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February 13, 2025

Forensic Victory: Catching the Ransomware EDR Couldn't See

This blog details a simulation of a ransomware attack that bypassed EDR, simulated via a ClickFix social engineering technique. The attack used an obfuscated HTML and custom C++ binary to encrypt files and establish a reverse shell. Cado's forensic platform then demonstrated how to trace the attack chain, highlighting the need for robust DFIR beyond EDR.
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
Nate Bill
Threat Researcher
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13
Feb 2025

Introduction: Catching the ransomware EDR couldn't see

Endpoint Detection & Response (EDR) is frequently used by organizations as the first line of defense against cyberattacks. EDR platforms monitor organizations’ endpoints (servers, employee laptops, etc.) and detect and contain malicious activity running where possible. This blog will explore a ransomware attack in a lab environment, using payloads inspired from real attacks.

The incident

For this experiment, Cado Security Labs (now part of Darktrace) set up an up-to-date Windows machine, with a mainstream EDR tool installed, and simulated a ClickFix attack [1] against the user, which relies on socially engineering the user into running malicious commands.

During the first stage of the attack, the fake end user receives a phishing email with a ClickFix attachment:

Test Email Screenshot
Figure 1: Test Email

As this is a test, the email was kept fairly short. However, an attacker in a real-world setting would make the email far more convincing to view. In the real world, this type of attack is often seen being used with fake invoices being sent to finance staff.

After opening up the HTML, the end user is presented with the following page:

ClickFix HTML
Figure 2: The ClickFix HTML the user is presented with as part of our simulated attack

This is taken from a real attack where a Microsoft Word online page is mimicked, prompting the user to interact with it. The user needs to interact with the button, as most browsers will block clipboard writes unless the user has interacted with an element. Clicking the button copies a command to the user’s clipboard, and updates the instructions to tell them to press Win + R, Ctrl + V, and then Enter. If the user does this, it will open the run dialog, paste in the command, and execute it. This approach capitalizes on the typical user's lack of comprehension or uncritical adherence to directives, a tactic that has demonstrated efficacy in real-world cyberattacks.

It is worth noting that the EDR tool flagged this stage during initial testing. However, adding a layer of obfuscation to the HTML allowed for bypass detection. The page was able to be encoded, decoded and then written to the document using reflection to access methods that would normally be flagged.

Once the command is executed, PowerShell is invoked to download and run an .exe file from an attacker-controlled server.

The payload is a custom C++ binary that was developed for the purpose of this test. The binary spawns a reverse shell, as well as encrypting all of the files in the Documents folder for ransom. This binary was iteratively tested against the EDR tool, and the functionality was tweaked each time to bypass elements that were getting detected. Bypassing the EDR tool did not require any fancy techniques. Simply using a different Windows API to accomplish a goal that was previously flagged by the EDR tool, or altering the behavior, timing, and ordering of activities performed was sufficient to evade detection. This may seem surprising that sophisticated techniques aren’t strictly required to be undetected.

The aftermath of the attack can be seen in the images below, with a ransom note being written, and our important documents no longer being readable.

Ransom Note
Figure 3: The Ransom Note
Error Message
Figure 4: The aftermath of trying to open one of the PDFs

With no alerts to investigate from the EDR tool - how could a blue team uncover this attack chain after the fact for incident response?  

Investigating the artifacts with cado

Using Cado (acquired by Darktrace), we can import the affected VM directly with just a few clicks.

Cado UI
Figure 5: Import the affect VM  

The ransom note is a good starting point for the investigation. The timeline search feature quickly finds entries that show what process made the readme.txt file.

Event information
Figure 6: Timeline search feature

It shows that the ransom note was created by the process fix.exe, which can be used to pivot off and build a better understanding of what else the malware did, and how it got onto the system.

Reviewing events relating to the fix.exe payload shows that an event established a connection to a server, in this case, an attacker-controlled C2 server. It also spawned a command prompt instance, which provides a remote shell to the attacker.

Event information
Figure 7: Event Information
Event information
Figure 8: Event Information showing ransomware

Looking at the event information, it’s easy to spot the ransom attacks against the files. For example, the ransom attack modified the internal_draft_important.pdf document, which was seen before it can no longer be opened.

Event information
Figure 9:  Event information showing the modified document

And finally reaching the start of the log trail relating to the payload, it shows it initially being executed by PowerShell.

Event information
Figure 10: Event information showing PowerShell

However, this does not definitively show what caused the malware to run in the first place, and so the next step is running the pivot feature to find related events.

Pivoting off the event allows for quickly figuring out this was precipitated by a visit to obfuscated.html, which was downloaded from an email in Outlook online:

Related Events
Figure 11: Related events showing that the attack was precipiated by a visit to a obfuscated.html

The Cado Platform [2] also allows for directly jumping to the file in the file browser to conduct further analysis:

Cado UI screenshot
Figure 12: File seen in file browser

An EDR platform usually only provides an alert, process snapshot, and event details for a singular moment in time, missing the vital context needed to successfully understand the attack. Cado provides the vital context needed to successfully understand the full scope of the attack, not just its entry point.

Key takeaways

This research covered how Cado can provide the ability to forensically analyze systems and fully understand how attacks have occurred and unfolded. Defense-in-depth is a core component of cybersecurity, and being entirely reliant on an EDR platform as your only line of defense and insight into attacks can leave you without full  context.

This was an example only, and a finely tuned EDR platform would likely detect an attack similar to this. However, many organizations may overlook the forensics side of Digital Forensics and Incident Response [3], and remediate incidents solely using their EDR platform. This can result in organizations missing out on the complete picture of an attack, potentially leaving them open to re-infection. A DFIR platform is vital to respond quickly to incidents across Cloud, SaaS, and on-prem.

References

[1] https://www.darktrace.com/blog/unpacking-clickfix-darktraces-detection-of-a-prolific-social-engineering-tactic  

[2] https://www.darktrace.com/forensic-acquisition-investigation

[3] https://www.darktrace.com/cyber-ai-glossary/digital-forensics-incident-response

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
Nate Bill
Threat Researcher

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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.

[related-resource]

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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