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December 11, 2024

Company Shuts Down Cyber-attacks with “Flawless” Detection and Response from Darktrace

This blog explores how Darktrace shut down a major third-party cyber-attack, preventing the deployment of ransomware. Read more to discover how the security team now spends 80-90% of their time working on more strategic projects vs. manual, low-level tasks.
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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.
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Dec 2024

Growing pains: Balancing efficiency with risk  

This organization has recently scaled its operations, and numerous acquisitions have significantly boosted the organization’s capabilities and growth. However, this also creates work and high expectations for the organization’s IT and security teams. Within 12 months of an acquisition, the teams must fully integrate each new business onto the company’s platform. “A huge piece of that integration plan is rolling out our security controls,” said the CISO. “While our goal is to connect those facilities up as quickly as possible to drive efficiency, we also need to implement the proper security controls to protect the enterprise.”

Gap beyond the perimeter  

The organization had established strong security measures to safeguard its perimeter; however, the CISO identified a critical gap in real-time network monitoring. If the perimeter were breached, threats were only discovered after an endpoint was compromised and the issue was manually reported.

As digital transformation progresses, the need to adopt advanced technologies is becoming essential, particularly as organizations begin to open up operational environments to greater connectivity. Many processes still rely on traditional methods, and integrating innovative solutions could drive significant improvements in efficiency and productivity. “We’re committed to adopting cutting-edge technologies,” the CISO explained. “But we understood that without more robust network security controls, opening up our operational environments would expose us to heightened risks, including advanced threats like ransomware.”

Building a layered, proactive security strategy with Darktrace  

To close the gap beyond the perimeter, the company embarked on a free trial with Darktrace. The CISO recalls: “The trials were fantastic. It was obvious that Darktrace was exactly what we needed. The Darktrace team was also very knowledgeable and helpful throughout the process, which was impressive.”  

Today, the organization is using a combination of Darktrace solutions for its layered security approach, including:

Detecting unusual behavior with AI  

Darktrace’s use of machine learning and Self-Learning AI is one of the reasons the company chose Darktrace. Instead of teaching an AI system what an ‘attack’ looks like, training it on large data lakes of thousands of organizations’ data, Darktrace AI learns from the company’s own unique data and user activity to learn and create baseline models of what ‘normal’ looks like for their business.

Darktrace can then detect subtle deviations and unusual activity that signals a possible threat. “That fascinated us because what it really means is this technology doesn’t need to know about every single threat because the threat itself isn’t important, it’s the behavior of the activity that’s important. That capability is unique when it when it comes to threat detection,” said the CISO.

Identifying and mitigating high-impact attack paths

The security team appreciated that with Darktrace they could take a more proactive approach to security by exposing high-risk attack paths through modeling and AI risk assessments. Darktrace / Proactive Exposure Management gives them visibility into vulnerable entry points and assets, identifies active risks, and prioritizes the most important security issues to be addressed.

“Specific users and assets within our business have a higher risk of being targeted by a cyber-attack, for example our executives,” said the CISO. “With Darktrace, we get an adversarial view of our risk. We can see the attack path around those potential targets and proactively take measures to mitigate that vulnerability and prevent an attack.”

Driving up productivity while putting the brakes on cyber-attacks  

The security team collaborated with Darktrace to fine tune the models that really fit their business. With Darktrace now automating most of their threat detection and response efforts, productivity has soared, the security team is now focused on delivering greater value to the business and, most importantly, Darktrace proved it could quickly detect and shut down a major cyber-attack–and do so without impacting business operations.

Fueling team productivity with automation and AI

Prior to using Darktrace, the security team had little visibility into potential risks beyond the perimeter. Today, the team has full control and visibility over the network. “My team is now spending 80-90% of their time doing proactive work because Darktrace is managing the vast majority of our detect and response needs. The team really has faith in the Darktrace system,” said the CISO.  

