as a core part of their strategy.

It eliminates the blind spots and uncertainty, says the CISO of a professional sports organization. “If you lack visibility, you’re not managing risk, you’re assuming it. AI gives you the actionable insights needed to turn uncertainty into control.”

And it provides the speed and agility that are vital when seconds matter, says the Executive Director of IT and Business Applications. “When Darktrace alerted us at 3:00 am to a ransomware attack, it had already quarantined the affected systems, blocked the attacker’s access, and provided us with the critical details and time needed to investigate. That action likely saved us hundreds of thousands, if not millions, of dollars.”

The modern SOC has become a cornerstone of enterprise resilience, responsible for protecting data and operational continuity while enabling digital growth and innovation. For today’s security professional, that means success is no longer measured by what they keep out, but by what they protect: revenue, reputation, and trust.

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May 28, 2026

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

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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.

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About the author
Oakley Cox
Director of Product
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