ブログ
/
/
June 13, 2021

Neutralizing QakBot: Darktrace SOC's Success Story

Learn about the strategies used by Darktrace's SOC team to neutralize the QakBot banking trojan and safeguard financial data.
No items found.
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.
No items found.
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
13
Jun 2021

While cutting-edge technology is essential for organizations to secure their digital assets, having on-hand human support to deal with threats can be invaluable for lean security teams and organizations without Autonomous Response in their digital enterprise.

Cyber AI technology recently detected the QakBot banking trojan in a customer environment, and with the help of Darktrace’s SOC team, the customer was able to shut down the attack in under two hours.

QakBot malware

QakBot has built a name for itself over the past twelve years as one of the most deadly trojans in the game. Used in fast-paced, automated attacks against individual businesses, it has the ability to drain company resources and steal vast amounts of financial data. It is often downloaded during Emotet campaigns to infect devices and harvest bank account information.

Like other banking trojans, QakBot uses a dropper to install itself on a corporate device. It then self-propagates through a system and collects credentials at machine speed. Cyber-criminals can use this information to extract private data or distribute ransomware and further malicious payloads.

QakBot is extremely difficult for traditional security tools to detect. Due to a combination of its automatic worm-like capabilities, its use of a virus dropper with delayed execution, and several other obfuscation methods, it is able to bypass the majority of legacy tools and can lead to extreme financial repercussions if not dealt with in its initial stages.

The Darktrace SOC team

Darktrace’s Security Operations Center (SOC) team, located in Cambridge, San Francisco, and Singapore, deal with a wide range of these quick-moving and stealthy threats which are identified by Cyber AI, including ransomware deployments, SaaS account takeovers, and data exfiltration.

Such attacks often use ‘Living off the Land’ techniques which make them difficult to differentiate from legitimate network traffic. Moreover, many threat actors carry out malicious activities outside of a target organization’s normal working hours, amplifying the potential impact of a breach before it is discovered.

The Darktrace SOC team provides around-the-clock coverage of customer environments through Proactive Threat Notification (PTN) and Ask the Expert (ATE) services. Alongside autonomous AI detection, these services provide additional human monitoring and support for customers undergoing significant security events.

Uncovering the QakBot banking trojan

Figure 1: Timeline of the QakBot banking trojan attack, including the response from Darktrace’s services.

At a company in the EMEA region with around 7,000 devices, Cyber AI detected the early signs of a trojan horse. The organization did not have Antigena Email analyzing its email traffic in order to respond to attacks in the inbox, so when a phishing email slipped through the gateway and was opened by a user, their device began connecting to a high volume of suspicious endpoints.

This resembled command and control (C2) communication, and, based on the unusual nature of this activity for the device and the environment, this behavior triggered multiple high scoring model breaches. One of these was a high fidelity model breach for ‘Suspicious SSL Activity’, which prompted an investigation through the Proactive Threat Notification service.

Figure 2: An example of the Cyber AI Analyst incident timeline for an infected device, showing command and control and reconnaissance activity.

An expert Darktrace analyst was alerted to the unusual connectivity by the Enterprise Immune System and began to investigate the anomalous behavior, determining that this device was exhibiting strong signs of a banking trojan infection. The analyst needed to move quickly: the trojan had immediately begun reconnaissance and was preparing to spread across the network.

Within an hour, the analyst had produced a brief report summarizing the activity and this was sent as a PTN alert to the customer. The report contained key technical information from the model breach and Cyber AI Analyst incident – including the timeframe, device hostname and IP address, suspicious external domains, and a reference for the customer to view this alert in the Darktrace UI.

Figure 3: Visual example of the Darktrace threat tray. In the QakBot attack, four Enhanced Monitoring model breaches were triggered, and these were investigated and alerted through the PTN service. They were all high scoring detections, clearly indicating a compromise.

Upon receiving the alert, the customer initiated further investigation and quickly shut down the affected device. The attack was contained in less than two hours.

Ask the Expert

After their initial remediation, the company reached out to the Darktrace team via Ask the Expert to confirm that this was a QakBot infection and to gain additional assistance in investigating the extent of the compromise.

The analyst team provided ongoing support to the investigation over the next six hours, concluding that this likely came from a phishing email and that no other devices in the environment were compromised. The analyst provided a list of observed Indicators of Compromise (IoCs) and worked with the customer to add these to the Darktrace Watched Domains List for further monitoring. The customer was also able to use this list to block the IoCs at the firewall.

The organization contained the infection, and no further suspicious behavior was observed from network devices.

Humans and AI

This case study is a perfect example of how Darktrace’s services provide constant assistance to customers every day of every week. On top of Darktrace’s advanced machine learning technology, the Darktrace SOC team serves as an additional layer of support for security teams of all sizes. Proactive Threat Notifications offer an extra set of eyes on emerging threats, while Ask The Expert provides a mechanism for customers to gain investigative support directly from Darktrace analysts.

The early detection of this banking trojan allowed the organization to deal with the threat before it could develop into a serious infection or a ransomware attack. QakBot is just one of many strains of swift self-spreading malware in today’s threat landscape. Such automated attacks consistently outpace the fastest of human defenders, exposing the desperate need for AI and autonomous systems to augment human teams and protect digital systems in real time.

If Antigena Network had been active in this environment, the suspicious external connectivity would have been blocked upon first detection, stopping the attack within seconds. In fact, the customer decided to deploy Antigena Network following this incident, and now benefits from 24/7 Autonomous Response against all emerging cyber-threats.

IoCs:

nerotimethod[.]com193[.]29[.]58[.]17345[.]32[.]211[.]20754[.]36[.]108[.]120144[.]139[.]166[.]1875[.]67[.]192[.]125 149[.]28[.]101[.]9037[.]211[.]90[.]17568[.]131[.]107[.]37162[.]222[.]226[.]194mywebscrap[.]com

Darktrace model detections:

  • Compromise / SSL or HTTP Beacon
  • Compromise / Suspicious SSL Activity
  • Device / Multiple C2 Model Breaches
  • Device / Lateral Movement and C2 Activity
  • Device / Multiple Lateral Movement Model Breaches
  • Device / Large Number of Model Breaches
  • Compromise / Suspicious Beaconing Behaviour
  • Compromise / SSL Beaconing to Rare Destination
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / High Volume of Connections with Beacon Score
  • Anomalous Connection / Suspicious Self-Signed SSL
  • Anomalous Connection / Rare External SSL Self-Signed
  • Device / Reverse DNS Sweep
  • Unusual Activity / Possible RPC Recon Activity
  • Device / Active Directory Reconnaissance
  • Device / Network Scan - Low Anomaly Score
  • Anomalous Connection / SMB Enumeration

No items found.
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.
No items found.

More in this series

No items found.

Blog

/

AI

/

December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

Default 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 18, 2025

Why organizations are moving to label-free, behavioral DLP for outbound email

Default 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
あなたのデータ × DarktraceのAI
唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