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January 9, 2024

Three Ways AI Secures OT & ICS from Cyber Attacks

Explore the three challenges facing industries that manage OT and ICS Systems, the benefits of adopting AI technology, and Darktrace / OT’s unique role!
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
Oakley Cox
Director of Product
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09
Jan 2024

What is OT and ICS?

Operational technologies and industrial control systems are the networked technologies used for the automation of physical processes. These are the technologies that allow operators to control processes and retrieve real time process data from a factory, rail system, pipeline, and other industrial processes.  

The role of AI in defending OT/ICS networks  

While largely adopted by industrial organizations, OT is utilized by Critical Infrastructures, these being the industries that directly affect the health, safety, and welfare of the public. As these organizations expand and adopt new networked industrial technologies, they are simultaneously expanding their attack surface.  

With a larger attack surface, more attacks targeting OT/ICS, and focused coordination around cyber security from regulatory authorities, security personnel have increasing workloads that make it difficult to keep pace with threats and vulnerabilities. Defenders are managing growing attack surfaces due to IT and OT convergence. Thus, the adoption of AI technology to protect, detect, respond, and recover from cyber incidents in industrial systems is paramount for keeping critical infrastructure safe.

This blog will explore three challenges facing industries managing OT/ICS, the perceived benefits of adopting AI technology to address these challenges, and Darktrace/OT’s unique role in this process.  

Darktrace also delivers complete AI-powered solutions to defend US federal government customers from cyber disruptions and ensure mission resilience. Learn more about high fidelity detection in Darktrace Federal’s TAC report.

Figure 1: AI statistics from Gartner and Deloitte

Three ways AI helps improves OT/ICS security  

1. Anomaly detection and response

In this heightened security landscape, OT/ICS environments face a spectrum of external cyber threats that demand vigilant defense. From the looming risk of industrial ransomware to the threat of insiders, yet another dimension is added to security challenge, meaning security professionals must be equipped to detect and respond to internal and external threats.  

While threats are eminent from both inside and outside the organization, many organizations rely on Indicator of Compromises (IOCs) for threat detection. By definition, these solutions can only detect network activity they recognize as an indicator of compromise; therefore, often miss insider threats and novel (zero-day) attacks because the tactics, techniques, and procedures (TTPs) and attack toolkits have never been seen in practice.  

Anomaly-based detection is best suited to combat never-before-seen threats and signatureless threats from the inside. However, not all detection methods are equal. Most anomaly-based detection solutions that leverage AI rely on a combination of supervised machine learning, deep learning, and transformers to train and inform their systems. This entails shipping your company’s data out to a large data lake housed somewhere in the cloud where it gets blended with attack data from thousands of other organizations. This data set gets used to train AI systems — yours and everyone else’s — to recognize patterns of attack based on previously encountered threats.  

While this method reduces the workload for security teams who would have to input attack data otherwise manually, it runs the same risk of only detecting known threats and has potential privacy concerns when shipping this data externally.  

To improve the quality and speed of anomaly detection, Darktrace/OT uses Self-Learning AI that leverages Bayesian Probabilistic Methodologies, Graph Theory, and Deep Neural Networks to learn your organization from the ground up in real time. By learning your unique organization, Darktrace/OT develops a sophisticated baseline knowledge of your network and assets, identifying abnormal activity that indicates a threat based on your unique network data at machine speed. Because the AI engine is local to the organization and/or assets, concerns of data residency and privacy are reduced, and the result is faster time to detect and triage incidents.  

Leveraging Self-Learning AI, Darktrace/OT uses autonomous response that severs only the anomalous or risky behaviors allowing the assets to continue to operate as normal. Organizations work with Darktrace to customize how they want Darktrace’s autonomous response to be applied. These options vary from on a device- by-device basis, device type by device type, or subnet by subnet basis and can be done completely autonomously or in human confirmation mode. This gives security teams more time to respond to an incident and reduces operational downtime when facing a threat.  

Darktrace leverages a combination of AI methods:

  • Self-Learning AI
  • Bayesian classification probabilistic models  
  • Deep neural networks
  • Transformers
  • Graph theory models
  • Clustering models  
  • Anomaly detection models
  • Generative and applied AI  
  • Natural language processing  
  • Supervised machine learning for investigation process of alerts

2. Vulnerability & Asset Management

At present, managing OT cyber risk is labor and resource intensive. Many organizations use third-party auditors to identify assets and vulnerabilities, grade compliance, and recommend improvements.  

