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April 28, 2021

When Phishing Emails Look Real: How can behavioral AI support against human error

Learn how autonomous AI frees up IT teams and allows them to focus on what matters. Say goodbye to weighed-down teams and lengthy security processes.
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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.
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Ai-drive email attacks, email phishing, email cybersecurityDefault blog image
28
Apr 2021

Why email attacks still succeed despite security tools

At the heart of any email attack is the goal of moving the recipient to engage: whether that’s clicking a link, filling in a form, or opening an attachment. And with over nine in ten cyber-attacks starting with an email, this attack vector continues to prove successful, despite organizations’ best efforts to safeguard their workforce by deploying email gateways and training employees to spot phishing attempts.

Email attackers have seen such success because they understand their victims. They know that, ultimately, human beings are creatures of habit, prone to error, and susceptible to their emotions. Years of experience has allowed attackers to fine tune their emails making them more plausible and more provocative. Automated tools are now increasing the speed and scale at which criminals can buy new domains and send emails en masse. This makes it even easier to ‘A/B test’ attack methods: abandoning those that don’t see high success rates and capitalizing on those that do.

What emotional triggers phishing attacks rely on

We can classify phishing attempts into five broad categories, each aiming to trigger a different emotional reaction and elicit a response.

  • Fear: “We have detected a virus on your device, log in to your McAfee account.”
  • Curiosity: “You have 3 new voicemails, click here.”
  • Generosity: “COVID-19 has greatly impacted homelessness in your area. Donate now.”
  • Greed: “Only 23 iPhones left to give away, act now!”
  • Concern: “Coronavirus outbreak in your area: Find out more.”

It’s worth noting that today’s increasingly dynamic workforces are more susceptible to these techniques, isolated in their homes and hungry for new information.

Why traditional email security tools fail against modern attacks

As email attacks continue to trick employees and find success, many organizations have realized that the built-in security tools that come with their email provider aren’t enough to defend against today’s attacks. Additional email gateways are successful in catching spam and other low-hanging fruit, but fail to stop advanced attacks – particularly those leveraging novel malware, new domains, or advanced techniques. These advanced attacks are also the most damaging to businesses.

This failure is due to an inherent weakness in the legacy approach of traditional security tools. They compare inbound mail against lists of ‘known bad’ IPs, domains, and file hashes. Senders and recipients are treated simply as data points – ignoring the nuances of the human beings behind the keyboards.

Looking at these metrics in isolation fails to take into account the full context that can only be gained by understanding the people behind email interactions: where they usually log in from, who they communicate with, how they write, and what types of attachments they send and receive. It is this rich, personal context that reveals seemingly benign emails to be unmistakably malicious, especially when other data fails to reveal the danger.

Misunderstanding the human

Frustrated with the ineffectiveness of traditional tools, many organizations think that the solution is to minimize the chances that employees engage with malicious emails through comprehensive employee training. Indeed, companies often attempt to train their employees to spot malicious emails to compensate for their technology’s lack of detection.

Considering humans to be the last line of defense is dangerous, and this approach overlooks the fact that today’s sophisticated fakes can appear indistinguishable to legitimate mails. It's only when you really break an email down beyond the text, beyond the personal name, beyond the domain and email address (in the case of compromised trusted senders), that you can decipher between real and fake.

Large data breaches of recent years have given attackers greater access than ever to corporate emails and stolen passwords, and so supply chain attacks are becoming increasingly common. When attackers take over a trusted account or an existing email thread, how can an employee be expected to notice a subtle change in wording or the different type of attached document? However rigorous the internal training program and regardless of how vigilant employees are, we are now at the point where humans cannot spot these very subtle indicators. And one click is all it takes.

How behavioral AI detects email threats that other tools miss

Email security, for a long time, remains an unsolved piece of the complex cyber security puzzle. The failure of both traditional tools and employee training has prompted organizations to take a radically different approach. Thousands of businesses across the world, in both the public and private sector, use artificial intelligence that understands the human behind the keyboard and forms a nuanced and continually evolving understanding of email interactions across the business.

