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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 21, 2025

Global Telecom Provider: Powering and Protecting the World's Data Giants

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This global leader plays a critical role in keeping the world connected. The company works with some of the largest and most influential public and private organizations in the world to enable ultra-fast data transmission.

Safeguarding the systems that keep the world connected

Standing at the forefront of global connectivity, this industry leader designs and manages large-scale communications systems that power the world’s most data-intensive enterprises – including social media giants, hyperscale cloud providers, and major data center operators. Given the scale, confidentiality, and sensitivity of the systems and data it helps transport, the company faces complex cybersecurity challenges.

Protecting sensitive customer data

Most of the organization’s projects are custom-designed and highly proprietary, making data privacy and Intellectual Property (IP) protection critical to maintaining trust and confidentiality with customers. In an industry where every competitor knows the landscape intimately, any loss of data could cause significant damage.

International security implications

The company faces a broad range of advanced cyber threats – from corporate espionage and supply chain risks to cyber-physical attacks on critical infrastructure. Its international footprint adds complexity, including cross-border regulatory compliance. A successful attack could disrupt business, compromise IP, or trigger wider consequences like disruptions to international data transfers and other critical services.

The global leader works closely with communities to anticipate threats that could impact the global communications network at large.

In this environment, cybersecurity is a foundation for international trust,” said the organization’s CISO.

Building a resilient cybersecurity strategy from the ground up

The CISO had the rare opportunity to build the IT and cybersecurity infrastructure from scratch. "Initially, we bought what everyone else buys,” referencing the traditional mix of firewalls, routers, and antivirus tools. “But I knew we needed to do more.”

Self-Learning AI – “the missing piece”

With solid perimeter defenses in place, the security team sought deeper protection inside the network. Darktrace’s Self-Learning AI stood out. “Unlike other solutions, Darktrace’s AI looks beyond known threat signatures, learning what’s normal for our environment and flagging what’s not. That was the missing piece – something that could help us even when everything else failed.”

A solution and partnership that delivered

The CISO said he appreciated the ability to observe Darktrace in action before full deployment, noting that the Darktrace team was there every step of the way, providing guidance and expertise to ensure he got the most out of his investment.

Partnership was especially valuable given the company’s explosive 400% growth over the last six years. As resources were stretched and priorities shifted, “Darktrace remained patient and responsive. We’re slow and methodical, but the Darktrace support team was phenomenal, never losing momentum and earning our trust.”

A unified cybersecurity ecosystem

Today, the global leader is using the Darktrace ActiveAI Security Platform™ as a core part of its layered defense strategy, including:

The CISO appreciates how, as a unified cybersecurity platform, Darktrace has an intuitive user interface, which makes it easier for his team to investigate alerts visually, even without deep technical expertise.

Advancing defenses while impacting the bottom line

A 24/7 “safety net”

The fact that this company has never been hacked is the clearest proof it made the right decision with Darktrace, said the CISO. Initially rolled out in Human Confirmation Mode, meaning it would not take autonomous action without explicit approval from the security team, Darktrace immediately uncovered threats and anomalies that other tools had missed.

Darktrace acts as a must-have safety net—ready to step in when other tools fall short,” said the CISO.

From monitoring internal behavior and identifying unusual attack patterns, to autonomously neutralizing threats after hours, the platform provides peace of mind in a high-stakes industry. “Darktrace is my dark horse – the thing I have in my back pocket if everything else fails. It’s here to save the day, save my company, and maybe even save my career.”

Autonomous capabilities free up time for skilled analysts

Darktrace’s AI-powered detection and response capabilities are deeply embedded in the team’s day-to-day operations, autonomously investigating and responding to the majority of potential threats. Cyber AI Analyst conducted a total of 2,776 total investigations within three months, averaging just 12 minutes to autonomously investigate an incident. Of those 2,776 investigations, Darktrace resolved 2,671 (96%) autonomously and escalated only 105 (4%) to analysts. Darktrace has dramatically reduced alert fatigue and freed up analysts to focus on what really matters, saving the security team 486 analyst hours on investigations within a 20-day period.

