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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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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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September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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Jason Lusted
AI Governance Advisor

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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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