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.

Written by

Senior Product Marketing Manager, Email

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

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.

Senior Product Marketing Manager, Email

Inside the SOC

Written by

Dan Fein

VP, Product

Written by

Carlos Gray

Senior Product Marketing Manager, Email

Associate Principal Analyst & Regional Consultancy Lead

•

March 26, 2026

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March 26, 2026

Phantom Footprints: Tracking GhostSocks Malware

Why are attackers using residential proxies?

In today's threat landscape, blending in to normal activity is the key to success for attackers and the growing reliance on residential proxies shows a significant shift in how threat actors are attempting to bypass IP detection tools.

The increasing dependency on residential proxies has exposed how prevalent proxy services are and how reliant a diverse range of threat actors are on them. From cybercriminal groups to state‑sponsored actors, the need to bypass IP detection tools is fundamental to the success of these groups. One malware that has quietly become notorious for its ability to avoid anomaly detection is GhostSocks, a malware that turns compromised devices into residential proxies.

What is GhostSocks?

Originally marketed on the Russian underground forum xss[.]is as a Malware‑as‑a‑Service (MaaS), GhostSocks enables threat actors to turn compromised devices into residential proxies, leveraging the victim's internet bandwidth to route malicious traffic through it.

How does Ghostsocks malware work?

The malware offers the threat actor a “clean” IP address, making it look like it is coming from a household user. This enables the bypassing of geographic restrictions and IP detection tools, a perfect tool for avoiding anomaly detection. It wasn’t until 2024, when a partnership was announced with the infamous information stealer Lumma Stealer, that GhostSocks surged into widespread adoption and alluded to who may be the author of the proxy malware.

Written in GoLang, GhostSocks utilizes the SOCKS5 proxy protocol, creating a SOCKS5 connection on infected devices. It uses a relay‑based C2 implementation, where an intermediary server sits in between the real command-and-control (C2) server and the infected device.

How does Ghostsocks malware evade detection?

To further increase evasion, the Ghostsocks malware wraps its SOCKS5 tunnels in TLS encryption, allowing its malicious traffic to blend into normal network traffic.

Early variants of GhostSocks do not implement a persistence mechanism; however, later versions achieve persistence via registry run keys, ensuring sustained proxy operational time [1].

While proxying is its primary purpose, GhostSocks also incorporates backdoor functionality, enabling malicious actors to run arbitrary commands and download and deploy additional malicious payloads. This was evident with the well‑known ransomware group Black Basta, which reportedly used GhostSocks as a way of maintaining long‑term access to victims’ networks [1].

Darktrace’s detection of GhostSocks Malware

Darktrace observed a steady increase in GhostSocks activity across its customer base from late 2025, with its Threat Research team identifying multiple incidents involving the malware. In one notable case from December 2025, Darktrace detected GhostSocks operating alongside Lumma Stealer, reinforcing that the partnership between Lumma and GhostSocks remains active despite recent attempts to disrupt Lumma’s infrastructure.

Darktrace’s first detection of GhostSocks‑related activity came when a device on the network of a customer in the education sector began making connections to an endpoint with a suspicious self‑signed certificate that had never been seen on the network before.

The endpoint in question, 159.89.46[.]92 with the hostname retreaw[.]click, has been flagged by multiple open‑source intelligence (OSINT) sources as being associated with Lumma Stealer’s C2 infrastructure [2], indicating its likely role in the delivery of malicious payloads.

Figure 1: Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.

Less than two minutes later, Darktrace observed the same device downloading the executable (.exe) file “Renewable.exe” from the IP 86.54.24[.]29, which Darktrace recognized as 100% rare for this network.

Figure 2: Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.

Both the file MD5 hash and the executable itself have been identified by multiple OSINT vendors as being associated with the GhostSocks malware [3], with the executable likely the backdoor component of the GhostSocks malware, facilitating the distribution of additional malicious payloads [4].

Following this detection, Darktrace’s Autonomous Response capability recommended a blocking action for the device in an early attempt to stop the malicious file download. In this instance, Darktrace was configured in Human Confirmation Mode, meaning the customer’s security team was required to manually apply any mitigative response actions. Had Autonomous Response been fully enabled at the time of the attack, the connections to 86.54.24[.]29 would have been blocked, rendering the malware ineffective at reaching its C2 infrastructure and halting any further malicious communication.