With less time spent on low-level manual tasks, the security team can now focus on higher priority initiatives. For example, they have expanded their internal vulnerability assessments across the entire group. The team couldn’t focus on this additional audit and vulnerability management work if Darktrace wasn’t taking care of most of their security monitoring. “Darktrace has allowed us to move on to these additional kinds of governance projects that we otherwise would have to hire an army of staff to get through”.

Stopping email threats in their tracks

Using Darktrace / EMAIL, the company has identified and blocked a significant percentage of emails that were making it past their native email filters. “Darktrace is especially good at detecting impersonation emails, and we really appreciate its ability to automatically remove suspicious emails directly from a user’s inbox. It adds an extra level of confidence,” said the CISO.

Self-Learning AI understands anomalies within unique communication patterns to stop known and unknown threats. For example, when an employee sent an email to a brand new domain, Darktrace identified the behavior as unusual and inconsistent with baseline models and blocked the email.

Darktrace passes the biggest test of all

In 2024, the company experienced the value of the security system firsthand when attackers exploited a vulnerability in a third-party remote support solution that they was using. This solution provided remote access and tech support capabilities. If successful, the attackers could have infiltrated high-value end points and created their own administrative user, giving them full control over the server.

“We first became aware of the attack when Darktrace notified us of unusual behavior coming from the remote support server,” said the CISO. The attackers were attempting to put backdoors onto the service with the intent of selling access to the highest bidder who would then install ransomware on their servers. It all happened very quickly, as the attackers tried to connect to the internal network and other servers, while also firing off a host of other actions, like PowerShell commands, to escalate their privileges.  

“Darktrace worked flawlessly. There was no chance that ransomware was ever going to come in,” the CISO said. “Even though there was no signature to really look at, Darktrace realized this was not normal behavior for this server, shutting down connections and doing everything it could do to stop the attack.” Within eight hours, the security team identified and stopped the attack, severed its connection to the third-party solution, and completed additional analysis and clean-up. “In addition to our own investigation, third parties like our external SOC and legal department also confirmed that Darktrace performed as expected. We were able to report back to the executive team that there was zero risk that any data or systems were compromised.”

Post-attack, there was no need to make any changes to Darktrace. The team consistently reviews its models and baselines, often collaborating with Darktrace to make adjustments when needed to continuously improve performance. “Because of this relationship and constant engagement with Darktrace’s technical teams, we didn't have to go back and ask: ‘why wasn’t this updated’ or ‘why didn’t this model work.’ The models worked.”

His advice to other organizations facing similar challenges? First, focus on updating, patching, and vulnerability management, and act quickly when vulnerabilities are identified. His second piece of advice: “have an automated detection system like Darktrace in place so you can respond at the speed that these attacks evolve. Humans can no longer keep up with a scripted attack as it moves around and tries to compromise items on your network. You need the right technology to fight these types of attacks.”

Dynamic capabilities for a dynamic future

Real-time playbooks

With a proactive, enterprise-wide security strategy in place, the CISO now has the time to think about future projects and innovations. He’s particularly interested in the idea of generating playbooks on the fly in response to real-time events. He believes cyber-attacks are far too varied for a static playbook to be useful; when an attack strikes, teams need to quickly understand exactly what’s in front of them and how to shut it down. “This fits into our future cybersecurity strategy, and Darktrace is the only company I’ve seen talking about building playbooks dynamically. This kind of technology would really help bring our cybersecurity strategy full circle.”

“Darktrace ’s technology, experience and expertise is helping us staying ahead of cyber-attacks, minimizing our risk and driving greater productivity for our team,” said the CISO. In collaboration with Darktrace, the team have created a security foundation that is both powerful and agile. “While Darktrace is detecting and responding to attacks targeting our business today, we know that it’s always learning, adapting and scaling to ensure we’re protected tomorrow. That gives me peace of mind and the freedom to focus on our future.”

