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June 16, 2021

Stopping Corp-Internal Phishing Attacks with Darktrace

Discover how Darktrace Email stopped a series of multi-language phishing attacks, including an Emotet campaign in Japanese. Learn how Darktrace can help!
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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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16
Jun 2021

Click here! Clique aqui! ここをクリック! Klikk here! !اینجا کلیک کنید naDev yIbej! Hic tange!

Language is deceptive. In the realm of email security, language can deceive a recipient into clicking a link or completing a transaction, and it can trick a security tool into thinking an email is legitimate.

It is for this reason that Darktrace/Email is not reliant on language, but rather uses mathematics to develop an understanding of ‘normal’ for every email user in an organization. This enables it to neutralize anomalous emails indicative of a threat around the world, no matter in what format or language they come.

Natural language processing

When it comes to catching a compromised account or impersonation email, how can you teach a computer to understand intent or a change of tone, compared to the normal way a person corresponds?

One of the most common approaches in email security is natural language processing. NLP looks at how to program computers to analyze natural language, commonly by exposing them to a large volume of data.

The result is a computer capable of ‘understanding’ the contents of documents, including the nuances of the language within them. The technology can then extract information in the documents as well as categorize and organize the documents themselves.

Modern-day limitations

However, using NLP is limited in scope for email security as it will often misunderstand specific jargon or colloquialisms, as well as terms that had not been invented when the computer was programmed, unless it is trained on these too. Each additional language requires the computer to learn from zero every time. NLP only works on the regional languages it has been trained on, and it is not commercially viable to teach the technology to work in all small markets.

If a company hires an email security vendor based in America, therefore, it is probable that the security vendor has invested most of their time in detecting English-based phishing threats. That is fine if the company only communicates in English, but this is often not the case. In a 21st century globalized world, the need for security technology to be language-agnostic is more critical than ever.

Not all AI is the same: Unsupervised machine learning

Darktrace/Email relies on unsupervised machine learning, which can learn on the job and does not need to be fed large data sets. It can glean insights from NLP for good measure, but it does not depend on NLP for detection or understanding.

When working with AI it is crucial to understand how the AI learns: does it learn on the job or was it trained with a labeled data set? This is particularly important when looking to understand the intent behind an email, specifically to uncover solicitation attempts either through spoofing, phishing, impersonation of a supplier or any other form of email attack.

Rather than teaching a computer to understand language in an email, Darktrace Cyber AI dynamically assesses activity across inbound and outbound emails including senders, recipients, links, IP addresses, and attachment types. The movement of all these objects are then used by the AI to create the ‘patterns of life’ for every user across all communications, including communications with external users who frequently correspond with a given business.

By taking a mathematical approach, Darktrace/Email is able to understand ‘normal’ for any user regardless of the dialect they are corresponding in, uniquely interpreting all languages from Norwegian to Latin and Persian, and subsequently identifying subtle anomalies indicative of a phishing attack or an account takeover.

Catching Emotet in Japanese

Last year, Darktrace uncovered a sophisticated Spamware campaign which leveraged Emotet, the infamous banking malware. The campaign targeted various industries with highly sophisticated phishing emails.

At a food production company in Japan, Darktrace detected six phishing emails sent over a two-day period in July.

Figure 1: An email from the Emotet campaign.

In the email above, both the subject line and the filename translate to “Regarding the invoice,” followed by a number and the date. The attacker was clearly trying to imitate a legitimate business email here, spoofing a well-known Japanese company (三菱食品(株)) and a common Japanese name (‘藤沢 昭彦’).

Darktrace/Email revealed key metrics behind the email including that the real sender was using a domain name from GMO, a Japanese company which offers cheap web email services, and that the sender’s location was actually Portugal, not Japan.

Figure 2: Darktrace/Email detects the attempt at inducement.

Darktrace/Email’s models recognized the topic anomalies and inducement attempts in the emails, regardless of the language they had been written in – giving a high anomaly score of 85. Furthermore, Darktrace’s AI determined that the extension and the MIME type in the attachments were anomalous, when compared to the documents which the user normally exchanges via email.

Portuguese threat find

In another instance, a series of malicious emails were sent to an organization in Europe. These emails used several tactics to bypass the company’s security tools, including personalized subject lines and hidden malicious URLs.

Figure 3: An interactive snapshot of Darktrace/Email’s user interface. The subject line reads ‘Notice of transfer.’

As displayed above, the email contained a link that appeared to lead to a CaixaBank domain. However, Darktrace/Email recognized this as a deliberate attempt to mislead the recipient and revealed that the link in fact led to a WordPress domain, which Cyber AI identified as 100% rare for the business.

A closer inspection revealed that these emails were sent from Vietnam. The sender had never been in any previous correspondence with the business, and the isolated link within the email was also marked as a 100% rare domain. Darktrace/Email held these malicious emails back, protecting the organization from harm.

