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September 30, 2025

Out of Character: Detecting Vendor Compromise and Trusted Relationship Abuse with Darktrace

Phishing emails from compromised vendors are increasingly difficult to distinguish from genuine correspondence. They challenge workers, security teams and traditional email SEGs alike. Anomaly detection can be a game-changer in spotting the subtle signs of these meticulous attacks.
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
Jennifer Beckett
Cyber Analyst
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30
Sep 2025

What is Vendor Email Compromise?

Vendor Email Compromise (VEC) refers to an attack where actors breach a third-party provider to exploit their access, relationships, or systems for malicious purposes. The initially compromised entities are often the target’s existing partners, though this can extend to any organization or individual the target is likely to trust.

It sits at the intersection of supply chain attacks and business email compromise (BEC), blending technical exploitation with trust-based deception. Attackers often infiltrate existing conversations, leveraging AI to mimic tone and avoid common spelling and grammar pitfalls. Malicious content is typically hosted on otherwise reputable file sharing platforms, meaning any shared links initially seem harmless.

While techniques to achieve initial access may have evolved, the goals remain familiar. Threat actors harvest credentials, launch subsequent phishing campaigns, attempt to redirect invoice payments for financial gain, and exfiltrate sensitive corporate data.

Why traditional defenses fall short

These subtle and sophisticated email attacks pose unique challenges for defenders. Few busy people would treat an ongoing conversation with a trusted contact with the same level of suspicion as an email from the CEO requesting ‘URGENT ASSISTANCE!’ Unfortunately, many traditional secure email gateways (SEGs) struggle with this too. Detecting an out-of-character email, when it does not obviously appear out of character, is a complex challenge. It’s hardly surprising, then, that 83% of organizations have experienced a security incident involving third-party vendors [1].  

This article explores how Darktrace detected four different vendor compromise campaigns for a single customer, within a two-week period in 2025.  Darktrace / EMAIL successfully identified the subtle indicators that these seemingly benign emails from trusted senders were, in fact, malicious. Due to the configuration of Darktrace / EMAIL in this customer’s environment, it was unable to take action against the malicious emails. However, if fully enabled to take Autonomous Response, it would have held all offending emails identified.

How does Darktrace detect vendor compromise?

The answer lies at the core of how Darktrace operates: anomaly detection. Rather than relying on known malicious rules or signatures, Darktrace learns what

‘normal’ looks like for an environment, then looks for anomalies across a wide range of metrics. Despite the resourcefulness of the threat actors involved in this case, Darktrace identified many anomalies across these campaigns.

Different campaigns, common traits

A wide variety of approaches was observed. Individuals, shared mailboxes and external contractors were all targeted. Two emails originated from compromised current vendors, while two came from unknown compromised organizations - one in an associated industry. The sender organizations were either familiar or, at the very least, professional in appearance, with no unusual alphanumeric strings or suspicious top-level domains (TLDs). Subject line, such as “New Approved Statement From [REDACTED]” and “[REDACTED] - Proposal Document” appeared unremarkable and were not designed to provoke heightened emotions like typical social engineering or BEC attempts.

All emails had been given a Microsoft Spam Confidence Level of 1, indicating Microsoft did not consider them to be spam or malicious [2]. They also passed authentication checks (including SPF, and in some cases DKIM and DMARC), meaning they appeared to originate from an authentic source for the sender domain and had not been tampered with in transit.  

All observed phishing emails contained a link hosted on a legitimate and commonly used file-sharing site. These sites were often convincingly themed, frequently featuring the name of a trusted vendor either on the page or within the URL, to appear authentic and avoid raising suspicion. However, these links served only as the initial step in a more complex, multi-stage phishing process.

Figure 1: A legitimate file sharing site used in phishing emails to host a secondary malicious link.

Figure 2: Another example of a legitimate file sharing endpoint sent in a phishing email and used to host a malicious link.

