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September 30, 2024

Business Email Compromise (BEC) in the Age of AI

Generative AI tools have increased the risk of BEC, and traditional cybersecurity defenses struggle to stay ahead of the growing speed, scale, and sophistication of attacks. Only multilayered, defense-in-depth strategies can counter the AI-powered BEC threat.
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
Carlos Gray
Senior Product Marketing Manager, Email
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30
Sep 2024

As people continue to be the weak link in most organizations’ cybersecurity practices, the growing use of generative AI tools in cyber-attacks makes email, their primary communications channel, a more compelling target than ever. The risk associated with Business Email Compromise (BEC) in particular continues to rise as generative AI tools equip attackers to build and launch social engineering and phishing campaigns with greater speed, scale, and sophistication.

What is BEC?

BEC is defined in different ways, but generally refers to cyber-attacks in which attackers abuse email — and users’ trust — to trick employees into transferring funds or divulging sensitive company data.

Unlike generic phishing emails, most BEC attacks do not rely on “spray and pray” dissemination or on users’ clicking bogus links or downloading malicious attachments. Instead, modern BEC campaigns use a technique called “pretexting.”

What is pretexting?

Pretexting is a more specific form of phishing that describes an urgent but false situation — the pretext — that requires the transfer of funds or revelation of confidential data.  

This type of attack, and therefore BEC, is dominating the email threat landscape. As reported in Verizon’s 2024 Data Breach Investigation Report, recently there has been a “clear overtaking of pretexting as a more likely social action than phishing.” The data shows pretexting, “continues to be the leading cause of cybersecurity incidents (accounting for 73% of breaches)” and one of “the most successful ways of monetizing a breach.”

Pretexting and BEC work so well because they exploit humans’ natural inclination to trust the people and companies they know. AI compounds the risk by making it easier for attackers to mimic known entities and harder for security tools and teams – let alone unsuspecting recipients of routine emails – to tell the difference.

BEC attacks now incorporate AI

With the growing use of AI by threat actors, trends point to BEC gaining momentum as a threat vector and becoming harder to detect. By adding ingenuity, machine speed, and scale, generative AI tools like OpenAI’s ChatGPT give threat actors the ability to create more personalized, targeted, and convincing emails at scale.

In 2023, Darktrace researchers observed a 135% rise in ‘novel social engineering attacks’ across Darktrace / EMAIL customers, corresponding with the widespread adoption of ChatGPT.

Large Language Models (LLMs) like ChatGPT can draft believable messages that feel like emails that target recipients expect to receive. For example, generative AI tools can be used to send fake invoices from vendors known to be involved with well-publicized construction projects. These messages also prove harder to detect as AI automatically:

  • Avoids misspellings and grammatical errors
  • Creates multiple variations of email text  
  • Translates messages that read well in multiple languages
  • And accomplishes additional, more targeted tactics

AI creates a force multiplier that allows primitive mass-mail campaigns to evolve into sophisticated automated attacks. Instead of spending weeks studying the target to craft an effective email, cybercriminals might only spend an hour or two and achieve a better result.  

Challenges of detecting AI-powered BEC attacks

Rules-based detections miss unknown attacks

One major challenge comes from the fact that rules based on known attacks have no basis to deny new threats. While native email security tools defend against known attacks, many modern BEC attacks use entirely novel language and can omit payloads altogether. Instead, they rely on pure social engineering or bide their time until security tools recognize the new sender as a legitimate contact.  

Most defensive AI can’t keep pace with attacker innovation

Security tools might focus on the meaning of an email’s text in trying to recognize a BEC attack, but defenders still end up in a rules and signature rat race. Some newer Integrated Cloud Email Security (ICES) vendors attempt to use AI defensively to improve the flawed approach of only looking for exact matches. Employing data augmentation to identify similar-looking emails helps to a point but not enough to outpace novel attacks built with generative AI.

What tools can stop BEC?

A modern defense-in-depth strategy must use AI to counter the impact of AI in the hands of attackers. As found in our 2024 State of AI Cybersecurity Report, 96% of survey participants believe AI-driven security solutions are a must have for countering AI-powered threats.

However, not all AI tools are the same. Since BEC attacks continue to change, defensive AI-powered tools should focus less on learning what attacks look like, and more on learning normal behavior for the business. By understanding expected behavior on the company’s side, the security solution will be able to recognize anomalous and therefore suspicious activity, regardless of the word choice or payload type.  

To combat the speed and scale of new attacks, an AI-led BEC defense should spot novel threats.

Darktrace / EMAIL™ can do that.  

