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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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May 20, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

Sign up today to stay informed about innovations across securing AI.

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Jamie Bali
Technical Author (AI) Developer

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May 20, 2026

State of AI Cybersecurity 2026: 77% of security stacks include AI, but trust is lagging

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Findings in this blog are taken from Darktrace’s annual State of AI Cybersecurity Report 2026.

AI is a contributing member of nearly every modern cybersecurity team. As we discussed earlier in this blog series, rapid AI adoption is expanding the attack surface in ways that security professionals have never before experienced while also empowering attackers to operate at unprecedented speed and scale. It’s only logical that defenders are harnessing the power of AI to fight back.

After all, AI can help cybersecurity teams spot the subtle signs of novel threats before humans can, investigate events more quickly and thoroughly, and automate response. But although AI has been widely adopted, this technology is also frequently misunderstood, and occasionally viewed with suspicion.

For CISOs, the cybersecurity marketplace can be noisy. Making sense of competing vendors’ claims to distinguish the solutions that truly deliver on AI’s full potential from those that do not isn’t always easy. Without a nuanced understanding of the different types of AI used across the cybersecurity stack, it is difficult to make informed decisions about which vendors to work with or how to gain the most value from their solutions. Many security leaders are turning to Managed Security Service Providers (MSSPs) for guidance and support.

The right kinds of AI in the right places?

Back in 2024, when we first conducted this annual survey, more than a quarter of respondents were only vaguely familiar with generative AI or hadn’t heard of it at all. Today, GenAI plays a role in 77% of security stacks. This percentage marks a rapid increase in both awareness and adoption over a relatively short period of time.

According to security professionals, different types of AI are widely integrated into cybersecurity tooling:

  • 67% report that their organization’s security stack uses supervised machine learning
  • 67% report that theirs uses agentic AI
  • 58% report that theirs uses natural language processing (NLP)
  • 35% report that theirs uses unsupervised machine learning

But their responses suggest that organizations aren’t always using the most valuable types of AI for the most relevant use cases.

Despite all the recent attention AI has gotten, supervised machine learning isn’t new. Cybersecurity vendors have been experimenting with models trained on hand-labeled datasets for over a decade. These systems are fed large numbers of examples of malicious activity – for instance, strains of ransomware – and use these examples to generalize common indicators of maliciousness – such as the TTPs of multiple known ransomware strains – so that the models can identify similar attacks in the future. This approach is more effective than signature-based detection, since it isn’t tied to an individual byte sequence or file hash. However, supervised machine learning models can miss patterns or features outside the training data set. When adversarial behavior shifts, these systems can’t easily pivot.

Unsupervised machine learning, by contrast, can identify key patterns and trends in unlabeled data without human input. This enables it to classify information independently and detect anomalies without needing to be taught about past threats. Unsupervised learning can continuously learn about an environment and adapt in real time.

One key distinction between supervised and unsupervised machine learning is that supervised learning algorithms require periodic updating and re-training, whereas unsupervised machine learning trains itself while it works.

The question of trust

Even as AI moves into the mainstream, security professionals are eyeing it with a mix of enthusiasm and caution. Although 89% say they have good visibility into the reasoning behind AI-generated outputs, 74% are limiting AI’s ability to take autonomous action in their SOC until explainability improves. 86% do not allow AI to take even small remediation actions without human oversight.

This model, commonly known as “human in the loop,” is currently the norm across the industry. It seems like a best-of-both-worlds approach that allows teams to experience the benefits of AI-accelerated response without relinquishing control – or needing to trust an AI system.

Keeping humans somewhat in the loop is essential for getting the best out of AI. Analysts will always need to review alerts, make judgement calls, and set guardrails for AI's behavior. Their input helps AI models better understand what “normal” looks like, improving their accuracy over time.

However, relying on human confirmation has real costs – it delays response, increases the cognitive burden analysts must bear, and creates potential coverage gaps when security teams are overwhelmed or unavailable. The traditional model, in which humans monitor and act on every alert, is no longer workable at scale.

If organizations depend too heavily on in-the-loop humans, they risk recreating the very problem AI is meant to solve: backlogs of alerts waiting for analyst review. Removing the human from the loop can buy back valuable time, which analysts can then invest in building a proactive security posture. They can also focus more closely on the most critical incidents, where human attention is truly needed.

Allowing AI to operate autonomously requires trust in its decision-making. This trust can be built gradually over time, with autonomous operations expanding as trust grows. But it also requires knowledge and understanding of AI — what it is, how it works, and how best to deploy it at enterprise scale.

Looking for help in all the right places

To gain access to these capabilities in a way that’s efficient and scalable, growing numbers of security leaders are looking for outsourced support. In fact, 85% of security professionals prefer to obtain new SOC capabilities in the form of a managed service.

This makes sense: Managed Security Service Providers (MSSPs) can deliver deep, continuously available expertise without the cost and complexity of building an in-house team. Outsourcing also allows organizations to scale security coverage up or down as needs change, stay current with evolving threats and regulatory requirements, and leverage AI-native detection and response without needing to manage the AI tools themselves.

Preferences for MSSP-delivered security operations are particularly strong in the education, energy (87%), and healthcare sectors. This makes sense: all are high-value targets for threat actors, and all tend to have limited cybersecurity budgets, so the need for a partner who can deliver affordable access to expertise at scale is strong. Retailers also voiced a strong preference for MSSP-delivered services. These companies are tasked with managing large volumes of consumer personal and financial data, and with transforming an industry traditionally thought of as a late adopter to a vanguard of cyber defense. Technology companies, too, have a marked preference for SOC capabilities delivered by MSSPs. This may simply be because they understand the complexity of the threat landscape – and the advantages of specialized expertise — so well.

In order to help as many organizations as possible – from major enterprises to small and midmarket companies – benefit from enterprise-grade, AI-native security, Darktrace is making it easier for MSSPs to deliver its technology. The ActiveAI Security Portal introduces an alert dashboard designed to increase the speed and efficiency of alert triage, while a new AI-powered managed email security solution is giving MSSPs an edge in the never-ending fight against advanced phishing attacks – helping partners as well as organizations succeed on the frontlines of cyber defense.

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

Learn more about securing AI in your enterprise.

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