From noise to actionable insight

Darktrace delivers meaningful data and meaningful alerts. “If Darktrace escalates an incident, we drop everything and work on that. We trust in Darktrace.” When analysts do need to investigate an incident, Darktrace’s forensic logs and guided remediation suggestions have slashed the time analysts spend on investigations by four to five times.

Stronger security. Lower cost.

The CISO says, “Darktrace is a money-saver for our organization, making continued investments an easy sell to the CEO and the board.”  When he found himself down a resource after a member of the security team left the organization, the CISO turned to Darktrace Managed Threat Detection and Response services for 24/7 expert support. “It was a no brainer. We got better coverage, higher skill levels, and around-the-clock support – all for less than what we would pay to employ a single analyst.”

Scaling securely into the future

Securing networks in motion  

The organization is preparing to scale both its operations and security posture across existing distributed, mobile and deployable communications networks that historically have been disconnected. Some of these networks are in constant motion and operating in some of the world’s most volatile regions. “Darktrace will act as an autonomous defender, monitoring for anomalous behavior and intervening, when necessary, especially during those dangerous times when an asset ‘goes dark’ and becomes disconnected from the broader network,” said the CISO.

Applying AI strategically

As the organization continues to evaluate where and how to apply AI, its emphasis will be on technologies that can act independently to contain threats – especially in environments where human response may be delayed. “It’s about using the right kind of AI for the right challenge. That’s why we’re investing in Darktrace, with tools that can adapt and learn even in isolation and provide real-time protection wherever we operate.”

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July 17, 2025

Introducing the AI Maturity Model for Cybersecurity

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AI adoption in cybersecurity: Beyond the hype

Security operations today face a paradox. On one hand, artificial intelligence (AI) promises sweeping transformation from automating routine tasks to augmenting threat detection and response. On the other hand, security leaders are under immense pressure to separate meaningful innovation from vendor hype.

To help CISOs and security teams navigate this landscape, we’ve developed the most in-depth and actionable AI Maturity Model in the industry. Built in collaboration with AI and cybersecurity experts, this framework provides a structured path to understanding, measuring, and advancing AI adoption across the security lifecycle.

Overview of AI maturity levels in cybersecurity

Why a maturity model? And why now?

In our conversations and research with security leaders, a recurring theme has emerged:

There’s no shortage of AI solutions, but there is a shortage of clarity and understanding of AI uses cases.

In fact, Gartner estimates that “by 2027, over 40% of Agentic AI projects will be canceled due to escalating costs, unclear business value, or inadequate risk controls. Teams are experimenting, but many aren’t seeing meaningful outcomes. The need for a standardized way to evaluate progress and make informed investments has never been greater.

That’s why we created the AI Security Maturity Model, a strategic framework that:

  • Defines five clear levels of AI maturity, from manual processes (L0) to full AI Delegation (L4)
  • Delineating the outcomes derived between Agentic GenAI and Specialized AI Agent Systems
  • Applies across core functions such as risk management, threat detection, alert triage, and incident response
  • Links AI maturity to real-world outcomes like reduced risk, improved efficiency, and scalable operations

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How is maturity assessed in this model?

The AI Maturity Model for Cybersecurity is grounded in operational insights from nearly 10,000 global deployments of Darktrace's Self-Learning AI and Cyber AI Analyst. Rather than relying on abstract theory or vendor benchmarks, the model reflects what security teams are actually doing, where AI is being adopted, how it's being used, and what outcomes it’s delivering.

This real-world foundation allows the model to offer a practical, experience-based view of AI maturity. It helps teams assess their current state and identify realistic next steps based on how organizations like theirs are evolving.

Why Darktrace?

AI has been central to Darktrace’s mission since its inception in 2013, not just as a feature, but the foundation. With over a decade of experience building and deploying AI in real-world security environments, we’ve learned where it works, where it doesn’t, and how to get the most value from it. This model reflects that insight, helping security leaders find the right path forward for their people, processes, and tools

Security teams today are asking big, important questions:

  • What should we actually use AI for?
  • How are other teams using it — and what’s working?
  • What are vendors offering, and what’s just hype?
  • Will AI ever replace people in the SOC?

