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July 11, 2023

Detecting and Responding to Vendor Email Compromises (VEC)

Learn how Darktrace detected and responded to a March 2023 Vendor Email Compromise (VEC) attacks on customer in the energy industry. Read more here!
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
Tiana Kelly
Deputy Team Lead, London & Cyber Analyst
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11
Jul 2023

Threat Trends: Email Landscape

As organizations and security teams around the world continue to improve their cyber hygiene and strengthen the defenses of their digital environments, threat actors are being forced to adapt and employ more advanced, sophisticated attack methods to achieve their goals.

Vendor Email Compromise (VEC) is one such elaborate and sophisticated type of Business Email Compromise (BEC) attack which exploits pre-existing trusted business relationships to impersonate vendors, with the goal of launching a targeted attack on the vendor’s customers [1].  

In March 2023, Darktrace/Email™ detected an example of a VEC attack on the network of a customer in the energy sector. Darktrace’s Self-Learning AI worked to successfully neutralize the VEC attack before it was able to take hold, by blocking the malicious emails so that they did not reach the inboxes of the intended recipients.

Business Email Compromise (BEC)

BEC is the practice of using deceitful emails to trick an organization into transferring funds or divulging sensitive information to a malicious actor. BEC attacks can have devastating financial consequences for organizations, with the FBI reporting a total of USD 2.7 billion in losses from BEC attacks in 2022 [2].  Along with ransomware attacks, BEC attacks are one of the greatest cyber threats facing organizations.

Vendor Email Compromise (VEC)

VEC represents a “new milestone in the evolution of BEC attacks” having taken BEC attacks “to a whole new level of sophistication” [3]. Traditional BEC attacks involve the impersonation of an upper or middle-management employee by a cybercriminal, who attempts to trick a senior executive or employee with access to the company’s finances into transferring funds [4]. Thus, they are crafted to target a specific individual within an organization.

On the other hand, VEC attack campaigns take this attack style even further as they tend to require a greater understanding of existing vendor-customer business relationships. A cyber-criminal gains access to a legitimate vendor account, the process of which may take months to design and fully implement, and uses the account to spread malicious emails to the vendor’s customers. VEC attacks are complex and difficult to detect, however they share some common features [1,3]:

1. Reconnaissance on the vendor and their customer base – the threat actor conducts in-depth research in an attempt to be as convincing as possible in their impersonation efforts. This process may take weeks or months to complete.

2. Credential stealing through phishing campaigns – the threat actor tricks the vendor’s employees into revealing confidential data or corporate credentials in order to gain access to one of the email accounts belonging to the vendor.

3. Account takeover - once the attacker has gained access to one of the vendor’s email accounts, they will create mailbox rules which forward emails meeting certain conditions (such as having ‘Invoice’ in their subject line) to the threat actor’s inbox. This is typically a lengthy process and requires the malicious actors to harvest as much sensitive information as they need in order to successfully masquerade as vendor employees.

4. Deceitful emails are sent to the vendor’s customers – the attacker crafts and sends a highly sophisticated and difficult to detect email campaign to targeted individuals amongst the vendor’s customers. These emails, which may be embedded into existing email threads, will typically contain instructions on how to wire money to the bank account of an attacker.

There have been many high-profile cases of BEC attacks over the years, one of the most famous being the vendor-impersonating BEC attacks carried out between 2013 and 2015 [5]. This BEC campaign resulted in victim companies transferring a total of USD 120 million to bank accounts under the attacker’s control. As the threat of BEC, and in particular VEC, attacks continue to rise, so too does the importance of being able to detect and respond to them.

Observed VEC Attack  

In March 2023, Darktrace/Email observed a VEC attack on an energy company. Email communication between this customer and one of their third-party vendors was common and took place as part of expected business activity, earning previous emails tags such as “Known Domain Relationship”, “Known Correspondent”, and “Established Domain Relationship”. These tags identify the sender relationship as trusted, causing Darktrace’s AI to typically attribute an anomaly score of 0% to emails from this third-party sender.

Just fifty minutes after the above legitimate email was observed, a group of suspicious emails were sent from the same domain, indicating that the trusted third-party had been compromised. Darktrace’s AI picked up on the peculiarity of these emails straight away, detecting elements of the mails which were out of character compared to the sender’s usual pattern of life, and as a result attributing these emails a 100% anomaly score despite the trusted relationship between the customer and sender domain. These suspicious emails were part of a targeted phishing attack, sent to high value individuals such as the company’s CTO and various company directors.  

Figure 1: Darktrace/Email's interface highlighting tags indicating the trusted relationship between the third-party domain and the customer.

Using methods outside of Darktrace’s visibility, a malicious actor managed to hijack the corporate account of a senior employee of this vendor company. The actor abused this email account to send deceitful emails to multiple employees at the energy company, including senior executives.

Figure 2: This screenshot shows Darktrace/Email’s assessment of emails from the vendor account pre-compromise and post-compromise.

