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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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August 21, 2026

AI Agents: Securing the Path from Intent to Action

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The UK’s National Cyber Security Centre (NCSC) recently published guidance on managing the cyber risk of agentic AI. While the document is framed as interim advice as more formal guidance is developed, the framing reflects the current state of the industry: organizations are already deploying agents into production environments while standards, controls, and operating models for autonomous systems remain unsettled. Governance is evolving alongside adoption rather than preceding it, a reality which underscores the importance of robust controls.  

The NCSC’s guidance recommends aligning controls to an agent's level of autonomy, assigning distinct identities, limiting permissions, constraining access to systems and data, monitoring activity, maintaining human oversight, and preserving the ability to intervene when necessary. Most of these recommendations will sound familiar to security teams. The challenge is not the novelty of the controls. It is the type of system those controls now need to govern.

The shift from model security to agent security

For several years, AI security discussions have focused heavily on models. Can a model be manipulated? Jailbroken? Trusted? Can it expose information it should not? Those questions remain important, but they capture only part of the problem. A model generating text is one thing. A system connected to identities, applications, tools, workflows, and business data is another.

The difference becomes clearer when comparing a chatbot that answers questions with an agent that can retrieve customer records, update tickets, invoke tools, trigger workflows, and interact with external systems. The underlying model may be identical. Its access is not. The security question begins to shift from what the model knows to what the system can do.

The same theme appears in the Five Eyes statement released earlier this year, describing AI as a force multiplier that is accelerating both offensive and defensive cyber operations. The NCSC guidance explores what that reality looks like when autonomous systems begin operating inside enterprise environments.

Securing AI agents in operation

The NCSC spends relatively little time debating model behavior and considerably more time discussing identity, permissions, monitoring, oversight, containment, and response. Agents are treated as participants within an environment rather than isolated pieces of technology.  

That's broadly consistent with how we think about the problem at Darktrace.

An agent should not be treated as an extension of a user account. It develops its own behavioral patterns. It accesses systems, interacts with data, invokes tools, and moves across workflows in ways that can be observed independently. Understanding what an agent is permitted to do matters. Understanding how it actually behaves once deployed, and whether that behavior aligns with business intent, matters just as much.

Identity provides an obvious example. The NCSC recommends assigning distinct identities to agents rather than allowing them to disappear into surrounding human or service accounts. Most importantly, assigning agents distinct identities enables independent behavioral monitoring.

Development assumptions vs. real-world behavior

The same principle extends to monitoring. NCSC guidance places agent activity within normal security operations rather than treating it as a separate AI governance function. Many of the controls described are put in place before an agent begins operating. Sandboxing, credential design, approval workflows and human oversight all reflect judgments about how the system is expected to behave and what risks it is likely to create.

Actual use may challenge those assumptions. Access patterns change. Workflows expand. Systems begin interacting with resources they have never touched before. Processes that appeared reasonable during design behave differently in production. Human oversight requirements may turn out to be either excessive or inadequate once the system is operating at scale and operating within the context of unique business processes.

The Five Eyes statement points to a similar issue: organizations need confidence that controls continue to work as intended once systems are exposed to real users, data, tools and operational pressures. Often, the question is not whether an agent is technically allowed to perform an action, but whether its behavior remains consistent with the role it was intended to play.

Monitoring and governance of AI agents go hand-in-hand

This problem is exactly why monitoring and governance should be treated as part of the same process. Governance sets the initial parameters for deployment, while monitoring provides evidence about whether those parameters remain appropriate. That evidence should, in turn, inform changes to permissions, controls and oversight.

This matters increasingly as autonomous systems are integrated into business processes. The relevant risk is shaped not only by the model or agent itself, but by what it can access, what actions it can take, and how its behavior changes in practice.

Developing continuous oversight of AI agent behavior

The implication is clear: governance cannot end at deployment. Organizations need a way to understand how agents behave after deployment, test whether controls remain appropriate, and adjust them as conditions change. That requires visibility not just into technical activity, but into whether that activity makes sense in the context of the business process the agent is intended to support.

This is where business-centric behavioral security can become critical. Risk does not emerge from the model itself: it emerges from the actions an autonomous system takes within the enterprise and the downstream consequences of those actions.  

An agent can operate exactly as intended and still create risk if it accesses sensitive information in an unexpected context, exercises permissions in ways that create unintended exposure, or influences business processes in ways that were not anticipated during design and review.