Self-Learning AI builds profiles for every email user, including their relationships, tone and sentiment, content, and link sharing patterns. Rich context helps in understanding how people communicate and identifying deviations from the normal routine to determine what does and does not belong in an individual’s inbox and outbox.  

Other email security vendors may claim to use behavioral AI and unsupervised machine learning in their products, but their AI are still pre-trained with historical data or signatures to recognize malicious activity, rather than demonstrating a true learning process. Darktrace’s Self Learning-AI truly learns from the organization in which it is installed, allowing it to detect unknown and novel vectors that other security tools are not yet trained on.

Because Darktrace understands the human behind email communications rather than knowledge of past attacks, Darktrace / EMAIL can stop the most sophisticated and evolving email security risks. It enhances your native email security by leveraging business-centric behavioral anomaly detection across inbound, outbound, and lateral messages in both email and Teams.

This unique approach quickly identifies sophisticated threats like BEC, ransomware, phishing, and supply chain attacks without duplicating existing capabilities or relying on traditional rules, signatures, and payload analysis.  

The power of Darktrace’s AI can be seen in its speed and adaptability: Darktrace / EMAIL blocks the most novel threats up to 13 days faster than traditional security tools.

Learn more about AI-led BEC threats, how these threats extend beyond the inbox, and how organizations can adopt defensive AI to outpace attacker innovation in the white paper “Beyond the Inbox: A Guide to Preventing Business Email Compromise.”

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
Carlos Gray
Senior Product Marketing Manager, Email

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July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

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Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician

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July 1, 2026

5 Ways AI is changing traditional security models according to modern CISOs

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The Reality of Securing AI in Motion

Traditional security tools were built for environments defined by fixed rules and predictable workflows. But AI behavior is non-deterministic. The same prompt can produce different outcomes, and risk often emerges gradually as AI behavior adapts, and permissions drift over time. This creates a constantly shifting environment where security teams are working to define control in a system that resists stability. “In AI security, yesterday's priorities can become tomorrow's blind spots. The landscape shifts that fast,” warned the SVP and Head of Technology and Cybersecurity of a real estate investment trust. Conventional approaches, which rely on establishing and maintaining a steady baseline, struggle to keep up with that level of change.

At the same time, AI adoption is accelerating across organizations, often faster than security teams can implement the controls needed to manage it. “The car is being built while it’s already on the road,” explained the CISO of a global private fund administrator. “The threats we're securing against today won't be the threats we're facing tomorrow. What kept us up three months ago looks nothing like what we're dealing with today.”

As businesses move quickly to unlock value from AI, security teams are left closing gaps in real time, while also facing adversaries who are using AI to make their attacks more scalable, adaptive, and difficult to detect. In this recent roundtable discussion of CISOs and security leaders, five themes emerged around AI cyber risk.  

1. AI agents with human access but no human judgment

In Darktrace’s 2026 State of AI Cybersecurity report, 96% of the surveyed security professionals agree that AI significantly improves the speed and efficiency with which they work. Yet, 92% admitted that they’re concerned with the security implications of the use of AI agents across their workforce.

AI agents now operate with human-level permissions across systems, acting at machine speed, orchestrating actions across platforms, and making decisions without the judgment or caution a person would apply. Unlike human users, they cannot be expected to pause and question whether a given action is appropriate.

Their identities are also difficult to inventory, govern, and audit. As agents become easier to deploy than legacy IT systems ever were, organizations are quickly losing track of what is running, what it has access to, and what it is doing. This creates a growing class of highly privileged, autonomous actors operating without the visibility or oversight that traditional identity and access controls were designed to provide.“While AI adoption is critical to running a modern business, AI alone can’t solve all our cybersecurity challenges,” said a global financial sector CISO. “We still need think critically and use human judgement. Those are two things AI can’t do.”

This lack of human judgment becomes especially risky as new architectures, such as Model Context Protocol (MCP), can expand how agents connect to data, tools, and external systems. By design, MCP enables agents to dynamically discover and interact with new resources, increasing flexibility but also introducing new pathways for unintended access, data exposure, or abuse if not properly governed.

The CISO of a fund administrator highlighted one emerging vector as an example: rogue MCP servers. “Our developers want to move quickly and bring value to the business, but technologies like these can unintentionally expose sensitive data in ways that would never have happened before.”

2. Increased digital complexity and expanded attack surface

AI activity rarely stays contained. A single prompt can trigger a chain of actions across networks, email, cloud infrastructure, SaaS platforms, endpoints, identity systems, and development environments, spanning systems that were never designed to be secured as a single, connected flow. This expands both the scale and complexity of what security teams need to monitor and defend.

