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April 10, 2023

Employee-Conscious Email Security Solutions in the Workforce

Email threats commonly affect organizations. Read Darktrace's expert insights on how to safeguard your business by educating employees about email security.
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
Dan Fein
VP, Product
Written by
Carlos Gray
Senior Product Marketing Manager, Email
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10
Apr 2023

When considering email security, IT teams have historically had to choose between excluding employees entirely, or including them but giving them too much power and implementing unenforceable, trust-based policies that try to make up for it. 

However, just because email security should not rely on employees, this does not mean they should be excluded entirely. Employees are the ones interacting with emails daily, and their experiences and behaviors can provide valuable security insights and even influence productivity. 

AI technology supports employee engagement in this non-intrusive, nuanced way to not only maintain email security, but also enhance it. 

Finding a Balance of Employee Involvement in Security Strategies

Historically, security solutions offered ‘all or nothing’ approaches to employee engagement. On one hand, when employees are involved, they are unreliable. Employees cannot all be experts in security on top of their actual job responsibilities, and mistakes are bound to happen in fast-paced environments.  

Although there have been attempts to raise security awareness, they often have shortcomings, as training emails lack context and realism, leaving employees with poor understandings that often lead to reporting emails that are actually safe. Having users constantly triaging their inboxes and reporting safe emails wastes time that takes away from their own productivity as well as the productivity of the security team.

Other historic forms of employee involvement also put security at risk. For example, users could create blanket rules through feedback, which could lead to common problems like safe-listing every email that comes from the gmail.com domain. Other times, employees could choose for themselves to release emails without context or limitations, introducing major risks to the organization. While these types of actions include employees to participate in security, they do so at the cost of security. 

Even lower stakes employee involvement can prove ineffective. For example, excessive warnings when sending emails to external contacts can lead to banner fatigue. When employees see the same warning message or alert at the top of every message, it’s human nature that they soon become accustomed and ultimately immune to it.

On the other hand, when employees are fully excluded from security, an opportunity is missed to fine-tune security according to the actual users and to gain feedback on how well the email security solution is working. 

So, both options of historically conventional email security, to include or exclude employees, prove incapable of leveraging employees effectively. The best email security practice strikes a balance between these two extremes, allowing more nuanced interactions that maintain security without interrupting daily business operations. This can be achieved with AI that tailors the interactions specifically to each employee to add to security instead of detracting from it. 

Reducing False Reports While Improving Security Awareness Training 

Humans and AI-powered email security can simultaneously level up by working together. AI can inform employees and employees can inform AI in an employee-AI feedback loop.  

By understanding ‘normal’ behavior for every email user, AI can identify unusual, risky components of an email and take precise action based on the nature of the email to neutralize them, such as rewriting links, flattening attachments, and moving emails to junk. AI can go one step further and explain in non-technical language why it has taken a specific action, which educates users. In contrast to point-in-time simulated phishing email campaigns, this means AI can share its analysis in context and in real time at the moment a user is questioning an email. 

The employee-AI feedback loop educates employees so that they can serve as additional enrichment data. It determines the appropriate levels to inform and teach users, while not relying on them for threat detection

In the other direction, the AI learns from users’ activity in the inbox and gradually factors this into its decision-making. This is not a ‘one size fits all’ mechanism – one employee marking an email as safe will never result in blanket approval across the business – but over time, patterns can be observed and autonomous decision-making enhanced.  

Figure 1: The employee-AI feedback loop increases employee understanding without putting security at risk.

The employee-AI feedback loop draws out the maximum potential benefits of employee involvement in email security. Other email security solutions only consider the security team, enhancing its workflow but never considering the employees that report suspicious emails. Employees who try to do the right thing but blindly report emails never learn or improve and end up wasting their own time. By considering employees and improving security awareness training, the employee-AI feedback loop can level up users. They learn from the AI explanations how to identify malicious components, and so then report fewer emails but with greater accuracy. 

While AI programs have classically acted like black boxes, Darktrace trains its AI on the best data, the organization’s actual employees, and invites both the security team and employees to see the reasoning behind its conclusions. Over time, employees will trust themselves more as they better learn how to discern unsafe emails. 

Leveraging AI to Generate Productivity Gains

Uniquely, AI-powered email security can have effects outside of security-related areas. It can save time by managing non-productive email. As the AI constantly learns employee behavior in the inbox, it becomes extremely effective at detecting spam and graymail – emails that aren't necessarily malicious, but clutter inboxes and hamper productivity. It does this on a per-user basis, specific to how each employee treats spam, graymail, and newsletters. The AI learns to detect this clutter and eventually learns which to pull from the inbox, saving time for the employees. This highlights how security solutions can go even further than merely protecting the email environment with a light touch, to the point where AI can promote productivity gains by automating tasks like inbox sorting.