By learning what a human does, who they interact with, how they write, and the substance of a typical conversation between any two or more people, AI begins to understand the habits of employees, and over time it builds a comprehensive picture of their normal patterns of behavior. Most importantly, AI is self-learning, continuously revising its understanding of ‘normal’ so that when employees’ habits change, so does the AI’s understanding.

This enables the technology to detect behavioral anomalies that fall outside of an employee’s ‘pattern of life’, or the pattern of life for the organization as a whole.

This fundamentally new approach to email security enables the system to recognize the subtle indicators of a threat and make accurate decisions to stop or allow emails to pass through, even if a threat has never been seen before.

Sitting behind email gateways, this self-learning technology has extremely high catch rates. It has caught countless malicious emails that other tools missed, from impersonations of senior financial personnel to ‘fearware’ that played on the workforce’s uncertainties at a time of pandemic.

Why AI-driven email attacks are increasing risk for organizations

Attackers are continuing to innovate, and automation has led to a new wave of email threats. 88% of security leaders now believe that cyber-attacks powered by offensive AI are inevitable. The email threat landscape is rapidly changing, and we can expect to receive more hoax emails that are more convincing. Now is a crucial moment for organizations to prepare for this eventuality by adopting AI in their email defenses.

How to stop phishing attacks with AI-driven email security

Stopping phishing attacks requires more than filtering emails or training users. The real risk comes after the click, when attackers move across identity, SaaS, and cloud environments.

AI helps close this gap by detecting subtle changes in behavior, even when emails appear legitimate or come from trusted accounts. When combined with existing tools, it allows security teams to catch advanced threats earlier and respond before they escalate.

To see how this approach works in practice, explore how Darktrace applies behavioral AI to email security.

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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.
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June 24, 2026

A New Security Challenge: The Curious Case of Prompt Language Analysis

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Why prompt analysis is emerging as a key AI security challenge

If securing AI has been one of the defining cybersecurity conversations of the past year, prompt analysis is quickly becoming one of its most interesting frontiers.

Security leaders are under pressure to understand how AI is being used across the business. In some organizations, that means governing employee use of chatbots. In others, it means overseeing copilots embedded into SaaS platforms, monitoring coding assistants, or assessing the growing footprint of autonomous agents. However different these use cases may appear on the surface, they share a common factor: humans and machines are usually interacting with enterprise systems through language.  

How prompt language differs from traditional security telemetry

For years, defenders have become used to working with familiar forms of telemetry: email traffic, network connections, API calls, endpoint processes, authentication events. Prompt language is different. It is not simply another log source. It is an expression of intent, instruction, curiosity, urgency, and sometimes manipulation. It reflects the end-goal of a user or agent, but not always with enough surrounding context to interpret the risk correctly.

Why existing security approaches only partially explain prompt risk

A growing number of vendors are approaching the task of securing AI from the angle they know best. Perimeter vendors are extending web or browser controls into AI usage. Identity vendors are emphasizing agent permissions and access governance. Data security and DLP providers are focusing on content inspection and exfiltration risk. All of these perspectives matter, but individually can’t fully explain the problem.

The challenge with securing AI is not just that a new application category has emerged. It is that language has become a new operating layer in the enterprise.

Employees now use prompts to summarize documents, generate code, analyze spreadsheets, query internal knowledge, and trigger multi-step actions through agents. In each case, prompt language acts as the interface between human intent and machine execution. That makes prompts incredibly valuable from a security perspective as they can hint at misuse, policy violations, data exposure, or attempts to circumvent controls. However, they can also be deeply ambiguous when viewed in isolation. That ambiguity is the heart of the issue.

Prompts as behavioral signals, not just text to classify

A prompt by itself tells you what was asked. It does not necessarily tell you whether the request is expected, risky, accidental, or entirely legitimate in context. Two nearly identical prompts can carry very different meanings depending on the role and function of who issued them, what systems they can access, and what actions followed. In other words, prompts are not just text to classify. They are behavioral signals to interpret.