If followed, the recipient would be redirected, sometimes via CAPTCHA, to fake Microsoft login pages designed to capturing credentials, namely http://pub-ac94c05b39aa4f75ad1df88d384932b8.r2[.]dev/offline[.]html and https://s3.us-east-1.amazonaws[.]com/s3cure0line-0365cql0.19db86c3-b2b9-44cc-b339-36da233a3be2ml0qin/s3cccql0.19db86c3-b2b9-44cc-b339-36da233a3be2%26l0qn[.]html#.

The latter made use of homoglyphs to deceive the user, with a link referencing ‘s3cure0line’, rather than ‘secureonline’. Post-incident investigation using open-source intelligence (OSINT) confirmed that the domains were linked to malicious phishing endpoints [3] [4].

Figure 3: Fake Microsoft login page designed to harvest credentials.

Figure 4: Phishing kit with likely AI-generated image, designed to harvest user credentials. The URL uses ‘s3cure0line’ instead of ‘secureonline’, a subtle misspelling intended to deceive users.

Darktrace Anomaly Detection

Some senders were unknown to the network, with no previous outbound or inbound emails. Some had sent the email to multiple undisclosed recipients using BCC, an unusual behavior for a new sender.  

Where the sender organization was an existing vendor, Darktrace recognized out-of-character behavior, in this case it was the first time a link to a particular file-sharing site had been shared. Often the links themselves exhibited anomalies, either being unusually prominent or hidden altogether - masked by text or a clickable image.

Crucially, Darktrace / EMAIL is able to identify malicious links at the time of processing the emails, without needing to visit the URLs or analyze the destination endpoints, meaning even the most convincing phishing pages cannot evade detection – meaning even the most convincing phishing emails cannot evade detection. This sets it apart from many competitors who rely on crawling the endpoints present in emails. This, among other things, risks disruption to user experience, such as unsubscribing them from emails, for instance.

Darktrace was also able to determine that the malicious emails originated from a compromised mailbox, using a series of behavioral and contextual metrics to make the identification. Upon analysis of the emails, Darktrace autonomously assigned several contextual tags to highlight their concerning elements, indicating that the messages contained phishing links, were likely sent from a compromised account, and originated from a known correspondent exhibiting out-of-character behavior.

Figure 5: Tags assigned to offending emails by Darktrace / EMAIL.

Figure 6: A summary of the anomalous email, confirming that it contained a highly suspicious link.

Out-of-character behavior caught in real-time

In another customer environment around the same time Darktrace / EMAIL detected multiple emails with carefully crafted, contextually appropriate subject lines sent from an established correspondent being sent to 30 different recipients. In many cases, the attacker hijacked existing threads and inserted their malicious emails into an ongoing conversation in an effort to blend in and avoid detection. As in the previous, the attacker leveraged a well-known service, this time ClickFunnels, to host a document containing another malicious link. Once again, they were assigned a Microsoft Spam Confidence Level of 1, indicating that they were not considered malicious.

Figure 7: The legitimate ClickFunnels page used to host a malicious phishing link.

This time, however, the customer had Darktrace / EMAIL fully enabled to take Autonomous Response against suspicious emails. As a result, when Darktrace detected the out-of-character behavior, specifically, the sharing of a link to a previously unused file-sharing domain, and identified the likely malicious intent of the message, it held the email, preventing it from reaching recipients’ inboxes and effectively shutting down the attack.

Figure 8: Darktrace / EMAIL’s detection of malicious emails inserted into an existing thread.*

*To preserve anonymity, all real customer names, email addresses, and other identifying details have been redacted and replaced with fictitious placeholders.

Legitimate messages in the conversation were assigned an Anomaly Score of 0, while the newly inserted malicious emails identified and were flagged with the maximum score of 100.

Key takeaways for defenders

Phishing remains big business, and as the landscape evolves, today’s campaigns often look very different from earlier versions. As with network-based attacks, threat actors are increasingly leveraging legitimate tools and exploiting trusted relationships to carry out their malicious goals, often staying under the radar of security teams and traditional email defenses.