At best, these exercises become tick-box exercises for companies to stay in compliance with little measurable reduction in cyber risk. At worst, asset owners can be left with a mountain of vulnerability information to work through, much of it irrelevant to the security risks Engineering and Operations teams deal with day to day, and increasingly out of date each passing day after the annual or biannual audit has been completed.  

In both cases, organizations are left using a patchwork of point products to address different aspects of preventative OT cyber security, most of which lack wider business context and lead to costly inefficiencies with no real impact to vulnerability or risk exposure.  

Darktrace’s technology helps in three unique ways:

  1. AI populates asset inventories: Self-Learning AI technology listens and learns from network traffic to populate or update asset inventories. It does this not just by identifying simple IPs, mac addresses, and hostnames, it learns from what it sees and automatically classifies or tags specific types of assets with the function that they perform. For example, if a specific device is performing functions like a PLC, sending commands to and from an HMI, it can appropriately tag and label these systems.
  2. AI prioritizes risk: Leveraging Bayesian Probabilistic Methodologies, Graph Theory, and Deep Neural Networks, Darktrace/OT assesses the strategic risks facing your organization in real time. Using knowledge of data points on all your networked assets, data flow topology, your assets vulnerabilities and OSINT, Darktrace identifies and prioritizes high-value assets, potential attack pathways based on an existing vulnerabilities targetability and impact.
  3. AI explains remediation tactics: Many OT devices run 24/7 operations and cannot be taken offline to apply a patch, assuming a patch is even available. Darktrace/OT uses natural language processing to provide and explain prioritized remediation and mitigation associated with a given cyber risk across all MITRE ATT&CK techniques. Thus, where a CVE exists but a patch cannot be applied, a different technical mitigation can be recommended to remove a potential attack path before it can be exploited, preemptively securing vital internal systems and assets.
Figure 2: A critical attack path which starts with the compromise of a PC in the internal IT network, and ends with a PLC in the OT network. Each step is mapped out to the real world TTPs including abuse of SSH sessions and the modifications of ICS programs

3. Simplify compliance and reporting

Organizations, regardless of size or resources, have compliance regulations they need to adhere to. What this creates is an increased workload for security professionals. For smaller organizations, security teams might lack the manpower or resources to report in the short time frame that is required. For large organizations, keeping track of a massive amount of assets proves to be a challenge. Both cases emanate the risk of reporting fatigue where organizations might be hesitant to report incidents due to the complexity and time requirements they demand.  

An AI engine within the Darktrace/OT platform, Cyber AI analyst autonomously investigates incidents, summarize findings in natural language, and provides comprehensive insights into the nature and scope of cyber threats to improve the time it takes to triage and report on incidents. The ability to stitch together and present related security events provides a holistic understanding of the incident, enabling security analysts to identify patterns, assess the scope of potential threats, and prioritize responses effectively.  

Darktrace's detection capabilities identify every stage of an intrusion, from a compromised domain controller to network reconnaissance and privilege escalation. The AI technology is capable of detecting infections across several devices and generating incident reports that piece together disparate events to give a clear security narrative containing details of the attack, bridging the communication gap between IT and OT specialists.  

Post-incident, the technology assists in outlining timelines, discerning compromised data, pinpointing unusual activities, and aiding security teams in proactive threat mitigation.  

With its capabilities, organizations can swiftly understand the attack timeline, affected assets, unauthorized accesses, compromised data points, and malicious interactions, facilitating appropriate communication and action. For example, when Cyber AI Analyst shows an attack path, the security team gains insight on the segmentation or lack thereof between two subnets allowing the security team to appropriately segment the subnets.  

Cyber AI improves critical infrastructure operators’ ability to report major cyber-attacks to regulatory authorities. Considering that 72 hours is the reporting period for most significant incidents — and 24 hours for ransomware payments — Cyber AI Analyst is no longer a nice-to-have but a must-have for critical infrastructure.

Figure 3: The tabs labeled 1-4 denote model breaches, each with a specific action and severity indicated by color dots. Darktrace integrates these breaches, offering the security team a unified view of interconnected security events.  