Figure 3: Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.

As the attack was able to progress, two days later the device was detected downloading additional payloads from the endpoint www.lbfs[.]site (23.106.58[.]48), including “Setup.exe”, “,.exe”, and “/vp6c63yoz.exe”.

Figure 4: Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.

Once again, Darktrace recognized the anomalous nature of these downloads and suggested that a “group pattern of life” be enforced on the offending device in an attempt to contain the activity. By enforcing a pattern of life on a device, Darktrace restricts its activity to connections and behaviors similar to those performed by peer devices within the same group, while still allowing it to carry out its expected activity, effectively preventing deviations indicative of compromise while minimizing disruption. As mentioned earlier, these mitigative actions required manual implementation, so the activity was able to continue. Darktrace proceeded to suggest further actions to contain subsequent malicious downloads, including an attempt to block all outbound traffic to stop the attack from progressing.

Figure 5: An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.

Around the same time, a third executable download was detected, this time from the hostname hxxp[://]d2ihv8ymzp14lr.cloudfront.net/2021-08-19/udppump[.]exe, along with the file “udppump.exe”.While GhostSocks may have been present only to facilitate the delivery of additional payloads, there is no indication that these CloudFront endpoints or files are functionally linked to GhostSocks. Rather, the evidence points to broader malicious file‑download activity.

Shortly after the multiple executable files had been downloaded, Darktrace observed the device initiating a series of repeated successful connections to several rare external endpoints, behavior consistent with early-stage C2 beaconing activity.

Cyber AI Analyst’s investigation

Figure 7: Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.

Throughout the course of this attack, Darktrace’s Cyber AI Analyst carried out its own autonomous investigation, piecing together seemingly separate events into one wider incident encompassing the first suspicious downloads beginning on December 4, the unusual connectivity to many suspicious IPs that followed, and the successful beaconing activity observed two days later. By analyzing these events in real-time and viewing them as part of the bigger picture, Cyber AI Analyst was able to construct an in‑depth breakdown of the attack to aid the customer’s investigation and remediation efforts.

Figure 8: Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.

Conclusion

The versatility offered by GhostSocks is far from new, but its ability to convert compromised devices into residential proxy nodes, while enabling long‑term, covert network access—illustrates how threat actors continue to maximise the value of their victims’ infrastructure. Its growing popularity, coupled with its ongoing partnership with Lumma, demonstrates that infrastructure takedowns alone are insufficient; as long as threat actors remain committed to maintaining anonymity and can rapidly rebuild their ecosystems, related malware activity is likely to persist in some form.

Credit to Isabel Evans (Cyber Analyst), Gernice Lee (Associate Principal Analyst & Regional Consultancy Lead – APJ)
Edited by Ryan Traill (Content Manager)

Appendices

References

1. https://bloo.io/research/malware/ghostsocks

2. https://www.virustotal.com/gui/domain/retreaw.click/community

3. https://synthient.com/blog/ghostsocks-from-initial-access-to-residential-proxy

4. https://www.joesandbox.com/analysis/1810568/0/html

5. https://www.virustotal.com/gui/url/fab6525bf6e77249b74736cb74501a9491109dc7950688b3ae898354eb920413

Darktrace Model Detections

Real-time Detection Models

Anomalous Connection / Suspicious Self-Signed SSL

Anomalous Connection / Rare External SSL Self-Signed

Anomalous File / EXE from Rare External Location

Anomalous File / Multiple EXE from Rare External Locations

Compromise / Possible Fast Flux C2 Activity

Compromise / Large Number of Suspicious Successful Connections

Compromise / Large Number of Suspicious Failed Connections

Compromise / Sustained SSL or HTTP Increase

Autonomous Response Models

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

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique

Resource Development – T1588 - Malware

Initial Access - T1189 - Drive-by Compromise

Persistence – T1112 – Modify Registry

Command and Control – T1071 – Application Layer Protocol

Command and Control – T1095 – Non-application Layer Protocol

Command and Control – T1071 – Web Protocols

Command and Control – T1571 – Non-Standard Port

Command and Control – T1102 – One-Way Communication

List of Indicators of Compromise (IoCs)

