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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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July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

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Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician

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July 1, 2026

5 Ways AI is changing traditional security models according to modern CISOs

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The Reality of Securing AI in Motion

Traditional security tools were built for environments defined by fixed rules and predictable workflows. But AI behavior is non-deterministic. The same prompt can produce different outcomes, and risk often emerges gradually as AI behavior adapts, and permissions drift over time. This creates a constantly shifting environment where security teams are working to define control in a system that resists stability. “In AI security, yesterday's priorities can become tomorrow's blind spots. The landscape shifts that fast,” warned the SVP and Head of Technology and Cybersecurity of a real estate investment trust. Conventional approaches, which rely on establishing and maintaining a steady baseline, struggle to keep up with that level of change.

At the same time, AI adoption is accelerating across organizations, often faster than security teams can implement the controls needed to manage it. “The car is being built while it’s already on the road,” explained the CISO of a global private fund administrator. “The threats we're securing against today won't be the threats we're facing tomorrow. What kept us up three months ago looks nothing like what we're dealing with today.”

As businesses move quickly to unlock value from AI, security teams are left closing gaps in real time, while also facing adversaries who are using AI to make their attacks more scalable, adaptive, and difficult to detect. In this recent roundtable discussion of CISOs and security leaders, five themes emerged around AI cyber risk.  

1. AI agents with human access but no human judgment

In Darktrace’s 2026 State of AI Cybersecurity report, 96% of the surveyed security professionals agree that AI significantly improves the speed and efficiency with which they work. Yet, 92% admitted that they’re concerned with the security implications of the use of AI agents across their workforce.

AI agents now operate with human-level permissions across systems, acting at machine speed, orchestrating actions across platforms, and making decisions without the judgment or caution a person would apply. Unlike human users, they cannot be expected to pause and question whether a given action is appropriate.

Their identities are also difficult to inventory, govern, and audit. As agents become easier to deploy than legacy IT systems ever were, organizations are quickly losing track of what is running, what it has access to, and what it is doing. This creates a growing class of highly privileged, autonomous actors operating without the visibility or oversight that traditional identity and access controls were designed to provide.“While AI adoption is critical to running a modern business, AI alone can’t solve all our cybersecurity challenges,” said a global financial sector CISO. “We still need think critically and use human judgement. Those are two things AI can’t do.”

This lack of human judgment becomes especially risky as new architectures, such as Model Context Protocol (MCP), can expand how agents connect to data, tools, and external systems. By design, MCP enables agents to dynamically discover and interact with new resources, increasing flexibility but also introducing new pathways for unintended access, data exposure, or abuse if not properly governed.

The CISO of a fund administrator highlighted one emerging vector as an example: rogue MCP servers. “Our developers want to move quickly and bring value to the business, but technologies like these can unintentionally expose sensitive data in ways that would never have happened before.”

2. Increased digital complexity and expanded attack surface

AI activity rarely stays contained. A single prompt can trigger a chain of actions across networks, email, cloud infrastructure, SaaS platforms, endpoints, identity systems, and development environments, spanning systems that were never designed to be secured as a single, connected flow. This expands both the scale and complexity of what security teams need to monitor and defend.

Yet no single control has visibility across that entire chain. “You can’t defend effectively what you can’t see,” cautioned the private fund administrator CISO. As AI-driven activity moves fluidly across environments, gaps in coverage become inevitable, creating blind spots that attackers can exploit.

Threat actors are already capitalizing on this lack of visibility. “Threat actors have advanced their use of generative AI to launch more convincing phishing campaigns, automate social engineering, and scale attacks with greater precision down to the individual level,” said the SVP of Technology and Cybersecurity for the real estate investment trust. What was once manual and targeted can now be automated and personalized at scale, making attacks harder to detect and easier to execute.

At the same time, the pace of exploitation is accelerating. As a global CISO operating across 40+ countries described it: “Zero-day vulnerabilities are no longer zero day; it’s minus one day. By the time you get to it and address it, it’s already a problem.” By the time risk is identified, it has often already been realized.