As attackers continue to exploit trusted relationships between organizations and their third-party associates, security teams must remain vigilant to unexpected or suspicious email activity. Protecting the digital estate requires an email solution capable of identifying malicious characteristics, even when they originate from otherwise trusted senders.

Credit to Jennifer Beckett (Cyber Analyst), Patrick Anjos (Senior Cyber Analyst), Ryan Traill (Analyst Content Lead), Kiri Addison (Director of Product)

Appendices

IoC - Type - Description + Confidence  

- http://pub-ac94c05b39aa4f75ad1df88d384932b8.r2[.]dev/offline[.]html#p – fake Microsoft login page

- https://s3.us-east-1.amazonaws[.]com/s3cure0line-0365cql0.19db86c3-b2b9-44cc-b339-36da233a3be2ml0qin/s3cccql0.19db86c3-b2b9-44cc-b339-36da233a3be2%26l0qn[.]html# - link to domain used in homoglyph attack

MITRE ATT&CK Mapping  

Tactic – Technique – Sub-Technique  

Initial Access - Phishing – (T1566)  

References

1.     https://gitnux.org/third-party-risk-statistics/

2.     https://learn.microsoft.com/en-us/defender-office-365/anti-spam-spam-confidence-level-scl-about

3.     https://www.virustotal.com/gui/url/5df9aae8f78445a590f674d7b64c69630c1473c294ce5337d73732c03ab7fca2/detection

4.     https://www.virustotal.com/gui/url/695d0d173d1bd4755eb79952704e3f2f2b87d1a08e2ec660b98a4cc65f6b2577/details

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content

The Darktrace / EMAIL Solution Brief

Learn more about how Darktrace / EMAIL stops block novel threats up to 13 days earlier than other tools

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
Jennifer Beckett
Cyber Analyst

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June 26, 2026

How Darktrace Transformed Cybersecurity at Our Health Center: A CIO’s Perspective

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How Darktrace Transformed Cybersecurity at Our Health Center: A CIO’s Perspective

In my role as CIO, I bring years of experience leading IT for healthcare organizations. I’ve seen firsthand the unique cybersecurity challenges that nonprofit health centers face: limited budgets, small IT teams, and the constant pressure to prioritize patient care over technology investments. Yet, the threat landscape for health is relentless, and the stakes for protecting patient data and ensuring operational continuity have never been higher. It’s a balancing act.

The search for a better solution

Like many nonprofits, organizations I work at start with Microsoft’s security stack. The discounted pricing for nonprofits makes it an obvious choice, and Microsoft Defender provided a solid foundation for endpoint and email security. However, I quickly realized that relying on a single vendor, even one as robust as Microsoft, left gaps in our defenses. Cybersecurity is never one-size-fits-all, which is why my preference was to layer an additional solution on top of our native security to improve our security posture.

Teams needed a solution that could layer seamlessly on top of Microsoft, without adding complexity or draining limited resources. That’s when I found Darktrace. I had heard of their reputation after seeing how other organizations used Darktrace to secure their infrastructure and was impressed by their AI-native, agentless approach and agreed to a proof of value (POV).

Our goal was to elavate Microsoft with an additional layer of intelligence- one that could seamlessly integrate, operate autonomously, and support a small team without increasing overhead. We turned to Darktrace because its AI-native, agentless approach offered a fundamentally different way to detect and respond to threats, learning our environment in real time and filling gaps that traditional tools can miss. With a quick POV, we were able to validate how effectively Darktrace works alongside Microsoft to deliver a more complete and resilient security architecture.