The right AI for the right challenge

Incident Phase:

Protect

Role of AI:

Cyber risk prioritization

Attack path modelling

Compliance reporting

Darktrace Product:

PREVENT/OT

Incident Phase:

Detect

Role of AI:

Anomaly detection

Triaging and investigating

Darktrace Product:

Cyber AI analyst

DETECT/OT

Incident Phase:

Respond

Role of AI: 

Autonomous response  

Incident reporting

Darktrace Product:

RESPOND/OT

Incident Phase:

Recover

Role of AI:

Incident preparedness

Incident simulations

Darktrace Product:

HEAL

Credit to: Nicole Carignan, VP of Strategic Cyber AI - Kendra Gonzalez Duran, Director of Technology Innovation - & Daniel Simonds, Director of Operational Technology for their contribution to this blog.

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
Oakley Cox
Director of Product

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January 30, 2026

ClearFake: From Fake CAPTCHAs to Blockchain-Driven Payload Retrieval

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What is ClearFake?

As threat actors evolve their techniques to exploit victims and breach target networks, the ClearFake campaign has emerged as a significant illustration of this continued adaptation. ClearFake is a campaign observed using a malicious JavaScript framework deployed on compromised websites, impacting sectors such as e‑commerce, travel, and automotive. First identified in mid‑2023, ClearFake is frequently leveraged to socially engineer victims into installing fake web browser updates.

In ClearFake compromises, victims are steered toward compromised WordPress sites, often positioned by attackers through search engine optimization (SEO) poisoning. Once on the site, users are presented with a fake CAPTCHA. This counterfeit challenge is designed to appear legitimate while enabling the execution of malicious code. When a victim interacts with the CAPTCHA, a PowerShell command containing a download string is retrieved and executed.

Attackers commonly abuse the legitimate Microsoft HTML Application Host (MSHTA) in these operations. Recent campaigns have also incorporated Smart Chain endpoints, such as “bsc-dataseed.binance[.]org,” to obtain configuration code. The primary payload delivered through ClearFake is typically an information stealer, such as Lumma Stealer, enabling credential theft, data exfiltration, and persistent access [1].

Darktrace’s Coverage of ClearFake

Darktrace / ENDPOINT first detected activity likely associated with ClearFake on a single device on over the course of one day on November 18, 2025. The system observed the execution of “mshta.exe,” the legitimate Microsoft HTML Application Host utility. It also noted a repeated process command referencing “weiss.neighb0rrol1[.]ru”, indicating suspicious external activity. Subsequent analysis of this endpoint using open‑source intelligence (OSINT) indicated that it was a malicious, domain generation algorithm (DGA) endpoint [2].

Figure 1: The process line referencing weiss.neighb0rrol1[.]ru, as observed by Darktrace / ENDPOINT.

This activity indicates that mshta.exe was used to contact a remote server, “weiss.neighb0rrol1[.]ru/rpxacc64mshta,” and execute the associated HTA file to initiate the next stage of the attack. OSINT sources have since heavily flagged this server as potentially malicious [3].

The first argument in this process uses the MSHTA utility to execute the HTA file hosted on the remote server. If successful, MSHTA would then run JavaScript or VBScript to launch PowerShell commands used to retrieve malicious payloads, a technique observed in previous ClearFake campaigns. Darktrace also detected unusual activity involving additional Microsoft executables, including “winlogon.exe,” “userinit.exe,” and “explorer.exe.” Although these binaries are legitimate components of the Windows operating system, threat actors can abuse their normal behavior within the Windows login sequence to gain control over user sessions, similar to the misuse of mshta.exe.

EtherHiding cover

Darktrace also identified additional ClearFake‑related activity, specifically a connection to bsc-testnet.drpc[.]org, a legitimate BNB Smart Chain endpoint. This activity was triggered by injected JavaScript on the compromised site www.allstarsuae[.]com, where the script initiated an eth_call POST request to the Smart Chain endpoint.

Figure 2: Example of a fake CAPTCHA on the compromised site www.allstarsuae[.]com.

EtherHiding is a technique in which threat actors leverage blockchain technology, specifically smart contracts, as part of their malicious infrastructure. Because blockchain is anonymous, decentralized, and highly persistent, it provides threat actors with advantages in evading defensive measures and traditional tracking [4].

In this case, when a user visits a compromised WordPress site, injected base64‑encoded JavaScript retrieved an ABI string, which was then used to load and execute a contract hosted on the BNB Smart Chain.

Figure 3: JavaScript hosted on the compromised site www.allstaruae[.]com.

Conducting malware analysis on this instance, the Base64 decoded into a JavaScript loader. A POST request to bsc-testnet.drpc[.]org was then used to retrieve a hex‑encoded ABI string that loads and executes the contract. The JavaScript also contained hex and Base64‑encoded functions that decoded into additional JavaScript, which attempted to retrieve a payload hosted on GitHub at “github[.]com/PrivateC0de/obf/main/payload.txt.” However, this payload was unavailable at the time of analysis.