These questions are valid, and they’re not always easy to answer. That’s why we created this model: to help security leaders move past buzzwords and build a clear, realistic plan for applying AI across the SOC.

The structure: From experimentation to autonomy

The model outlines five levels of maturity :

L0 – Manual Operations: Processes are mostly manual with limited automation of some tasks.

L1 – Automation Rules: Manually maintained or externally-sourced automation rules and logic are used wherever possible.

L2 – AI Assistance: AI assists research but is not trusted to make good decisions. This includes GenAI agents requiring manual oversight for errors.

L3 – AI Collaboration: Specialized cybersecurity AI agent systems  with business technology context are trusted with specific tasks and decisions. GenAI has limited uses where errors are acceptable.

L4 – AI Delegation: Specialized AI agent systems with far wider business operations and impact context perform most cybersecurity tasks and decisions independently, with only high-level oversight needed.

Each level reflects a shift, not only in technology, but in people and processes. As AI matures, analysts evolve from executors to strategic overseers.

Strategic benefits for security leaders

The maturity model isn’t just about technology adoption it’s about aligning AI investments with measurable operational outcomes. Here’s what it enables:

SOC fatigue is real, and AI can help

Most teams still struggle with alert volume, investigation delays, and reactive processes. AI adoption is inconsistent and often siloed. When integrated well, AI can make a meaningful difference in making security teams more effective

GenAI is error prone, requiring strong human oversight

While there is a lot of hype around GenAI agentic systems, teams will need to account for inaccuracy and hallucination in Agentic GenAI systems.

AI’s real value lies in progression

The biggest gains don’t come from isolated use cases, but from integrating AI across the lifecycle, from preparation through detection to containment and recovery.

Trust and oversight are key initially but evolves in later levels

Early-stage adoption keeps humans fully in control. By L3 and L4, AI systems act independently within defined bounds, freeing humans for strategic oversight.

People’s roles shift meaningfully

As AI matures, analyst roles consolidate and elevate from labor intensive task execution to high-value decision-making, focusing on critical, high business impact activities, improving processes and AI governance.

Outcome, not hype, defines maturity

AI maturity isn’t about tech presence, it’s about measurable impact on risk reduction, response time, and operational resilience.

[related-resource]

Outcomes across the AI Security Maturity Model

The Security Organization experiences an evolution of cybersecurity outcomes as teams progress from manual operations to AI delegation. Each level represents a step-change in efficiency, accuracy, and strategic value.

L0 – Manual Operations

At this stage, analysts manually handle triage, investigation, patching, and reporting manually using basic, non-automated tools. The result is reactive, labor-intensive operations where most alerts go uninvestigated and risk management remains inconsistent.

L1 – Automation Rules

At this stage, analysts manage rule-based automation tools like SOAR and XDR, which offer some efficiency gains but still require constant tuning. Operations remain constrained by human bandwidth and predefined workflows.

L2 – AI Assistance

At this stage, AI assists with research, summarization, and triage, reducing analyst workload but requiring close oversight due to potential errors. Detection improves, but trust in autonomous decision-making remains limited.

L3 – AI Collaboration

At this stage, AI performs full investigations and recommends actions, while analysts focus on high-risk decisions and refining detection strategies. Purpose-built agentic AI systems with business context are trusted with specific tasks, improving precision and prioritization.

L4 – AI Delegation

At this stage, Specialized AI Agent Systems performs most security tasks independently at machine speed, while human teams provide high-level strategic oversight. This means the highest time and effort commitment activities by the human security team is focused on proactive activities while AI handles routine cybersecurity tasks

Specialized AI Agent Systems operate with deep business context including impact context to drive fast, effective decisions.

Join the webinar

Get a look at the minds shaping this model by joining our upcoming webinar using this link. We’ll walk through real use cases, share lessons learned from the field, and show how security teams are navigating the path to operational AI safely, strategically, and successfully.

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About the author
Ashanka Iddya
Senior Director, Product Marketing
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