Example: How context changes prompt risk entirely

Consider a common enterprise scenario. An employee is pulled into a new project with an aggressive deadline. Almost overnight, their use of AI tools spikes. They begin prompting more frequently, working across unfamiliar documents, querying new data sources, and interacting with more systems than usual to accelerate delivery. Viewed narrowly, this may look suspicious. Prompt volume increases, file access patterns change, API and SaaS activity rise. From some vantage points, it may resemble insider risk or unmanaged AI usage.

But now add context. Imagine that, earlier that day, the employee received instructions from a senior leader asking them to support a time-sensitive initiative. Their communication history shows that this leader is a legitimate reporting-line superior. Their recent collaboration patterns align with the new project team. Their subsequent activity, while unusual for that individual’s baseline, is consistent with the business task they were assigned.

What initially looked like a risk event may actually be a normal response to business pressure. Without the surrounding context of communication, organizational relationships, and broader behavioral patterns, prompt activity alone could generate more noise than insight.

The reverse is also true. A prompt may appear benign on the surface while the context around it suggests elevated risk. A request that seems routine could originate from a compromised user, a newly connected external agent, a shadow AI workflow, or a user acting outside their normal role. The language itself may not contain anything obviously malicious, but the surrounding conditions may tell a very different story.

What security teams need to analyze prompts effectively

The future of prompt analysis is not just about understanding language. It is about understanding language in context.

To do that well, security teams need more than prompt inspection. They need to understand:

  • Who is issuing the prompt, whether human or agent
  • How that identity normally behaves across the enterprise
  • What systems, data, and workflows are connected to the interaction
  • Which relationships and communications explain the surrounding activity
  • Whether the downstream actions align with expected business behavior

When those layers are absent, prompt analysis can become another isolated control surface: useful in theory, but limited in practice. Security teams may detect unusual wording but miss the operational function behind it, overreact to benign changes in behavior, or miss subtle misuse because the prompt itself did not appear dangerous.

How organizations should think about prompt analysis going forward

Security teams have seen this pattern before. In the cloud, posture without runtime context left important gaps. In identity, access control without behavioral understanding missed misuse that looked legitimate on paper. In data security, content inspection without business context often created friction without resolving risk. AI is exposing the same lesson again: controls are strongest when they are coordinated, not isolated. As organizations work to secure AI and identify gaps across their security operations, prompt analysis will become an increasingly important source of insight, but only as part of a broader strategy.

Prompt analysis will undoubtedly become more common, as prompts are one of the clearest windows into how people and agents are using AI systems. However, what matters most is not simply collecting prompts or filtering dangerous phrases, but being able to place that language inside a wider behavioral and operational picture.

Organizations that already have a broader understanding of how work gets done across the enterprise will be better positioned to make sense of prompt language as this category matures. They will be better able to distinguish urgency from abuse, experimentation from exfiltration, and productive AI adoption from hidden risk.

Figure 1: Darktrace / SECURE AI reconstructs the full sequence of events, showing every user and agent interaction in context, with risky prompts highlighted and categorized, including PII, sensitive data, and other policy violations.

At Darktrace, this is the key lesson emerging from the market: prompt language does matter, but it does not stand alone. It is most valuable when treated as a new behavioral input that can enrich understanding across the enterprise, not as a self-contained source of truth.

Why prompts become less useful when analyzed in isolation

The curious case of prompt language analysis, then, is this: the more important prompts become, the less useful they are in a vacuum.

The real opportunity is not just to see what was asked. It is to understand why it was asked, what it meant in that moment, and what happened next.

For a deeper look at how organizations are approaching this challenge from the strengths of prompt analysis to its limitations in isolation see Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches, which expands on the role prompt-level controls play within a broader, context-driven security strategy.