Figure 4: Darktrace’s detection of the POST request to bsc-testnet.drpc[.]org.

Figure 5: Darktrace’s detection of the executable file and the malicious hostname.

Autonomous Response

As Darktrace’s Autonomous Response capability was enabled on this customer’s network, Darktrace was able to take swift mitigative action to contain the ClearFake‑related activity early, before it could lead to potential payload delivery. The affected device was blocked from making external connections to a number of suspicious endpoints, including 188.114.96[.]6, *.neighb0rrol1[.]ru, and neighb0rrol1[.]ru, ensuring that no further malicious connections could be made and no payloads could be retrieved.

Autonomous Response also acted to prevent the executable mshta.exe from initiating HTA file execution over HTTPS from this endpoint by blocking the attempted connections. Had these files executed successfully, the attack would likely have resulted in the retrieval of an information stealer, such as Lumma Stealer.

Figure 6: Autonomous Response’s intervention against the suspicious connectivity observed.

Conclusion

ClearFake continues to be observed across multiple sectors, but Darktrace remains well‑positioned to counter such threats. Because ClearFake’s end goal is often to deliver malware such as information stealers and malware loaders, early disruption is critical to preventing compromise. Users should remain aware of this activity and vigilant regarding fake CAPTCHA pop‑ups. They should also monitor unusual usage of MSHTA and outbound connections to domains that mimic formats such as “bsc-dataseed.binance[.]org” [1].

In this case, Darktrace was able to contain the attack before it could successfully escalate and execute. The attempted execution of HTA files was detected early, allowing Autonomous Response to intervene, stopping the activity from progressing. As soon as the device began communicating with weiss.neighb0rrol1[.]ru, an Autonomous Response inhibitor triggered and interrupted the connections.

As ClearFake continues to rise, users should stay alert to social engineering techniques, including ClickFix, that rely on deceptive security prompts.

Credit to Vivek Rajan (Senior Cyber Analyst) and Tara Gould (Malware Research Lead)


Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Detections

Process / New Executable Launched

Endpoint / Anomalous Use of Scripting Process

Endpoint / New Suspicious Executable Launched

Endpoint / Process Connection::Unusual Connection from New Process

Autonomous Response Models

Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block

List of Indicators of Compromise (IoCs)

  • weiss.neighb0rrol1[.]ru – URL - Malicious Domain
  • 188.114.96[.]6 – IP – Suspicious Domain
  • *.neighb0rrol1[.]ru – URL – Malicious Domain

MITRE Tactics

Initial Access, Drive-by Compromise, T1189

User Execution, Execution, T1204

Software Deployment Tools, Execution and Lateral Movement, T1072

Command and Scripting Interpreter, T1059

System Binary Proxy Execution: MSHTA, T1218.005

References

1.        https://www.kroll.com/en/publications/cyber/rapid-evolution-of-clearfake-delivery

2.        https://www.virustotal.com/gui/domain/weiss.neighb0rrol1.ru

3.        https://www.virustotal.com/gui/file/1f1aabe87e5e93a8fff769bf3614dd559c51c80fc045e11868f3843d9a004d1e/community

4.        https://www.packetlabs.net/posts/etherhiding-a-new-tactic-for-hiding-malware-on-the-blockchain/

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Vivek Rajan
Cyber Analyst

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January 30, 2026

The State of Cybersecurity in the Finance Sector: Six Trends to Watch

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The evolving cybersecurity threat landscape in finance

The financial sector, encompassing commercial banks, credit unions, financial services providers, and cryptocurrency platforms, faces an increasingly complex and aggressive cyber threat landscape. The financial sector’s reliance on digital infrastructure and its role in managing high-value transactions make it a prime target for both financially motivated and state-sponsored threat actors.

Darktrace’s latest threat research, The State of Cybersecurity in the Finance Sector, draws on a combination of Darktrace telemetry data from real-world customer environments, open-source intelligence, and direct interviews with financial-sector CISOs to provide perspective on how attacks are unfolding and how defenders in the sector need to adapt.  