Why Darktrace stood out

From the start, Darktrace differentiated itself in several critical ways:

  • Deep visibility: Unlike other solutions that rely simply on host-based monitoring with endpoint agents, Darktrace operates passively at the network layer and integrates via APIs for email and identity security. This gave full visibility into network traffic that we previously didn’t have, going beyond our existing endpoint-based tools without adding additional maintenance overhead for our small IT team.
  • AI-native from the ground up: Darktrace wasn’t just layering AI on top of an existing product; it was built with AI at its core. Their autonomous detection and response to threats immediately reduced the need for constant human supervision. In a world where cyber-attacks are increasingly sophisticated and subtle, having an AI that learns our environment and adapts in real time is invaluable.
  • Comprehensive coverage: We started with a POV focused on email security, but quickly expanded to full deployment across our entire infrastructure. Darktrace’s products now protect our email, network, and identity layers, providing visibility and defense against lateral movement and abnormal behavior that traditional tools often miss.

Integration and workflow: Smooth and simple

One of the most impressive aspects of Darktrace is how easy it was to integrate into an existing environment. For network security, it was as simple as plugging an appliance into our top-of-rack switch – no downtime, no complex configuration. For email and identity, API integrations meant we could be up and running in hours, not weeks.

This simplicity extended to day-to-day operations. Our IT team received regular security reports, and any time we had questions or needed to adjust policies, Darktrace’s support team was there with white-glove service. Their responsiveness- even in the middle of the night- gave us confidence that we had true partners, not just a vendor.

Real-world impact: Threats stopped, time saved

The results spoke for themselves. During the time with Darktrace, I did not experience any security incidents. The team slept better at night knowing that Darktrace was monitoring for anomalies and proactively blocking suspicious activity, alerting us even before we noticed anything was wrong.

A memorable example was during an Electronic Health Record (EHR) upgrade, when my team forgot to adjust the policy in advance. Darktrace’s autonomous response was so effective that it blocked our upgrade activities- proof that nothing, not even internal changes, could slip by unnoticed. This level of vigilance meant that ransomware, data exfiltration attempts, or insider threats would be detected and contained before causing harm.

While I can’t share specific ROI numbers, the value was clear: we’ve avoided costly breaches, reduced the time spent investigating alerts, and eliminated the performance drag of agent-based tools. With Darktrace layered on top of Microsoft, I’ve hit the right balance of maximum protection with minimal spending. The cost of Darktrace / EMAIL was competitive, especially when factoring in the included Managed Detection and Response (MDR) service, which provides expert human oversight on top of the AI.

Key differentiators over the competition

  • Extending visibility beyond the endpoint: Traditional host-based monitoring solutions, such as EDR, play a critical role in securing individual devices. By adding a network detection and response (NDR) layer, we gained visibility into activity across our wider digital environment, surfacing threats that move laterally, operate between devices, or bypass endpoint controls. Darktrace also stood out for its ability to learn our normal patterns of behavior and identify subtle deviations in real time, not just known indicators of compromise. Because this is delivered through passive, non-disruptive monitoring, we were able to strengthen our defenses without adding complexity or impacting performance.
  • Layered security without complexity: Darktrace elevated our Microsoft foundation without creating conflicts or requiring us to disable existing protections. This layered approach maximized our security posture without adding operational burden.
  • Expert partnership: Beyond technology, Darktrace’s team acted as true partners, guiding us through deployment, providing ongoing support, and helping us interpret findings. This partnership was as valuable as the technology itself.

Advice for other nonprofits

If you’re an IT leader in a nonprofit, my advice is simple: look for solutions that are easy to deploy, intelligent in their response, and cost-effective. Don’t settle for more endpoint based tools that overlap with what you already have. Seek out a layered approach that covers your blind spots – especially at the network and email layers- at a price point that suits your organization.

Most importantly, don’t be afraid to evaluate new solutions. Even if you’re inundated with vendor pitches, you owe it to your organization to explore options that could save you time, money, and sleepless nights.

For organizations I work at, combining Microsoft’s security stack with Darktrace’s AI-native, platform struck the right balance between protection and practicality. We gained enterprise-grade security without sacrificing performance or stretching our budget. In the end, that meant more resources for what matters most: delivering care to our patients. If you’re facing similar challenges, I encourage you to consider how Darktrace could transform your security posture, and give your team the peace of mind they deserve.

