Blog
/
Email
/
February 2, 2023

Securing Credit Unions: Darktrace Email Security

Discover how Darktrace ensures compliant email security for credit unions. Learn about advanced risk management and protection strategies.
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
Director of ITIS
Finance (Guest Contributor)
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
02
Feb 2023

As the Director of ITIS for a credit union in the American Pacific Northwest, I know that if malware breaches our internal systems, it will debilitate us and affect the financial wellbeing of our 10,000 members. 

My security team must protect our cyber infrastructure, including our online banking, internal network, and employee email systems. As part of that effort, we are tasked heavily by the Credit Union National Association (CUNA) and the federal government to follow specific, regularly changing standards for our IT security. 

To meet those compliance standards, we deployed Darktrace. Once its AI learned our digital landscape, we could customize the settings to react in specific ways that adhere to compliance frameworks, and we can easily adapt to all changes that we’ve seen.

Darktrace learns the usual behavior of every device and user within our digital landscape. It then uses this understanding to identify threats within seconds and make autonomous, precise decisions that neutralize attacks without disrupting our operations. 

Since we have five locations with hundreds of computers, servers, and switches, I don’t have the capacity to watch every system. However, using network mapping and traffic moderating capabilities, Darktrace gathers all the information I could need. It then generates clear, detailed reports through Explainable AI. 

With its autonomous capabilities, Darktrace helps us stay compliant and stop attacks faster and more reliably than humans, saving my team both time and money. 

Stopping Email Threats with Nuanced Interventions

In my experience, most breaches happen through email. I can control most web traffic with firewall rules and third-party tools. I can’t control, however, if a user clicks on something in a malicious email. 

Darktrace/Email uses AI to identify and stop malicious email activity before it ever reaches a user’s inbox. It can take more detailed actions beyond merely allowing or blocking emails. Instead, it will neutralize the threatening components of emails. I especially love its ability to flatten any attachment into a PDF. 

Since deploying Darktrace, I haven’t had a security breach that I couldn’t explain or fix. Darktrace has even blocked malicious emails that made it through my outside spam folder and internal exchange filter. 

The metrics it provides internally are amazing, too. I can tell who’s moving files, where they’re moving files, what files they’re moving, if they are plain text passwords or shares or other sensitive information. At a glance, Darktrace does everything that would take me hours to trace down. 

With this comprehensive visibility, we’ve started using Darktrace/Email in some unique ways. For example, we pull Darktrace’s metric breakdown of email traffic and feed it into a datamining program to see the efficacy of our marketing email campaigns. 

Beyond the metrics, Darktrace’s ability to autonomously respond to threats gives me peace of mind. I have a machine that watches our email and network around the clock. Beyond stopping breaches from originating in our email systems and shutting down malicious activity in our network, Darktrace brings our email and network data together to make its AI even smarter. I know that when we fall victim to a cyber-attack, Darktrace will handle it. 

Preempting Attacks by Understanding Our External Footprint

External footprint monitoring is an integral part of internal security because detecting and stopping an attack once it is launched is one thing, but being able to preempt an attack is even better. That’s why I deployed Darktrace PREVENT/Attack Surface Management™ (ASM) as soon as I could. It enables me to take a proactive approach and minimize risk before an attack ever occurs. 

PREVENT/ASM generates objective reports based entirely on my unique footprint. It took only 10 days from its implementation until it identified all the assets that were out there, including some we weren’t aware of. 

Now, two months later, it continues to monitor our ever-changing attack surface, informing us of vulnerabilities such as shadow IT, misconfigurations, and brand abuse. When it identifies threats, it generates digestible reports that I pass along to our third-party contractor to handle.  

However, PREVENT’s power is amplified when paired with Darktrace DETECT™ and Darktrace RESPOND™. These three tools work together in the Cyber AI Loop™ to harden our entire security stack.  

Since PREVENT can see potential avenues of attack in advance, the Loop can leverage this data to increase sensitivity in DETECT and RESPOND around these critical access points and inform my security team where to prioritize our resources to have the highest impact.

It’s hard to choose which capability of Darktrace has helped my team the most, because with the feedback loop, I now think of it holistically. Darktrace simply provides the value that I’m paying for, and I’m glad that I have it. As far as security software goes, it’s probably the slickest piece of software I’ve seen in my life, and I’ve been doing this for 30 years. 

My advice to other financial institutions is that if you don’t have an AI security system, you need it. Threat actors have started using AI in their attacks, so we need to use AI to protect against them. Otherwise, it’s like fighting a jet plane with a rock and a stick. With this proactive approach, especially with PREVENT, Darktrace is working all the time to protect our digital estate, harden our security posture, and meet our compliance standards. 

Darktrace’s free Proof of Value gives you the opportunity to speak directly with a Darktrace customer in a 1-1 reference call. Start a trial today.

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
Director of ITIS
Finance (Guest Contributor)

More in this series

No items found.

Blog

/

/

December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

2026 cyber threat trendsDefault blog imageDefault blog image

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.  

Continue reading
About the author
The Darktrace Community

Blog

/

Email

/

December 22, 2025

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

Man at laptopDefault blog imageDefault blog image

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.

[related-resource]

Continue reading
About the author
Carlos Gray
Senior Product Marketing Manager, Email
Your data. Our AI.
Elevate your network security with Darktrace AI