Preventing Email Mishaps: How to Deal with Human Error

Improved user understanding and decision making cannot stop natural human error. Employees are bound to make mistakes and can easily send emails to the wrong people, especially when Outlook auto-fills the wrong recipient. This can have effects ranging anywhere from embarrassing to critical, with major implications on compliance, customer trust, confidential intellectual property, and data loss. 

However, AI can help reduce instances of accidentally sending emails to the wrong people. When a user goes to send an email in Outlook, the AI will analyze the recipients. It considers the contextual relationship between the sender and recipients, the relationships the recipients have with each other, how similar each recipient’s name and history is to other known contacts, and the names of attached files.  

If the AI determines that the email is outside of a user’s typical behavior, it may alert the user. Security teams can customize what the AI does next: it can block the email, block the email but allow the user to override it, or do nothing but invite the user to think twice. Since the AI analyzes each email, these alerts are more effective than consistent, blanket alerts warning about external recipients, which often go ignored. With this targeted approach, the AI prevents data leakage and reduces cyber risk. 

Since the AI is always on and continuously learning, it can adapt autonomously to employee changes. If the role of an employee evolves, the AI will learn the new normal, including common behaviors, recipients, attached file names, and more. This allows the AI to continue effectively flagging potential instances of human error, without needing manual rule changes or disrupting the employee’s workflow. 

Email Security Informed by Employee Experience

As the practical users of email, employees should be considered when designing email security. This employee-conscious lens to security can strengthen defenses, improve productivity, and prevent data loss.  

In these ways, email security can benefit both employees and security teams. Employees can become another layer of defense with improved security awareness training that cuts down on false reports of safe emails. This insight into employee email behavior can also enhance employee productivity by learning and sorting graymail. Finally, viewing security in relation to employees can help security teams deploy tools that reduce data loss by flagging misdirected emails. With these capabilities, Darktrace/Email™ enables security teams to optimize the balance of employee involvement in email security.

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
Dan Fein
VP, Product
Written by
Carlos Gray
Senior Product Marketing Manager, Email

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April 21, 2026

How a Compromised eScan Update Enabled Multi‑Stage Malware and Blockchain C2

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The rise of supply chain attacks

In recent years, the abuse of trusted software has become increasingly common, with supply chain compromises emerging as one of the fastest growing vectors for cyber intrusions. As highlighted in Darktrace’s Annual Threat Report 2026, attackers and state-actors continue to find significant value in gaining access to networks through compromised trusted links, third-party tools, or legitimate software. In January 2026, a supply chain compromise affecting MicroWorld Technologies’ eScan antivirus product was reported, with malicious updates distributed to customers through the legitimate update infrastructure. This, in turn, resulted in a multi‑stage loader malware being deployed on compromised devices [1][2].

An overview of eScan exploitation

According to eScan’s official threat advisory, unauthorized access to a regional update server resulted in an “incorrect file placed in the update distribution path” [3]. Customers associated with the affected update servers who downloaded the update during a two-hour window on January 20 were impacted, with affected Windows devices subsequently have experiencing various errors related to update functions and notifications [3].

While eScan did not specify which regional update servers were affected by the malicious update, all impacted Darktrace customer environments were located in the Europe, Middle East, and Africa (EMEA) region.

External research reported that a malicious 32-bit executable file , “Reload.exe”, was first installed on affected devices, which then dropped the 64-bit downloader, “CONSCTLX.exe”. This downloader establishes persistence by creating scheduled tasks such as “CorelDefrag”, which are responsible for executing PowerShell scripts. Subsequently, it evades detection by tampering with the Windows HOSTS file and eScan registry to prevent future remote updates intended for remediation. Additional payloads are then downloaded from its command-and-control (C2) server [1].

Darktrace’s coverage of eScan exploitation

Initial Access and Blockchain as multi-distributed C2 Infrastructure

On January 20, the same day as the aforementioned two‑hour exploit window, Darktrace observed multiple devices across affected networks downloading .dlz package files from eScan update servers, followed by connections to an anomalous endpoint, vhs.delrosal[.]net, which belongs to the attackers’ C2 infrastructure.

The endpoint contained a self‑signed SSL certificate with the string “O=Internet Widgits Pty Ltd, ST=SomeState, C=AU”, a default placeholder commonly used in SSL/TLS certificates for testing and development environments, as well as in malicious C2 infrastructure [4].

Utilizing a multi‑distributed C2 infrastructure, the attackers also leveraged domains linked with the Solana open‑source blockchain for C2 purposes, namely “.sol”. These domains were human‑readable names that act as aliases for cryptocurrency wallet addresses. As browsers do not natively resolve .sol domains, the Solana Naming System (formerly known as Bonfida, an independent contributor within the Solana ecosystem) provides a proxy service, through endpoints such as sol-domain[.]org, to enable browser access.