Traditional governance vs. behavioral analytics

Traditional governance frameworks provide assurance at a point in time. Behavioral security can provide ongoing visibility into how autonomous systems interact with the organization they are meant to serve. Rather than focusing exclusively on model performance or policy compliance, organizations need to understand whether an agent's behavior aligns with business intent, operational expectations, and acceptable risk tolerances as conditions change.

As enterprises move from isolated AI deployments to interconnected ecosystems of agents, visibility into behavior becomes as important as visibility into code. Governance determines what an autonomous system is permitted to do. Behavioral analytics helps determine what it is doing, what business outcomes it is producing, and whether those outcomes remain aligned with the organization's objectives.

[related-resource]

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About the author
Margaret Cunningham, PhD
VP, Security & AI Strategy, Field CISO

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August 19, 2026

When AI Becomes the Lure: A Fake Gemini Installer Delivers Vidar

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Key takeaways

  • Darktrace observed a customer download a fake Google Gemini installer hosted on Google Colab, resulting in the execution of the Vidar information stealer.
  • Darktrace identified the compromise through behavioral indicators, including suspicious process activity, anomalous network communications, and indicators of credential theft, before autonomously containing the threat.
  • The incident highlights how threat actors are increasingly exploiting trusted platforms and a growing interest in AI tools to distribute malware through seemingly legitimate software acquisition workflows.

The Growing Abuse of Generative AI

As organizations are increasingly adopting generative AI tools into their daily workflows, attackers are adapting their distribution methods accordingly too. As part of their day-to-day work, users are now searching for AI assistants, programming tools, browser extensions, desktop applications, and productivity integrations.

Recent reports have highlighted campaigns that use fake AI software and AI-related installers to distribute malware and steal credentials [1]. Researchers have documented campaigns that exploit fake AI-themed websites and services to distribute information stealers and backdoors [2]. Security researchers have also observed attackers disguising malware as legitimate installers for AI software to increase the likelihood of victim interaction and execution [3].

In July 2026, Darktrace observed one such case within a customer environment in the Europe, Middle East and Africa (EMEA) region, where attackers used a fake generative AI installer to deliver the prolific information stealer Vidar. This incident highlights how threat actors are exploiting interest in AI services to distribute established malware using increasingly convincing social engineering techniques.

How a Fake Gemini Installer Delivered Vidar

Initial Access: From Search Result to Malware Download

Unlike many malware campaigns that begin with a phishing email, this activity appears to have originated from a user searching for and downloading software.

Darktrace first observed unusual activity on the customer network after a suspicious executable file was launched from a user’s Download folder. Further investigation revealed that the file purported to be a Google Gemini installer and was named “Download_Google_Gemini_For_Windows.exe”.

During the initial analysis, it was noted that the top search result for the suspicious filename associated pointed to a file hosted on Google Colab, a cloud-based Jupyter notebook platform, commonly used by developers, researchers, and data scientists to run code and machine learning workloads through a web browser. By leveraging another trusted Google platform, the attacker increased the likelihood that users would perceive the download as legitimate, making the lure more convincing to those searching for Gemini-related software.

Figure 1: The Google Colab page containing a download prompt for the fake Google Gemini installer.

Further investigation of the Google Colab page revealed that the download prompt redirected users to a secondary site, hxxps://micronsoftwares[.]com, which posed as a "Windows Software Hub" download page and offered the fake Gemini installer for download.

Figure 2: The secondary website posing as a "Windows Software Hub" download page, which likely hosted the fake Gemini installer.

While the investigation did not uncover any HTTP or file-download telemetry data that conclusively identified the download source, SSL communication sessions with Google Colab were detected immediately before the suspicious file was executed. The timing of these connections suggests that the user interacted with the Colab resource before being redirected to the secondary site from which the executable was downloaded.

The user was not simply tricked into opening an email attachment; instead, the attacker embedded malicious content into a process many users would consider entirely legitimate: searching for and downloading software associated with a trusted platform.

Weaponizing Trusted Platforms

At the time of review (July 15, 2026), Darktrace's Threat Research team confirmed that the Google Colab page was still active and prompting users to download a ZIP archive containing the binary file.

The archive also appeared to contain a README file instructing users to run the binary file with administrator privileges and add it to their antivirus software’s exception lists. These instructions suggest that the campaign relied heavily on social engineering, convincing users to take actions that would facilitate malware execution and potentially bypass security checks.

The use of a legitimate platform also complicates the user’s decision-making. Downloads associated with a trusted service are often perceived as less suspicious than those hosted on unfamiliar domains. When combined with the branding of a widely used AI tool, the lure becomes even more convincing.