Universal defense

These two examples demonstrate the benefits of an unsupervised machine learning approach. An AI security solution which analyzes hundreds of different metrics and does not rely on pre-existing data is a groundbreaking advantage when faced with global phishing threats that now utilize a wide range of languages.

Email-based attacks are becoming more targeted and more convincing by the day. Targeted social engineering and spear phishing with advanced translation tools bombard companies daily, in all languages.

Whether it’s a phishing attack against a local office in Korea or a solicitation attempt in Arabic – even a malicious email written in Klingon from a Star Trek convention – or any of the thousands of email exchanges which occur in countless vernaculars and tones, Darktrace/Email can keep your company safe across the world, and beyond.

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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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May 19, 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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May 18, 2026

AI Insider Threats: How Generative AI is Changing Insider Risk

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How generative AI changes insider behavior

AI systems, especially generative platforms such as chatbots, are designed for engagement with humans. They are equipped with extraordinary human-like responses that can both confirm, and inflate, human ideas and ideology; offering an appealing cognitive partnership between machine and human.  When considering this against the threat posed by insiders, the type of diverse engagement offered by AI can greatly increase the speed of an insider event, and can facilitate new attack platforms to carry out insider acts.  

This article offers analysis on how to consider this new paradigm of insider risk, and outlines key governance principles for CISOs, CSOs and SOC managers to manage the threats inherent with AI-powered insider risk.

What is an insider threat?

There are many industry or government definitions of what constitutes insider threat. At its heart, it relates to the harm created when trusted access to sensitive information, assets or personnel is abused bywith malicious intent, or through negligent activities.  

Traditional methodologies to manage insider threat have relied on two main concepts: assurance of individuals with access to sensitive assets, and a layered defense system to monitor for any breach of vulnerability. This is often done both before, and after access has been granted.  In the pre-access state, assurance is gained through security or recruitment checks. Once access is granted, controls such as privileged access, and zero-trust architecture offer defensive layers.

How does AI change the insider threat paradigm?

While these two concepts remain central to the management of insider threats, the introduction of AI offers three key new aspects that will re-shape the paradigm:.  

AI can act as a cognitive amplifier, influencing and affecting the motivations that can lead to insider-related activity. This is especially relevant for the deliberate insider - someone who is considering an act of insider harm. These individuals can now turn to AI systems to validate their thinking, provide unique insights, and, crucially, offer encouragement to act. With generative systems hard-wired to engage and agree with users, this can turn a helpful AI system into a dangerous AI hype machine for those with harmful insider intent.  

AI can act as an operational enabler. AI can now develop and increase the range of tools needed to carry out insider acts. New social engineering platforms such as vishing and deepfakes give adversaries a new edge to create insider harm. AI can generate solutions and operational platforms at increasing speeds; often without the need for human subject matter expertise to execute the activities. As one bar for advanced AI capabilities continues to be raised, the bar needed to make use of those platforms has become significantly lower.

AI can act as a semi-autonomous insider, particularly when agentic AI systems or non-human identities are provided broad levels of autonomy; creating a vector of insider acts with little-to-no human oversight or control. As AI agents assume many of the orchestration layers once reserved for humans, they do so without some of the restricted permissions that generally bind service accounts. With broad levels of accessibility and authority, these non-human identities (NHIs) can themselves become targets of insider intent.  Commonly, this refers to the increasing risks of prompt injection, poisoning, or other types of embedded bias. In many ways, this mirrors the risks of social engineering traditionally faced by humans. Even without deliberate or malicious efforts to corrupt them, AI systems and AI agents can carry out unintended actions; creating vulnerabilities and opportunities for insider harm.

How to defend against AI-powered insider threats

The increasing attack surfaces created or facilitated by AI is a growing concern.  In Darktrace’s own AI cybersecurity research, the risks introduced, and acknowledged, through the proliferation of AI tools and systems continues to outstrip traditional policies and governance guardrails. 22% of respondents in the survey cited ‘insider misuse aided by generative AI’ as a major threat concern.  And yet, in the same survey, only 37% of all respondents have formal policies in place to manage the safe and responsible use of AI.  This draws a significant and worrying delta between the known risks and threat concerns, and the ability (and resources) to mitigate them.

What can CISOs and SOC leaders do to protect their organization from AI insider threats?  

Given the rapid adaptation, adoption, and scale of AI systems, implementing the right levels of AI governance is non-negotiable. Getting the correct balance between AI-driven productivity gains and careful compliance will lead to long-term benefits. Adapting traditional insider threat structures to account for newer risks posed through the use of AI will be crucial. And understanding the value of AI systems that add to your cybersecurity resilience rather than imperil it will be essential.