Darktrace observed devices connecting to blackice.sol-domain[.]org, indicating that attackers were likely using this proxy to reach a .sol domain for C2 activity. Given this behavior, it is likely that the attackers leveraged .sol domains as a dead drop resolver, a C2 technique in which threat actors host information on a public and legitimate service, such as a blockchain. Additional proxy resolver endpoints, such as sns-resolver.bonfida.workers[.]dev, were also observed.

Solana transactions are transparent, allowing all activity to be viewed publicly. When Darktrace analysts examined the transactions associated with blackice[.]sol, they observed that the earliest records dated November 7, 2025, which coincides with the creation date of the known C2 endpoint vhs[.]delrosal[.]net as shown in WHOIS Lookup information [4][5].

WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
Figure 1: WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
 Earliest observed transaction record for blackice[.]sol on public ledgers.
Figure 2: Earliest observed transaction record for blackice[.]sol on public ledgers.

Subsequent instructions found within the transactions contained strings such as “CNAME= vhs[.]delrosal[.]net”, indicating attempts to direct the device toward the malicious endpoint. A more recent transaction recorded on January 28 included strings such as “hxxps://96.9.125[.]243/i;code=302”, suggesting an effort to change C2 endpoints. Darktrace observed multiple alerts triggered for these endpoints across affected devices.

Similar blockchain‑related endpoints, such as “tumama.hns[.]to”, were also observed in C2 activities. The hns[.]to service allows web browsers to access websites registered on Handshake, a decentralized blockchain‑based framework designed to replace centralized authorities and domain registries for top‑level domains. This shift toward decentralized, blockchain‑based infrastructure likely reflects increased efforts by attackers to evade detection.

In outgoing connections to these malicious endpoints across affected networks, Darktrace / NETWORK recognized that the activity was 100% rare and anomalous for both the devices and the wider networks, likely indicative of malicious beaconing, regardless of the underlying trusted infrastructure. In addition to generating multiple model alerts to capture this malicious activity across affected networks, Darktrace’s Cyber AI Analyst was able to compile these separate events into broader incidents that summarized the entire attack chain, allowing customers’ security teams to investigate and remediate more efficiently. Moreover, in customer environments where Darktrace’s Autonomous Response capability was enabled, Darktrace took swift action to contain the attack by blocking beaconing connections to the malicious endpoints, even when those endpoints were associated with seemingly trustworthy services.

Conclusion

Attacks targeting trusted relationships continue to be a popular strategy among threat actors. Activities linked to trusted or widely deployed software are often unintentionally whitelisted by existing security solutions and gateways. Darktrace observed multiple devices becoming impacted within a very short period, likely because tools such as antivirus software are typically mass‑deployed across numerous endpoints. As a result, a single compromised delivery mechanism can greatly expand the attack surface.

Attackers are also becoming increasingly creative in developing resilient C2 infrastructure and exploiting legitimate services to evade detection. Defenders are therefore encouraged to closely monitor anomalous connections and file downloads. Darktrace’s ability to detect unusual activity amidst ever‑changing tactics and indicators of compromise (IoCs) helps organizations maintain a proactive and resilient defense posture against emerging threats.

Credit to Joanna Ng (Associate Principal Cybersecurity Analyst) and Min Kim (Associate Principal Cybersecurity Analyst) and Tara Gould (Malware Researcher Lead)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

  • Anomalous File::Zip or Gzip from Rare External Location
  • Anomalous Connection / Suspicious Self-Signed SSL
  • Anomalous Connection / Rare External SSL Self-Signed
  • Anomalous Connection / Suspicious Expired SSL
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device

List of Indicators of Compromise (IoCs)

  • vhs[.]delrosal[.]net – C2 server
  • tumama[.]hns[.]to – C2 server
  • blackice.sol-domain[.]org – C2 server
  • 96.9.125[.]243 – C2 Server

MITRE ATT&CK Mapping

  • T1071.001 - Command and Control: Web Protocols
  • T1588.001 - Resource Development
  • T1102.001 - Web Service: Dead Drop Resolver
  • T1195 – Supple Chain Compromise

References

[1] https://www.morphisec.com/blog/critical-escan-threat-bulletin/

[2] https://www.bleepingcomputer.com/news/security/escan-confirms-update-server-breached-to-push-malicious-update/

[3] hxxps://download1.mwti.net/documents/Advisory/eScan_Security_Advisory_2026[.]pdf

[4] https://www.virustotal.com/gui/domain/delrosal.net

[5] hxxps://explorer.solana[.]com/address/2wFAbYHNw4ewBHBJzmDgDhCXYoFjJnpbdmeWjZvevaVv

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About the author
Joanna Ng
Associate Principal Analyst

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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

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How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.  

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.  

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.  

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.  

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.  

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.  

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.  

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

[related-resource]

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About the author
Ed Jennings
President and CEO
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