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March 22, 2022

Stopping Trickbot: Darktrace's Autonomous Response

Darktrace's autonomous response successfully thwarted a Trickbot intrusion. See how AI played a crucial role in this defense.
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
Tony Jarvis
VP, Field CISO
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22
Mar 2022

In the lead-up to the 2020 US election, Microsoft and its partners attempted to bring down the pernicious Trickbot malware and reduce election tampering attempts. These efforts were successful, to an extent: the takedown effectively eliminated 94% of Trickbot’s infrastructure and massively reduced its influence in late 2020.

Malware rarely stays dead, however. We discussed previously how the arrests which followed REvil’s widespread attacks in 2021 have done little to disrupt that group’s Ransomware-as-a-Service operation, and how Ryuk ransomware fell into new hands after being abandoned by its creators.

Trickbot has seen a resurrection of even greater proportions. By June 2021, when Darktrace detected a Trickbot intrusion in one of its customer environments, the malware was far from a forgotten, ineffectual strain. It had instead become the most prevalent malware in the world.

It was only due to a last-minute activation of Darktrace’s Autonomous Response that this customer was able to avoid falling victim to a successful ransomware attack. Because it can take action at any stage of an attack, Autonomous Response could interrupt Trickbot even after it had taken root within the digital environment, and successfully prevent the execution of ransomware.

Trickbot takes root

The intrusion took place at a public administration organization in the EMEA region. Prior to Darktrace’s deployment, a single internal domain controller had been compromised by Trickbot, which then lay dormant for at least a month. By the time the malware began to take action, however, Darktrace’s AI had been deployed. Despite entering a compromised environment, the AI was able to differentiate between benign and malicious activity and immediately detect the threat, though at this point Autonomous Response was configured to not take any action without human confirmation.

Darktrace detected the compromised domain controller uploading a malicious DLL file – very likely Trickbot itself – to approximately 280 devices in the organization over SMB, and then using Windows Management Instrumentation (WMI) to configure and execute it. Despite Trickbot’s age and infamy, tools dependent on threat intelligence remained silent at this stage.

Figure 1: Timeline of the attack

How attackers resurrected Trickbot

Trickbot’s modular nature makes it a perfect gateway for a host of criminal activities, and keeps the malware itself adaptable and therefore hard to defend against. The action coordinated by Microsoft successfully took down the known IP addresses of multiple Trickbot command and control (C2) servers and temporarily prevented Trickbot operators from purchasing or leasing new ones. But it did not take long for the Trickbot infrastructure to be rebuilt, and in May and June of 2021 it was again deemed the most prevalent malware in a Global Threat Index.

Trickbot’s ability to evolve and circumvent existing OSINT was demonstrated in this attack, as Darktrace noticed 160 of the 280 compromised devices it had detected beginning to connect to a host of new C2 endpoints. None of these had OSINT associating them with malicious activity, but Darktrace considered the activity highly unusual in the context of previous behavior, and the security team were notified of this potential high-severity incident via a Proactive Threat Notification (PTN).

The attackers laid low for over a month, before the compromised devices were detected downloading masqueraded executable files and conducting anomalous scanning activity. These files were likely Ryuk ransomware payloads. By spacing out these stages of the attack, the threat actors made it harder for human teams to connect the dots and reveal the full scope of the threat.

Darktrace’s Cyber AI Analyst, which investigates and triages threats across entire digital environments, was able to piece these disparate events into a single attack narrative, however, and deliver a further PTN. Due to the severity of the situation, the customer submitted to Darktrace’s Ask the Expert (ATE) service to receive assistance with their threat response.

Figure 2: Cyber AI Analyst investigates suspicious executable files being spread to multiple internal devices

Autonomous Response shuts down a late-stage attack

Having understood the scale of the threat they now faced, the team activated Autonomous Response to take autonomous action to contain the threat. If Autonomous Response had been in place from the beginning, it would have stopped this attack in its earliest stages, while it was restricted to a single compromised domain controller. Crucially, however, Autonomous Response can take action at any stage of a ransomware attack.

Even at this late stage, it was able to halt the attackers and prevent Ryuk from being executed on the network. The AI blocked a chain of malicious activities including SMB enumeration, networking scanning, and suspicious outbound connections in seconds, disrupting the attack while enforcing normal business operations to ensure that the rest of the company’s work could continue uninterrupted.

With their C2 communications and lateral movement efforts disrupted, the attackers were unable to execute Ryuk, and the attack came to an end just in time. It is likely that this last-minute activation of Autonomous Response avoided widespread data encryption and possibly exfiltration, as well as the numerous costs which follow a successful ransomware attack even if a ransom is paid.

Deploying Autonomous Response before it’s too late

Despite only being activated once the attack had taken root, Darktrace was still able to distinguish malicious activity from normal business operations and stop the threat without causing disruption. Next time an attack strikes, this organization will be prepared with Autonomous Response in fully autonomous mode from the outset, ready to take action at the first sign of an emerging threat and minimize their remediation efforts.

The journey to fully autonomous security requires organizations to build trust in AI’s accuracy and decision-making. What this journey looks like for each individual organization will differ, but the need for technology that can autonomously respond to emerging threats is not a lesson any organization ought to learn the hard way.

Thanks to Darktrace analyst Sam Lister for his insights on the above threat find.

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
Tony Jarvis
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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