86.54.24[.]29 - IP - Likely GhostSocks C2

http[://]86.54.24[.]29/Renewable[.]exe - Hostname - GhostSocks Distribution Endpoint

http[://]d2ihv8ymzp14lr.cloudfront[.]net/2021-08-19/udppump[.]exe - CDN - Payload Distribution Endpoint

www.lbfs[.]site - Hostname - Likely C2 Endpoint

retreaw[.]click - Hostname - Lumma C2 Endpoint

alltipi[.]com - Hostname - Possible C2 Endpoint

w2.bruggebogeyed[.]site - Hostname - Possible C2 Endpoint

9b90c62299d4bed2e0752e2e1fc777ac50308534 - SHA1 file hash – Likely GhostSocks payload

3d9d7a7905e46a3e39a45405cb010c1baa735f9e - SHA1 file hash - Likely follow-up payload

10f928e00a1ed0181992a1e4771673566a02f4e3 - SHA1 file hash - Likely follow-up payload

About the author

Gernice Lee

Associate Principal Analyst & Regional Consultancy Lead

Blog

AI

March 27, 2026

State of AI Cybersecurity 2026: 92% of security professionals concerned about the impact of AI agents

The findings in this blog are taken from Darktrace's annual State of AI Cybersecurity Report 2026.

AI is already embedded in day-to-day enterprise activity, with 78% of participants in one recent survey reporting that their organizations are using generative AI in at least one business function. Generative AI now acts as an always-on assistant, researcher, creator, and coach across an expanding array of departments and functions. Autonomous agents are performing multi-step operational workflows from end to end. AI features have been layered on top of every SaaS application. And vibe coding is making it possible for employees without deep technical expertise to build their own AI-powered automations.

According to Gartner, more than 80% of enterprises will have deployed GenAI models, applications, or APIs in production environments by the end of this year, up from less than 5% in 2023. Companies report a 130% increase in spending on AI over the same period, with 72% of business leaders using AI tools at least weekly. The outsized efficiency and productivity gains that were once a future vision are quickly becoming everyday reality.

AI is currently driving business growth and innovation, and organizations risk falling behind peers if they don’t keep up with the pace of adoption, but it is also quietly expanding the enterprise attack surface. The modern CISO is challenged to both enable innovation and protect the business from these emerging threats.

AI agents introduce new risks and vulnerabilities

AI agents are playing growing roles in enterprise production environments. In many cases, these agents act with broad permissions across multiple software systems and platforms. This means they’re granted far-reaching access – to sensitive data, business-critical applications, tokens and APIs, and IT and security tools. With this access comes risk for security leaders – 92% are concerned about the use of AI agents across the workforce and their impact on security.

These agents must be governed as identities, with least-privilege access and ongoing monitoring. They can’t be thought of as invisible aspects of the application estate. Understanding how AI agents behave, and how to manage their permissions, control their behavior, and limit their data access will be a top security priority throughout 2026.

Generative AI prompts: The next frontier

Prompts are how users – both human and agentic – interact with AI systems, and they’re where natural language gets translated into model behavior. Natural language is infinite in its potential combinations and permutations, making this aspect of the attack surface open-ended and far more complex than traditional CVEs. With carefully crafted prompts, bad actors may be able to coax models into disclosing sensitive data, bypassing guardrails, or initiating undesirable actions.

Among security leaders, the biggest worries about AI usage in their environments all involve ways that systems might be manipulated to bypass traditional controls.

61% are most concerned about the exposure of sensitive data
56% are most concerned about potential data security and policy violations
51% are most concerned about the misuse or abuse of AI tools

The more employees rely on AI in their day-to-day workflows, the more critical it becomes for security teams to understand how prompt behavior determines model behavior – and where that behavior could go wrong.

What does “securing AI” mean in practice?

AI adoption opens new security risks that blur the boundaries between traditional security disciplines. A single malicious interaction with an AI model could involve identity misuse, sensitive data exposure, application logic abuse, and supply chain risk – all within a single workflow. Protecting this dynamic and rapidly evolving attack surface requires an approach that spans identity security, cloud security, application security, data security, software development security, and more.