For the organization I work in, combining Microsoft with Darktrace delivered a clear step-change in our security posture. Microsoft provided the foundation, while Darktrace’s behavioral intelligence added visibility into the unknown, surfacing emerging threats based on deviations in real-time activity, not just known indicators.

The result was enterprise-grade protection without added overhead, allowing us to stay focused on patient outcomes, not security operations. For organizations facing similar pressures, this layered approach offers a smarter, more efficient path to securing modern environments.

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About the author
Mice Chen
Chief Information Security Officer

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June 25, 2026

Shadow AI Detection: The First Step Toward Securing AI

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Why shadow AI is emerging  

Imagine you’re an employee under pressure, deadlines stacking up, repetitive tasks piling higher by the day. You find a free AI tool online that promises to automate the work in seconds; no approvals are needed. It feels like a simple win, paste in some data, write a quick prompt, and move faster.

But in that moment, something changed.  

Sensitive customer information is entered into a tool your organization doesn’t monitor, doesn’t govern, and can’t see and suddenly, that data is no longer where it should be, and no one knows where it’s gone.

This is the reality of Shadow AI: employees using unsanctioned AI tools to move faster, while unintentionally creating risk that exists entirely outside visibility and control.  

This is not just a one off case, research across businesses indicate that nearly half of employees report using unsanctioned AI tools, often prioritizing speed and productivity over security. Additionally, 51% of employees report connecting AI tools to work systems or apps without IT approval, creating significant operational risk where the average cost of security incidents in organizations with a high level of shadow AI usage can reach $670k.

While shadow AI is often top of mind for security professionals, it is just one component of how AI use can increase risk. Understanding and managing shadow AI use should be considered as part of a broader, comprehensive risk management strategy that aims to secure AI systems, including human and agent identities, interactions, human-AI partnerships, and behaviors operating across the digital enterprise from visibility and governance through detection, response, and recovery.  

Effective risk management calls for a layered and interdisciplinary strategy. It requires addressing issues across governance and visibility; identity, access and agent control, data security and privacy, secure MLOps / LLMOps, runtime security, behavior-based detection, autonomous response and recovery.  

This blog explores a specific governance and visibility use case linked to shadow AI and reveals the challenges it presents as well as the defensive strategies that security teams can adopt.

Why shadow AI is hard to detect  

When it comes to AI, what organizations can easily see does not always reflect the full scope of AI activity occurring within the tools, applications, and workflows used across an enterprise. As a result, organizations using traditional rule-based methods to flag unusual activity may struggle to distinguish unsanctioned AI usage from legitimate operational behavior, particularly as SaaS applications, APIs, and orchestration layers increasingly have AI embedded into normal business workflows. Identifying threats using previously observed intelligence or depending on hard to maintain allow and block lists does not provide a dynamic enough strategy to manage risk. Also, many organizations are focusing on identifying Shadow AI in their governed infrastructure, like gateways, endpoints, or SASE, which is foundational. But, organizations require visibility and Shadow AI detection across all networked infrastructure from on-prem, hybrid, data centers, and cloud infrastructure that may not have endpoint agent visibility. This uncovers the utilization of MCP, data flows, and autonomous agents across these domains.

For example, employees interact with AI assistants across approved SaaS platforms every day. However, browser extensions and other types of plug-ins can route prompts that include enterprise data to embedded AI services in ways that are not visible to the security team. AI enabled workflows may invoke multiple APIs, orchestration layers, and cloud services behind the scenes, making it difficult for traditional security tooling to determine where data is processed, stored, or retransmitted. Because much of this activity occurs within trusted browser sessions and encrypted SaaS traffic, conventional network monitoring, DLP, and application allowlisting controls often lack the context needed to accurately identify or govern these interactions

Identifying AI tools in the environment is one part of the equation. Understanding the behavior surrounding their use is where the real challenge lies. An AI application is not inherently risky, but the way users or other assets interact with it may be. Sensitive data exposure, abnormal access patterns, and misuse of AI-assisted workflows often appear legitimate in isolation and only become visible through behavioral analysis across the broader environment.  