For those responsible for the security and protection of their business assets and data holdings, the way AI has changed the paradigm of insider threats can seem daunting.  Adopting strong, and suitable AI governance can become difficult to introduce due to the volume and complexity of systems needed to be monitored. As well as traditional insider threat mitigations such as user monitoring, access controls and active management, the speed and autonomy of some AI systems need different, as well as additional layers of control.  

How Darktrace helps protect against AI-powered insider threats

Darktrace has demonstrated that, through platforms such as our proprietary Cyber AI Analyst, and our latest product Darktrace / SECURE AI, there are ways AI systems can be self-learning, self-critical and resilient to unpredictable AI behavior whilst still offering impressive returns; complementing traditional SOC and CISO strategies to combat insider threat.  

With / SECURE AI, some of the ephemeral risks drawn through AI use can be more easily governed.  Specifically, the ability to monitor conversational prompts (which can both affect AI outputs as well as highlight potential attempts at manipulation of AI; raising early flags of insider intent); the real-time observation of AI usage and development (highlighting potential blind-spots between AI development and deployment); shadow AI detection (surfacing unapproved tools and agents across your IT stack) and; the ability to know which identities (human or non-human) have permission access. All these features build on the existing foundations of strong insider threat management structures.  

How to take a defense-in-depth approach to AI-powered insider threats

Even without these tools, there are four key areas where robust, more effective controls can mitigate AI-powered insider threat.  Each of the below offers a defencce-in-depth approach: layering acknowledgement and understanding of an insider vector with controls that can bolster your defenses.  

Identity and access controls

Having a clear understanding of the entities that can access your sensitive information, assets and personnel is the first step in understanding the landscape in which insider harm can occur.  AI has shown that it is not just flesh and bone operators who can administer insider threats; Non-Human Identities (such as agentic AI systems) can operate with autonomy and freedom if they have the right credentials. By treating NHIs in the same way as human operators (rather than helpful machine-based tools), and adding similar mitigation and management controls, you can protect both your business, and your business-based identities from insider-related attention.

Visibility and shadow AI detection

Configuring AI systems carefully, as well as maintaining internal monitoring, can help identify ‘shadow AI’ usage; defined as the use of unsanctioned AI tools within the workplace1 (this topic was researched in Darktrace’s own paper on "How to secure AI in the enterprise". The adoption of shadow AI could be the result of deliberate preference, or ‘shortcutting’; where individuals use systems and models they are familiar with, even if unsanctioned. As well as some performance risks inherent with the use of shadow AI (such as data leakage and unwanted actions), it could also be a dangerous precursor for insider-related harm (either through deliberate attempts to subvert regular monitoring, or by opening vulnerabilities through unpatched or unaccredited tooling).

Prompt and Output Guardrails

The ability to introduce guardrails for AI systems offers something of a traditional “perimeter protection” layer in AI defense architecture; checking prompts and outputs against known threat vectors, or insider threat methodologies. Alone, such traditional guardrails offer limited assurance.  But, if tied with behavior-centric threat detection, and an enforcement system that deters both malicious and accidental insider activities, this would offer considerable defense- in- depth containment.  

Forensic logging and incident readiness response

The need for detection, data capture, forensics, and investigation are inherent elements of any good insider threat strategy. To fully understand the extent or scope of any suspected insider activity (such as understanding if it was deliberate, targeted, or likely to occur again), this rich vein of analysis could prove invaluable.  As the nature of business increasingly turns ephemeral; with assets secured in remote containers, information parsed through temporary or cloud-based architecture, and access nodes distributed beyond the immediate visibility of internal security teams, the development of AI governance through containment, detection, and enforcement will grow ever more important.

Enabling these controls can offer visibility and supervision over some of the often-expressed risks about AI management. With the right kind of data analytics, and with appropriate human oversight for high-risk actions, it can illuminate the core concerns expressed through a new paradigm of AI-powered insider threats by:

  • Ensuring deliberately mis-configured AI systems are exposed through regular monitoring.
  • Highlighting changes in systems-based activity that might indicate harmful insider actions; whether malicious or accidental.
  • Promoting a secure-by-design process that discourages and deters insider-related ambitions.
  • Ensuring the control plane for identity-based access spans humans, NHIs and AI models, and:
  • Offering positive containment strategies that will help curate the extent of AI control, and minimize unwanted activities.

Why insider threat remains a human challenge

At its root, and however it has been configured, AI is still an algorithmic tool; something designed to automate, process and manage computational functions at machine speed, and boost productivity.  Even with the best cybersecurity defenses in place, the success of an insider threat management program will still depend on the ability of human operators to identify, triage, and manage the insider threat attack surface.  

AI governance policies, human-in-the-loop break points, and automated monitoring functions will not guard against acts of insider harm unless there is intention to manage this proactively, and through a strong culture of how to guard against abuses of trust and responsibility.

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Jason Lusted
AI Governance Advisor
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