Six cybersecurity trends in the finance sector for 2026

1. Credential-driven attacks are surging

Phishing continues to be a leading initial access vector for attacks targeting confidentiality. Financial institutions are frequently targeted with phishing emails designed to harvest login credentials. Techniques including Adversary-in-The-Middle (AiTM) to bypass Multi-factor Authentication (MFA) and QR code phishing (“quishing”) are surging and are capable of fooling even trained users. In the first half of 2025, Darktrace observed 2.4 million phishing emails within financial sector customer deployments, with almost 30% targeted towards VIP users.  

2. Data Loss Prevention is an increasing challenge

Compliance issues – particularly data loss prevention -- remain a persistent risk. In October 2025 alone, Darktrace observed over 214,000 emails across financial sector customers that contained unfamiliar attachments and were sent to suspected personal email addresses highlighting clear concerns around data loss prevention. Across the same set of customers within the same time frame, more than 351,000 emails containing unfamiliar attachments were sent to freemail addresses (e.g. gmail, yahoo, icloud), highlighting clear concerns around DLP.  

Confidentiality remains a primary concern for financial institutions as attackers increasingly target sensitive customer data, financial records, and internal communications.  

3. Ransomware is evolving toward data theft and extortion

Ransomware is no longer just about locking systems, it’s about stealing data first and encrypting second. Groups such as Cl0p and RansomHub now prioritize exploiting trusted file-transfer platforms to exfiltrate sensitive data before encryption, maximizing regulatory and reputational fallout for victims.  

Darktrace’s threat research identified routine scanning and malicious activity targeting internet-facing file-transfer systems used heavily by financial institutions. In one notable case involving Fortra GoAnywhere MFT, Darktrace detected malicious exploitation behavior six days before the CVE was publicly disclosed, demonstrating how attackers often operate ahead of patch cycles

This evolution underscores a critical reality: by the time a vulnerability is disclosed publicly, it may already be actively exploited.

4. Attackers are exploiting edge devices, often pre-disclosure.  

VPNs, firewalls, and remote access gateways have become high-value targets, and attackers are increasingly exploiting them before vulnerabilities are publicly disclosed. Darktrace observed pre-CVE exploitation activity affecting edge technologies including Citrix, Palo Alto, and Ivanti, enabling session hijacking, credential harvesting, and privileged lateral movement into core banking systems.  

Once compromised, these edge devices allow adversaries to blend into trusted network traffic, bypassing traditional perimeter defenses. CISOs interviewed for the report repeatedly described VPN infrastructure as a “concentrated focal point” for attackers, especially when patching and segmentation lag behind operational demands.

5. DPRK-linked activity is growing across crypto and fintech.  

State-sponsored activity, particularly from DPRK-linked groups affiliated with Lazarus, continues to intensify across cryptocurrency and fintech organizations. Darktrace identified coordinated campaigns leveraging malicious npm packages, previously undocumented BeaverTail and InvisibleFerret malware, and exploitation of React2Shell (CVE-2025-55182) for credential theft and persistent backdoor access.  

Targeting was observed across the United Kingdom, Spain, Portugal, Sweden, Chile, Nigeria, Kenya, and Qatar, highlighting the global scope of these operations.  

6. Cloud complexity and AI governance gaps are now systemic risks.  

Finally, CISOs consistently pointed to cloud complexity, insider risk from new hires, and ungoverned AI usage exposing sensitive data as systemic challenges. Leaders emphasized difficulty maintaining visibility across multi-cloud environments while managing sensitive data exposure through emerging AI tools.  

Rapid AI adoption without clear guardrails has introduced new confidentiality and compliance risks, turning governance into a board-level concern rather than a purely technical one.

Building cyber resilience in a shifting threat landscape

The financial sector remains a prime target for both financially motivated and state-sponsored adversaries. What this research makes clear is that yesterday’s security assumptions no longer hold. Identity attacks, pre-disclosure exploitation, and data-first ransomware require adaptive, behavior-based defenses that can detect threats as they emerge, often ahead of public disclosure.

As financial institutions continue to digitize, resilience will depend on visibility across identity, edge, cloud, and data, combined with AI-driven defense that learns at machine speed.  

Learn more about the threats facing the finance sector, and what your organization can do to keep up in The State of Cybersecurity in the Finance Sector report here.  

Acknowledgements:

The State of Cybersecurity in the Finance sector report was authored by Calum Hall, Hugh Turnbull, Parvatha Ananthakannan, Tiana Kelly, and Vivek Rajan, with contributions from Emma Foulger, Nicole Wong, Ryan Traill, Tara Gould, and the Darktrace Threat Research and Incident Management teams.

[related-resource]  

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Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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