Yet no single control has visibility across that entire chain. “You can’t defend effectively what you can’t see,” cautioned the private fund administrator CISO. As AI-driven activity moves fluidly across environments, gaps in coverage become inevitable, creating blind spots that attackers can exploit.

Threat actors are already capitalizing on this lack of visibility. “Threat actors have advanced their use of generative AI to launch more convincing phishing campaigns, automate social engineering, and scale attacks with greater precision down to the individual level,” said the SVP of Technology and Cybersecurity for the real estate investment trust. What was once manual and targeted can now be automated and personalized at scale, making attacks harder to detect and easier to execute.

At the same time, the pace of exploitation is accelerating. As a global CISO operating across 40+ countries described it: “Zero-day vulnerabilities are no longer zero day; it’s minus one day. By the time you get to it and address it, it’s already a problem.” By the time risk is identified, it has often already been realized.

The result is a rapidly expanding and increasingly interconnected attack surface that challenges security teams to maintain visibility, context, and control across AI-driven activity.

3. Shadow AI is already everywhere

76% of organizations now cite shadow AI as a problem, one that is spreading through organizations in ways that are hard to track and even harder to control.

Employees are experimenting with publicly available Gen AI tools. Teams are spinning up low-code automations on their own. SaaS providers are quietly embedding AI into existing products. Developers are plugging AI services directly into workflows, often without pausing to consider what that exposure means.

The result is a lack of visibility into:

  • What AI tools are being used
  • What data those tools can access
  • Where prompts and outputs are going
  • Which AI agents are interacting with enterprise systems

The SVP of Cybersecurity at a real estate investment trust described the shift: “Before, I was worried about someone sending data erroneously to their personal email. Now we have all these agents online that people are utilizing, and we’re looking at those vectors as well.” For security teams, this means operating without a complete view of how AI is being used, what it can access, and where risk may already be emerging.

4. Built-in guardrails are not enough

Organizations often assume that native AI guardrails or provider-level controls are sufficient to manage AI risk. But securing AI requires ongoing visibility, oversight, and governance, not just controls configured at deployment. "It’s a misconception that adopting AI is going to solve all your problems,” warns a global financial services CISO.

Security leaders are increasingly recognizing the limitations of these controls as:

  • Fragmented and difficult to enforce consistently across multiple AI systems, workflows, and environments
  • Ambiguous in terms of accountability due to shared responsibility for AI governance between IT, security, developers, business teams, and third-party providers
  • Limited in end-to-end oversight, leaving gaps that stretch from the initial prompt all the way through to the downstream impact of an agent's actions

Securing AI demands more than simple prompt filtering or static policy enforcement. It requires understanding intent, behavior, and context across both human and AI activity.

The next phase of cybersecurity: securing AI

To safely and responsibly adopt AI at scale, organizations need a new operational model for cybersecurity that’s capable of:

• Understanding AI behavior

• Identifying risk in real time

• Maintaining governance without slowing innovation

The CSO of a $10 billion municipal utility organization described the challenge with precision: “We have to move at the speed of innovation and risk, because both are accelerating faster than ever.”

Embrace AI with confidence with Darktrace / SECURE AI

Darktrace has introduced Darktrace / SECURE AI™, a new product within the Darktrace ActiveAI Security Platform™  ,designed to provide enterprise-wide security for AI by applying industry leading behavioral analysis to how prompts, agents, and AI systems are used.

Darktrace / SECURE AITM delivers real-time visibility and control across Enterprise and SaaS GenAI prompts, AI agent identities, development and production environments, and Shadow AI - detecting even subtle misuse, misconfiguration, and drift that traditional, rule-based controls simply do not understand. By interpreting context and intent across humans and machines, Darktrace enables organizations to adopt AI at scale without introducing unmanaged risk

What makes this possible is Darktrace’s decade-long maturity and expertise in behavioral understanding and AI-native cybersecurity. Achieved with Self-Learning AI that has been proven across more than 10,000 organizations, Darktrace understands what “normal” looks like for a business, across its users, systems, and now AI, so that meaningful deviations can be detected and acted on before they become incidents.

With one CISO describing Darktrace’s Self-Learning AI as “a leap forward compared to other tools” and another as a “force multiplier,” the technology can interpret ambiguous interactions, understand how access accumulates over time, and recognize when behavior, human or machine, begins to drift.

“Strategically, we’re looking to gain more visibility into how AI is operating across the environment and achieve greater control over what AI should be allowed to access and do,” shared the CISO at a private fund administrator.  

“What I’ve seen from Darktrace / SECURE AI is extremely promising. I have tremendous confidence in Darktrace’s vision for where this is headed and its ability to execute on this new solution.”

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