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June 24, 2020

Ekans Ransomware: Insights on OT Cyber Attacks

Uncover the impacts of the Ekans ransomware attack on operational technology and what organizations can do to enhance their cybersecurity posture.
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
David Masson
VP, Field CISO
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24
Jun 2020

In recent weeks, the security industry has become acutely aware of the challenges surrounding OT protection, with the EKANS ransomware attacks on Honda and the Enel Group demonstrating how novel threats continue to slip through the cracks of security systems in ICS environments. What’s more, with such attacks resulting in loss of productivity and damage to critical infrastructure, the need for a cyber security strategy that bridges both OT and IT technology is increasingly urgent.

The recent EKANS ransomware has been making waves in security circles because of its ability to target 64 specific ICS mechanisms in its ‘kill chain’. Standard attacks target ICS environments through vulnerabilities in IT infrastructure, pivoting through unpatched software to reach OT machinery, rather than heading straight for the jugular. The EKANS ransomware targeted ICS vulnerabilities directly and can be considered the first of its kind – marking a significant evolution in attacker techniques. Before now, ICS machinery-specific ransomware had either been an academic theory or a marketing tool.

Technical analysis

Written in the Go programming language, EKANS has additional obfuscation abilities compared to other ransomware strains, which enable it to better evade detection. As will be seen in this analysis, the power of EKANS ransomware is two-fold – it is able to disguise its attack in the beginning stages, and when it does strike, it is targeted at industrial pain points.

The ransomware’s first port of call is to check if the victim has already been encrypted. If not, standard encryption library functions ensue. These involve both the execution of encryption operations and the deletion of Volume Shadow Copy back-ups – meaning the victim cannot simply retrieve duplicated data copies and circumvent the ransom.

Before the relevant files are encrypted, EKANS ransomware kills various ICS processes listed in a pre-programmed, hard-coded list. The affected applications include GE’s Proficy data historian, GE Fanuc automation software, FLEXNet licensing server instance, Thingworx monitoring and management software, and Honeywell’s HMIWeb application – all specific to ICS environments.

proficyclient.exe
vmacthlp.exe
msdtssrvr.exe
sqlservr.exe
msmdsrv.exe
reportingservicesservice.exe
dsmcsvc.exe
winvnc4.exe
client.exe
collwrap.exe
bluestripecollector.exe

Figure 1: A small excerpt of the ICS-related processes targeted in the EKANS ‘kill list’

While stalling these processes doesn’t necessarily bring industrial plants crashing to a halt, it does reduce visibility and potentially make machine operations unpredictable. In the case of Honda’s attack, manufacturing operations across the US, the UK, and Turkey were suspended. With a workforce of 220,000 people worldwide, shutting down several factories and sending employees home results in a dramatic loss of production hours and employee salaries – not to mention the costs of getting systems up and running without giving in to ransom demands.

EKANS then goes one stage further. Once this initial kill chain has been executed, the ransomware starts encrypting data. Five randomly generated letters are added at the end of each original file extension. This in itself is unusual, as most ransomware encrypts data with a specific key.

Figure 2: Encryption results of EKANS ransomware

Rather than targeting specific devices or systems, EKANS ransomware looks to take down the entire network, which is part of what makes it such an aggressive style of ransomware. However, it lacks a self-propagating mechanism, so it has to be manually introduced to ICS environments. Malicious payloads hidden in links and attachments within emails are the primary mechanism used to introduce the ransomware. From there, EKANS exploits vulnerable and unpatched services, seeding itself across the entire business via script.

When the encryption process has been completed, a ransom note is displayed, requesting a covert financial exchange for a decryption key over the encrypted email platform CTemplar. In the case of both Honda and the Enel Group, they were told to contact CarrolBidell@tutanota[.]com for further information. The attackers also offered to send several decrypted files to prove the legitimacy of the encryption key.

| What happened to your files?
--------------------------------------------
We breached your corporate network and encrypted the data on your computers. The encrypted data includes documents, databases, photos and more –
all were encrypted using a military grade encryption algorithms (AES-256 and RSA-2048). You cannot access those files right now. But dont worry!
You can still get those files back and be up and running again in no time.
--------------------------------------------
| How to contact us to get your files back?
--------------------------------------------
The only way to restore your files is by purchasing a decryption tool loaded with a private key we created specifically for your network.
Once run on an effected computer, the tool will decrypt all encrypted files – and you resume day-to-day operations, preferably with
better cyber security in mind. If you are interested in purchasing the decryption tool contact us at %s
--------------------------------------------
| How can you be certain we have the decryption tool?
--------------------------------------------
In your mail to us attach up to 3 files (up to 3MB, no databases or spreadsheets).

Figure 3: Partial view of EKANS ransomware note

Honda has refrained from stating what specific plant capabilities were affected by the EKANS attack, however it has publicly affirmed that production operations have been affected in multiple factories across the world. Their visibility and control systems were disrupted significantly enough to suspend manufacturing.

Becoming immune to ransomware

While the EKANS ransomware leverages fairly crude techniques and is only able to halt processes rather than control ICS mechanisms, it represents a new frontier in OT cyber-attacks. ICS offensives will continue to evolve – with greater control over machinery a likely avenue of exploration for cyber-criminals.

What is clear from the Honda attack is that even some of the world’s largest global conglomerates are susceptible to these kind of ransomware attacks. What is needed to protect factory floors from such attacks is a cyber security solution that can detect the most subtle signals of threat, learning on the job to understand what is ‘normal’ for each unique ICS environment.

Darktrace’s AI learns the normal ‘patterns of life’ for every user, device, and controller across both OT and IT. By continuously analyzing data across organizations’ systems, the AI’s unique understanding of how each facet of a business and a dynamic workforce interacts ensures that any malicious activity is detected seconds after it emerges. In the case of EKANS, this self-learning approach would have identified a number of anomalous behaviors pertaining to the originally infected device, including beaconing to a rare destination and the unusual connections to encryption software.

Complementing Darktrace’s threat detection is the AI’s Autonomous Response abilities, which neutralize threats with surgical precision – allowing business activity to continue as normal. Autonomous Response has already proven itself successful in stopping ransomware attacks, preventing damaging operational outages at manufacturing facilities, hospitals, and municipalities around the world.

Conclusion

EKANS revealed that attackers are beginning to successfully target both IT and OT systems with one attack, making the need for security programs that can bridge this gap more urgent than ever. The ability to defend both environments with a single security solution ensures holistic protection for the entire organization. By correlating disparate data points across SaaS, email, cloud, traditional network, and OT environments, Cyber AI can identify and stop even the most sophisticated attacks.

The reality is that threats in the OT sphere will continue to evolve, becoming faster and more furious than ever. Given the potential damage ransomware can cause, security that can defend industrial systems along with dynamic workforces – detecting and stopping fast-acting threats across a complex business – has become more important than ever. The functionality of industrial systems depends on it.

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
David Masson
VP, Field CISO

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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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May 28, 2026

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

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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.

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