Malware Analysis

Darktrace’s Threat Research team identified the executable file as the information-stealing malware Vidar. Analysis revealed that the binary file was a newer Go-compiled variant that communicated with Telegram-based infrastructure. Darktrace’s researchers also identified dtm[.]kijangturbo88[.]top as a command-and-control (C2) endpoint associated with the activity. While the malware itself was not novel, the lure and delivery mechanism was.

For a deeper look at the information stealer, see Darktrace’s 2023 analysis of Vidar.

Figure 3: Darktrace’s detection of the unusual outbound connection associated with the fake Gemini installer.

Shortly after execution, the process established communications with the external IP address 91.98.98[.]86 via port 443, directly linking the executable to suspicious network activity observed on the device. Subsequent open-source intelligence (OSINT) analysis of the revealed multiple malicious associations [5].

Additional Darktrace detections included unusual SSL activity from the affected device. Analysis of related SSL telemetry identified 91.98.111[.]49 as additional infrastructure associated  with the activity [6].

Subsequent alerts from the customer's Microsoft Defender for Endpoint integration later confirmed activity consistent with the theft of browser credentials and other sensitive data from the affected endpoint.

Taken together, these detections provided a clear picture of the attack, from the execution of a suspicious file and unusual network connections to indicators of C2 activity and credential theft.

Figure 4: Darktrace’s detection of anomalous activity following the execution of the fake Gemini installer, seen in the Model Alert Event Log.

Darktrace's Autonomous Response

Following the detection, Darktrace’s Autonomous Response took immediate containment action, including blocking communication with suspicious external infrastructure, including 91.98.98[.]86, and quarantining the compromised device.

Despite the apparent legitimacy of the activity, with the installer hosted on a trusted platform and resembling a routine software download, Darktrace was able to detect and contain the attack because the device's behavior deviated from its normal pattern.

Figure 5: Automated containment actions implemented by Darktrace's Autonomous Response following the detection of activity associated with the fake Gemini installer.

Conclusion

This investigation highlights how threat actors continue to adapt established malware delivery techniques to emerging technology trends. While the malware itself was not new, the distribution method was. By disguising Vidar as a Google Gemini installer and hosting the malicious content on a trusted platform, the attack aligned its lure with a growing behavioral trend: users actively searching for AI tools and services as part of their day-to-day work.

Although fake installers are not a new phenomenon, the rapid rise of generative AI has created new opportunities for threat actors. Rather than relying solely on traditional delivery methods, attackers can now target users who are actively searching for AI applications. As AI adoption continues to accelerate across enterprise environments, organizations should remain alert to campaigns that exploit this interest through fake applications, malicious websites, manipulated search results, the misuse of trusted platforms, and AI-themed social engineering.

Credit to Rushanth Ramanathan (Cyber Analyst) Joanna Ng (Detection Engineer)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

  • Security Integration / C2 Activity and Integration Detection
  • Endpoint / New Suspicious Executable Launched
  • Endpoint / Process Connection / Unusual Connection from New Process
  • Anomalous Connection / Rare External SSL Self-Signed
  • Security Integration / High Severity Integration Detection
  • Antigena / Network / Significant Anomaly /  Antigena Significant Security Integration and Network Activity Block

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

List of Indicators of Compromise (IoCs)

IoC Type Description
Download_Google_Gemini_For_Windows.exe File Fake Gemini-themed installer observed during the investigation.
GoogleAppInstaller.exe File Related executable identified through endpoint telemetry.
91.98.98[.]86 IP Address External destination contacted by the malicious executable.
91.98.111[.]49 IP Address Related infrastructure identified through SSL certificate pivoting.
dtm[.]kijangturbo88[.]top Domain Command-and-control endpoint identified during malware analysis.
1e13c2c9eac72daf63fd00a9946878949e159ae6ec51b54ec64f942d79d61913 SHA256 Malware sample associated with the fake Gemini installer.

MITRE ATT@CK Mapping


MITRE ATT&CK Mapping Tactic Technique
Initial Access T1204 User Execution
Execution T1204.002 User Execution: Malicious File
Defence Evasion T1036 Masquerading
Credential Access T1555 Credentials from Password Stores
Credential Access T1555.003 Credentials from Web Browsers
Command and Control T1071 Application Layer Protocol
Exfiltration T1041 Exfiltration Over C2 Channel
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About the author
Rushanth Ramanathan
Cyber Analyst
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