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About the author
Nabil Zoldjalali
VP, Field CISO

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June 23, 2026

Advancing the Use of Frontier AI in Cybersecurity: Darktrace Joins the OpenAI Daybreak Cyber Partner Program to Explore Defensive AI Integrations

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Darktrace joins the OpenAI Daybreak Cyber Partner Program

Today, we announced that Darktrace is joining the OpenAI Daybreak Cyber Partner Program. We’ll be partnering with OpenAI to explore how their cyber capabilities can be integrated within Darktrace products and services to bring new capabilities to our customers.

This partnership is an exciting opportunity to bring together Darktrace’s behavioral AI modelling of the organization with OpenAI’s advanced contextual capabilities to create a new level of understanding for security teams. To understand the impact, it’s helpful to start with how we think about the problem.  

At Darktrace, we built our AI in support of the core belief that cybersecurity needs to understand the business it is defending. That's why our Self-Learning AI is designed to help organizations understand normal and abnormal behavior for each organization across their digital environment, including users and identities, networks and cloud, email and collaboration tools, and now AI systems and agents with the rollout of Darktrace / SECURE AI™.  

Our goal was never simply to spot known attacks faster. It was to help defenders understand how their organization behaves, potential risks and impact, and where disruption could take hold so they could prepare for the unknown threats that they may not have seen or even imagined before.  

That’s exactly what is happening across the threat landscape today. Attacks keep changing; techniques shift, infrastructure evolves, and attackers move with more speed, precision, and context. And now they have even more AI and automation on their side. Attackers are exploiting identities, trusted services, SaaS applications, and business workflows. They are not always breaking in; often, the threat may come from within the organization in the form of insider threat or even rogue agents.  

In this reality, defenders need a combination of deep AI modelling of the organization and AI that can connect identified threats to concrete business context, translating this information into real world value, and allow action before risk becomes disruption.

That is the opportunity we see in partnering with OpenAI.  

What is the OpenAI Daybreak Cyber Partner Program and why is Darktrace joining

The OpenAI Daybreak Cyber Partner Program is focused on advancing the safe use of AI for cybersecurity. As part of the program’s next phase, OpenAI is working with a select group of trusted partners including Darktrace on scoped product integrations, managed services, and partner-delivered defensive capabilities. We’ll be exploring how OpenAI’s advanced frontier AI capabilities can support defenders in the tools and workflows they already use each day.

For Darktrace, this is a natural extension of our expertise and the work we have been doing for a decade: safely and securely applying the most effective AI techniques in combination to understand organizations, detecting malicious activity at the earliest indicators, and helping cyber defenders act faster.  

By using the advanced models and more precise safeguards available in the OpenAI Daybreak Cyber Partner Program, Darktrace and OpenAI will combine Darktrace’s real-time behavioral understanding of an organization's digital estate with OpenAI's ability to interpret wider business context.  

This is a unique and powerful combination of insights that could give organizations deeper context on technical risk and help them prioritize workloads and investigations based on potential impact to revenue, operations, and resilience. It can also provide security teams and executives with intelligence into which events matter most to the business, why they matter, and what action to take. Not just finding, for instance, that an agent is compromised, but highlighting that the compromised agent could shut down order fulfilment within the next three hours.  

Why the Darktrace and OpenAI partnership matters for defenders

Security teams today have more attack surface, more complex environments to protect, and an increasing volume of threats. The ability to act quickly is critical, but they also need to be able to focus on the risks that could have the greatest business impact.

That is especially important as attackers use AI to scale phishing, automate reconnaissance, find weaknesses, and blend into normal business activity. At the same time, organizations and their employees are using AI to innovate, which introduces an even broader attack surface and new set of risks. Defenders need AI that can operate across the same complexity, but safely, transparently, and in service of building more resilience. And they need a way to safely adopt, govern, and defend AI across their organizations.

Joining the OpenAI Daybreak Cyber Partner Program is another step in that direction. We are still early in this work, and we will take a careful, disciplined approach. But the direction is clear: protecting organizations requires AI that understands the business, not just the attack.

At Darktrace, that is exactly where we remain focused and why we are so excited about this partnership with OpenAI.  

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