The result is a rapidly expanding and increasingly interconnected attack surface that challenges security teams to maintain visibility, context, and control across AI-driven activity.

3. Shadow AI is already everywhere

76% of organizations now cite shadow AI as a problem, one that is spreading through organizations in ways that are hard to track and even harder to control.

Employees are experimenting with publicly available Gen AI tools. Teams are spinning up low-code automations on their own. SaaS providers are quietly embedding AI into existing products. Developers are plugging AI services directly into workflows, often without pausing to consider what that exposure means.

The result is a lack of visibility into:

  • What AI tools are being used
  • What data those tools can access
  • Where prompts and outputs are going
  • Which AI agents are interacting with enterprise systems

The SVP of Cybersecurity at a real estate investment trust described the shift: “Before, I was worried about someone sending data erroneously to their personal email. Now we have all these agents online that people are utilizing, and we’re looking at those vectors as well.” For security teams, this means operating without a complete view of how AI is being used, what it can access, and where risk may already be emerging.

4. Built-in guardrails are not enough

Organizations often assume that native AI guardrails or provider-level controls are sufficient to manage AI risk. But securing AI requires ongoing visibility, oversight, and governance, not just controls configured at deployment. "It’s a misconception that adopting AI is going to solve all your problems,” warns a global financial services CISO.

Security leaders are increasingly recognizing the limitations of these controls as:

  • Fragmented and difficult to enforce consistently across multiple AI systems, workflows, and environments
  • Ambiguous in terms of accountability due to shared responsibility for AI governance between IT, security, developers, business teams, and third-party providers
  • Limited in end-to-end oversight, leaving gaps that stretch from the initial prompt all the way through to the downstream impact of an agent's actions

Securing AI demands more than simple prompt filtering or static policy enforcement. It requires understanding intent, behavior, and context across both human and AI activity.

The next phase of cybersecurity: securing AI

To safely and responsibly adopt AI at scale, organizations need a new operational model for cybersecurity that’s capable of:

• Understanding AI behavior

• Identifying risk in real time

• Maintaining governance without slowing innovation

The CSO of a $10 billion municipal utility organization described the challenge with precision: “We have to move at the speed of innovation and risk, because both are accelerating faster than ever.”

Embrace AI with confidence with Darktrace / SECURE AI

Darktrace has introduced Darktrace / SECURE AI™, a new product within the Darktrace ActiveAI Security Platform™  ,designed to provide enterprise-wide security for AI by applying industry leading behavioral analysis to how prompts, agents, and AI systems are used.

Darktrace / SECURE AITM delivers real-time visibility and control across Enterprise and SaaS GenAI prompts, AI agent identities, development and production environments, and Shadow AI - detecting even subtle misuse, misconfiguration, and drift that traditional, rule-based controls simply do not understand. By interpreting context and intent across humans and machines, Darktrace enables organizations to adopt AI at scale without introducing unmanaged risk

What makes this possible is Darktrace’s decade-long maturity and expertise in behavioral understanding and AI-native cybersecurity. Achieved with Self-Learning AI that has been proven across more than 10,000 organizations, Darktrace understands what “normal” looks like for a business, across its users, systems, and now AI, so that meaningful deviations can be detected and acted on before they become incidents.

With one CISO describing Darktrace’s Self-Learning AI as “a leap forward compared to other tools” and another as a “force multiplier,” the technology can interpret ambiguous interactions, understand how access accumulates over time, and recognize when behavior, human or machine, begins to drift.

“Strategically, we’re looking to gain more visibility into how AI is operating across the environment and achieve greater control over what AI should be allowed to access and do,” shared the CISO at a private fund administrator.  

“What I’ve seen from Darktrace / SECURE AI is extremely promising. I have tremendous confidence in Darktrace’s vision for where this is headed and its ability to execute on this new solution.”

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