What Shadow AI visibility does and doesn’t show

Comprehensive Shadow AI visibility allows organizations to answer several important questions:

  • What types of AI are we using? What AI platforms, agents, MCP clients/servers, and services are active across the enterprise?  
  • Who is using AI services? Which users, business units, or systems are interacting with those AI services?  
  • Is our data safe? Is sensitive or regulated data being exposed through prompts, workflows, or integrations?  
  • Are AI systems behaving as expected? Are AI systems behaving anomalously or operating outside approved governance processes?  
  • Are our AI systems under attack? Is an attacker attempting to manipulate prompts, influence agent behavior, or abuse AI-enabled workflows?

Answering these questions is foundational to broader AI governance efforts. However, it is limited to helping teams understand initial interactions and fails to offer insight into dependencies and outcomes that are critical to securing AI across an enterprise.  

Deeper visibility that includes the ability to understand dependencies and outcomes are not always available in AI security point products. Answering the questions below requires understanding runtime behavior and operational outcomes:  

  • What actions did the AI interaction trigger?  
  • What systems, applications, or data did it access? Did the AI operate beyond its intended permissions or scope?  
  • Could a low-risk interaction lead to high-risk outcomes?  
  • What is the risk and context understanding of an anomalous activity to assist in prioritization of analysis and autonomous response action?

The distinction between these two sets of questions offers two different layers of AI security. The first set of questions focuses on discovery and interaction visibility. The second set focuses on providing visibility that includes the context and outcomes that are critical for managing follow-on risks associated with obfuscated downstream activities.  

Together, these layers help organizations move beyond simply identifying AI usage toward understanding how AI behaves operationally across the enterprise.

How organizations are addressing shadow AI

Most organizations still approach shadow AI as an application control problem, relying on policies, browser restrictions, and allow/block lists. However, AI adoption is evolving faster than most governance processes can realistically keep pace with. New assistants, plugins, and embedded AI features appear continuously, creating pressure to enable business productivity while simultaneously containing risk.  

Existing governance processes were designed for a more traditional SaaS adoption cycle, where new applications could be reviewed, approved, and monitored over longer time horizons. AI adoption operates differently. New capabilities can appear overnight inside existing platforms employees already use, making it difficult for security and governance teams to maintain an accurate understanding of enterprise AI exposure. This means that many organizations are experiencing significant operational overhead, particularly in large environments where AI usage is decentralized across teams, departments, and third-party services.  

Where should organizations start when securing their AI systems?

Shadow AI identification is an on-going critical component for AI Risk/Governance Boards as well as security organizations. As organizations seek AI certifications like ISO 42001 AI Management Systems, visibility into all AI adoption from enterprise use to custom innovation and development is crucial. Shadow AI identification provides organizations with the visibility needed to decide whether an AI tool should be brought into governed environments to reduce data loss (DLP) risks or whether policies should be established and enforced to restrict their use.

As organizations rapidly innovate and adopt AI, they are taking on more and more risk. Organizations need to have a strategy in place to mitigate the assumed risk, especially with third-party adoption. Visibility, monitoring, governance enforcement, behavioral-based detection of non-deterministic systems, and autonomous investigation and containment becomes critical to mitigating the risk of AI systems.  

How Darktrace secures AI and shadow AI

Attackers are using AI to move faster, scale tactics, and make threats more adaptive and convincing. Internally, organizations are grappling with new forms of risk created by generative AI, autonomous agents, shadow AI, and increasingly complex digital environments.

Darktrace helps organizations protect both people and AI in a world where AI is now central to how business gets done. Darktrace / SECURE AI helps organizations discover and control shadow AI by surfacing unsanctioned or unexpected AI activity where it appears – including MCP detections, distinguishing misuse of legitimate tools and unapproved services, and applying policy to contain data exposure while guiding users toward sanctioned options.

Stay up to date on AI security

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

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About the author
Nicole Carignan
SVP, Security & AI Strategy, Field CISO
Your data. Our AI.
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