The task for security leaders is to implement the tools, policies, and frameworks to mitigate these novel, expansive, and cross-disciplinary risks.

However, within most enterprises, AI policy creation remains in its infancy. Just 37% of security leaders report that their organization has a formal AI policy, representing a small but worrisome decrease from last year. Conversations about AI abound: in 52% of organizations, there’s discussion about an AI policy. Still, talk is cheap, and leaders will need to take action if they’re to successfully enable secure AI innovation.

To govern and protect their AI systems, organizations must take a multi-pronged approach. This requires building out policies, but it also demands that they are able to:

Monitor the prompts driving GenAI assistants and agents in real time. Organizations must be able to inspect prompts, sessions, and responses across enterprise GenAI tools, low- and high-code environments, and SaaS and SASE so that they can detect clever conversational prompt attacks and malicious chaining.
Secure all business AI agent identities. Security teams need to identify all the agents acting within their environment and supply chain, map their connections and interactions via MCP and services like Amazon S3, and audit their behavior across the cloud, SaaS environments, and on the network and endpoint devices.
Maintain centralized, comprehensive visibility. Understanding intent, assessing risks, and enforcing policies all require that security teams have a single view that spans AI interactions across the entire business.
Discover and control shadow AI. Teams need to be able to identify unsanctioned AI activities, distinguish the misuse of legitimate tools from their appropriate use, and apply policies to protect data, while guiding users towards approved solutions.

Scaling AI safely and responsibly

The approach that most cybersecurity vendors have taken – using historical patterns to predict future threats – doesn’t work well for AI systems. Because AI changes its behavior in response to the information it encounters while taking action, previous patterns don’t indicate what it will do next. Looking at past attacks can’t tell you how complex models will behave in your individual business.

Securing AI requires interpreting ambiguous interactions, uncovering subtleties that reveal intent within extended conversations, understanding how access accumulates over time, and recognizing when behavior – both human and machine – begins to drift towards areas of risk. To do this, you need to understand what “normal” looks like in each unique organization: how users, systems, applications, and AI agents behave, how they communicate, and how data flows between them.

Darktrace has spent more than a decade designing AI-powered solutions that can understand and adapt to evolving behavior in complex environments. This technology learns directly from the environment it protects, identifying malicious actions that deviate from normal operations, so that it can stop AI-related threats on the very first encounter.

As AI adoption reshapes enterprise operations, humans and machines will collaborate more and more often. This collaboration might dramatically expand the attack surface, but it also has the potential to be a force multiplier for defenders.

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Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.

Learn more about securing AI in your enterprise.

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Check out this article by Darktrace: Employee-Conscious Email Security Solutions in the Workforce

Employee-Conscious Email Security Solutions in the Workforce

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.

Finding a Balance of Employee Involvement in Security Strategies

Reducing False Reports While Improving Security Awareness Training

Leveraging AI to Generate Productivity Gains

Preventing Email Mishaps: How to Deal with Human Error

Email Security Informed by Employee Experience

Phantom Footprints: Tracking GhostSocks Malware

State of AI Cybersecurity 2026: 92% of security professionals concerned about the impact of AI agents

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Darktrace Unites Human Behavior and Threat Detection Across Email, Slack, Teams, and Zoom

Why Organizations are Moving to Label-free, Behavioral DLP for Outbound Email

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

Blog

Network

March 26, 2026

Phantom Footprints: Tracking GhostSocks Malware

Why are attackers using residential proxies?

What is GhostSocks?

How does Ghostsocks malware work?

How does Ghostsocks malware evade detection?

Darktrace’s detection of GhostSocks Malware

Cyber AI Analyst’s investigation

Conclusion

Appendices

References

Darktrace Model Detections

MITRE ATT&CK Mapping

List of Indicators of Compromise (IoCs)

Blog

AI

March 27, 2026

State of AI Cybersecurity 2026: 92% of security professionals concerned about the impact of AI agents

AI agents introduce new risks and vulnerabilities

Generative AI prompts: The next frontier

What does “securing AI” mean in practice?

Scaling AI safely and responsibly