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July 12, 2020

Darktrace AI Email Finds Chase Fraud Alert

Stop Chase fraud alerts! Learn how Darktrace AI email security caught a malicious email impersonating Chase bank, preventing credential theft in real time.
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
Mariana Pereira
VP, Field CISO
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12
Jul 2020

In a previous blog, we analyzed a phishing attack that impersonated QuickBooks, an accounting software, in an attempt to install malware across an organization. This blog demonstrates another recent threat find where the brand of a trusted financial organization was leveraged to launch an email attack.

With an annual revenue of over $100 billion, Chase is the second largest issuer of credit cards in the US. It is unsurprising that this well-known, trusted brand is used by attackers in phishing attacks. With the recent surge in e-commerce transactions, together with increased scrutiny regarding digital security, consumers are on high-alert when it comes to the security of their banking details. A ‘fraud alert’ from a financial institution triggers stress and anxiety, and recipients may rush to take action, forgetting security training and clicking on links even if they appear to be suspicious. By playing on human emotions, attackers increase their likelihood of success.

The anatomy of an attack

An attacker appears to have invested a significant amount of research and preparation into crafting a legitimate-looking Chase fraud alert.

Figure 1: A partial recreation of the malicious email

In the phishing email above the recipient is asked to confirm that a listed transaction is legitimate. The notification, whether received through email, text message, or an app, will usually include the name of the vendor, date and time of the transaction, and the amount of money. The attacker has gone to the trouble to replicate this, listing specific suspicious transactions.

Attackers often leverage well-known brands like Chase to indiscriminately target a large pool of inboxes. They are statistically likely to find a Chase customer without having to go through the effort of actually hacking Chase’s CRM.

But while emails like these bypass legacy tools and often fool the human recipient, they are easily detected by Antigena Email’s contextual understanding of anomalous activity and stopped by its autonomous response.

How AI caught the fake fraud alert

In this case, as soon as the spoofed fraud alert hit the inbox, Antigena Email detected that the email was unusual, giving the email an 100% anomaly score.

100%

Mon Jun 22 2020, 10:38:34

From:Chase Fraud Alert <chase@fraudpreventino.czh.com>

Recipient:Kirsty Dunhill <kirsty.dunhill@holdingsinc.com>

Action Needed: Confirm you made these purchases

Email Tags

Suspicious Link

New Contact

Unknown Correspondent

Actions on Email

Lock Link

Hold Message

Figure 2: Darktrace’s AI surfacing the email as 100% anomalous

With this high anomaly score indicating a highly unusual email, Antigena Email automatically held it back from the user’s inbox.

The sender’s domain, ‘fraudpreventino’, is visually similar to ‘fraudprevention’ – the domain of the legitimate website – so the look-a-like could be easily misread as legitimate by a user.

However, in Antigena Email dashboard’s advanced tab, we see the metrics for KCE and KCD are both 0, indicating that this is a new email address that has not previously corresponded with either the recipient or anyone else within the organization. Additionally, we can see that DKIM failed and there is no SPF record, and so there were no records to validate the authenticity of the email.

Figure 3: The Threat Visualizer shows the emails have failed SPF and DKIM checks

Antigena Email detected other unusual aspects of the email indicating that it was an attack. The email contained a number of anomalous links and there was an inconsistency between the displayed link address and the actual destination of the hyperlink.

The display link in this particular email was a newly registered domain at the time the email was sent. Not surprisingly, this domain is now being identified as a malicious page. However, at the time the email was sent, the domain was not listed on ‘deny lists’ and would have slipped past spam filters or legacy security tools.

Upon clicking the link, the user would have been presented with a fraudulent Chase login screen. This is a common credential harvesting technique – when the user enters their credentials, they unknowingly hand over this information to the attacker.

Figure 4: The fake Chase login screen with credential harvesting malware

The website has now also been recognized as malicious, with users now presented with a warning encouraging them to think twice before entering sensitive information.

Figure 5: The page is later recognized as harmful by the web browser

It is not clear how long the fake login page was in existence before it was added to ‘denylists’, but what is certain is that Antigena Email was able to prevent the attack by holding back the email even without any threat intelligence on the attacker technique, ensuring no damage was done.

Figure 6: Antigena Email recognizes when a malicious link is hidden behind a misleading button

In addition to this button, the attacker also took time to add many legitimate Chase links and images. By padding the email with mostly valid content and links, the attacker attempted to deceive legacy email security tools into perceiving the email as benign. Notice below that these all link to the legitimate address for ‘fraudprevention,’ which itself was used as the source of the altered domain name for the sender.

Figure 7: The full list of links contained in the email

Defending against sophisticated phishing attacks

Attackers continue to leverage social engineering tactics to play on human error and fear in increasingly targeted phishing attacks, crafting nuanced misspellings in their domain names, padding emails with legitimate links, and creating a false sense of urgency. Self-learning AI that can spot and stop threats with both machine speed and precision becomes a critical tool at a time when humans have become even more susceptible as people’s stress and anxiety levels have become heightened by global disruption.

Of course, in this attack there is an irony in that the order of operations is directly inverted: first comes the notification, then comes the fraud. But with Antigena Email, attacks like this are stopped in their tracks, protecting employees and organizations from harm.

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
Mariana Pereira
VP, Field CISO

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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December 22, 2025

Why Organizations are Moving to Label-free, Behavioral DLP for Outbound Email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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