Each of the emails sent by the attacker contained a link to a malicious file hosted inside a SharePoint repository associated with a university that had no association with the energy company. The malicious actor therefore appears to have leveraged a previously hijacked SharePoint repository to host their payload.

Cyber-criminals frequently use legitimate file storage domains to host malicious payloads as traditional gateways often fail to defend against them using reputation checks. The SharePoint file which the attacker sought to distribute to employees of the energy company likely provided wire transfer or bank account update instructions. If the attacker had succeeded in delivering these emails to these employees’ mailboxes, then the employees may have been tricked into performing actions resulting in the transfer of funds to a malicious actor. However, the attacker’s attempts to deliver these emails were thwarted by Darktrace/Email.

Darktrace Coverage

Despite the malicious actor sending their deceitful emails from a trusted vendor account, a range of anomalies were detected by Darktrace’s AI, causing the malicious emails to be given a 100% anomaly score and thus held from their recipients’ mailboxes. Such abnormalities, which represented a deviation in normal behavior, included:

  • The presence of an unexpected, out of character file storage link (known to be used for hosting malicious content)
  • The geographical source of the email
  • The anomalous linguistic structure and content of the email body, which earned the emails a high inducement score
Figure 3: Darktrace/Email’s overview of one of the malicious VEC emails it observed.

Darktrace has a series of models designed to trigger when anomalous features, such as those described above, are detected. The emails which made up this particular VEC attack breached a number of notable Darktrace/Email models. The presence of the suspicious link in the emails caused multiple link-related models to breach, which in turn elicited Darktrace RESPOND™ to perform its ‘double lock link’ action – an action which ensures that a user who has clicked on it cannot follow it to its original source. Models which breached due to the suspicious SharePoint link include:

Link / Link To File Storage

  • Link / Low Link Association
  • Link / New Unknown Link
  • Link / Outlook Hijack
  • Link / Relative Sender Anomaly + New Unknown Link
  • Link / Unknown Storage Service
  • Link / Visually Prominent Link Unexpected for Sender
  • Unusual / Unusual Login Location + Unknown Link

The out-of-character and suspicious linguistic aspects of the emails caused the following Darktrace/Email models to breach:

  • High Anomaly Sender
  • Proximity / Phishing
  • Proximity / Phishing and New Activity
  • Unusual / Inducement Shift High
  • Unusual / Undisclosed Recipients
  • Unusual / Unusual Login Location
  • Unusual / Off Topic

Due to the combination of suspicious features that were detected, tags such as ‘Phishing Link’ and ‘Out of Character’ were also added to these emails by Darktrace/Email. Darktrace’s coverage of these emails’ anomalous features ultimately led Darktrace RESPOND to perform its most severe inhibitive action, ‘hold message’. Applying this action stopped the emails from entering their recipients’ mailboxes. By detecting deviations from the sender’s normal email behavior, Darktrace/Email was able to completely neutralize the emails, and prevent them from potentially leading to significant financial harm.

Conclusion

Despite bypassing the customer’s other security measures, Darktrace/Email successfully identified and held these malicious emails, blocking them from reaching the inboxes of the intended recipients and thus preventing a successful targeted VEC attack. The elaborate and sophisticated nature of VEC attacks makes them particularly perilous to customers, and they can be hard to detect due to their exploitation of trusted relationships, and in this case, their use of legitimate services to host malicious files.

Darktrace’s anomaly-based approach to threat detection means it is uniquely placed to identify deviations in common email behavior, while its autonomous response capabilities allow it to take preventative action against emerging threats without latency.

Credits to: Sam Lister, Senior Analyst, for his contributions to this blog.

Appendices

MITRE ATT&CK Mapping

Tactic - Techniques

Resource Development

  • T1586.002 – Compromise Accounts: Email Accounts
  • T1584.006 – Compromise Infrastructure: Web Services
  • T1608.005 – Stage Capabilities: Link Target

Initial Access

  • T1195 – Supply Chain Compromise
  • T1566.002 – Phishing : Spearphishing Link

References

[1] https://www.cloudflare.com/en-gb/learning/email-security/what-is-vendor-email-compromise/

[2] https://www.ic3.gov/Media/PDF/AnnualReport/2022_IC3Report.pdf

[3] https://heimdalsecurity.com/blog/vendor-email-compromise-vec/

[4] https://www.ncsc.gov.uk/files/Business-email-compromise-infographic.pdf  

[5] https://www.justice.gov/usao-sdny/pr/lithuanian-man-sentenced-5-years-prison-theft-over-120-million-fraudulent-business

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
Tiana Kelly
Deputy Team Lead, London & Cyber Analyst

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk: In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy


Prompt Injection Moves from Theory to Front-Page Breach: We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken: When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact: One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target: Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy


Increased commercialization of generative AI and AI assistants in cyber attacks: One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

 

-- Toby Lewis, Global Head of Threat Analysis


Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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December 18, 2025

Why organizations are moving to label-free, behavioral DLP for outbound email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

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Carlos Gray
Senior Product Marketing Manager, Email
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