Download the Darktrace / NETWORK Solution Brief

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Protect in real time: Defend against known and emerging threats without relying on historical data or external intelligence.

Full visibility: Gain comprehensive insights across all network environments, including on-premises, cloud, and remote devices.

AI-powered efficiency: Streamline incident response with AI automation, saving time and resources while ensuring minimal disruption to operations.

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
The Darktrace Community

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September 22, 2026

Darktrace / SECURE AI: Extending Behavioral Security for the Age of Agentic AI

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AI is moving faster than the controls built to secure it

Over 80% of Darktrace’s customers now use generative AI services, and the average organization interacted with five different AI provides in August 20261. That level of adoption reflects how quickly AI has become embedded in day-to-day business operations. But as adoption accelerates, so do the opportunities for new forms of exposure that often operate outside traditional security controls. Prompts can expose sensitive information, an over-permissioned agent can turn a routine task into a serious security concern, and an employee using an unsanctioned AI tool to get work done can quietly introduce risk long before the security team is aware of its existence.

Securing AI isn’t a nice-to-have. It's an urgent, board-level requirement for any business that wants AI adoption to be an advantage rather than a liability.

Why traditional security controls fall short

The challenge isn’t a lack of AI security tools, it’s that most security solutions weren’t built for the way AI behaves.  

AI systems are adaptive and, by nature, unpredictable. A prompt can be entirely benign in one context and a serious risk in another. An agent's permissions can look reasonable in isolation and dangerous the moment they're combined with what that agent is actually doing. Rule-based controls, built to catch known signatures and static policy violations, simply aren't designed to catch this kind of subtlety. They might be able to tell you what happened, but they can’t tell you if it mattered.  

Effective AI security requires something different: a deep understanding of what normal looks like across every human, system, and agent in an environment, so the earliest signs of drift, misuse, or compromise stand out as they emerge.

Behavioral understanding is our foundation

Darktrace isn’t reinventing itself to secure AI. We’re extending what we already do. For over a decade, Darktrace has been built on a single premise – that every organization has its own unique, evolving way of operating, shaped by how its systems, users, and devices behave. Understanding that is the only reliable way to catch what rules and signatures miss. Our Adaptive AI™ continuously learns the distinct behaviors, relationships, and operational patterns of every enterprise it protects, building a Unique Behavioral Profile that no other vendor can replicate. It's how we've spent years interpreting ambiguity, uncovering subtle intent, and spotting drift before it becomes dangerous across networks, cloud, identities, email, OT, and endpoints. AI is a new domain, but behavioral security isn't a new discipline for us.

Introducing Darktrace / SECURE AI

Darktrace / SECURE AI helps organizations embrace AI innovation without losing control of how it is used. Delivered through the Darktrace Behavioral Defense Platform™, it provides visibility into AI activity across employees, agents, and AI systems, helping security teams understand how AI is being used throughout their business.

Darktrace then applies behavioral understanding to that activity, adding the context needed to distinguish routine usage from genuine risk. By understanding the relationships between users, agents, systems, and data, / SECURE AI helps organizations move beyond simply observing AI usage to understanding risk, governing behavior against policy, and enabling AI adoption with confidence. The result is greater oversight, stronger governance, and the ability to confidently accelerate AI innovation without creating unmanaged risk.

Securing your AI ecosystem

AI risk doesn't originate from a single source, and effective AI security can't focus on just one layer of the problem. Organizations need visibility, understanding, and governance across the entire AI ecosystem, from the prompts users submit and the agents they create, to the development environments where AI is built and the unsanctioned tools operating outside approved channels. Here's how Darktrace / SECURE AI helps organizations secure each of these areas.

Shadow AI management

Darktrace / SECURE AI helps security teams discover unsanctioned AI services and track usage trends over time using Darktrace telemetry and supported SASE integrations such as Microsoft Entra Global Secure Access. It also extends visibility to Model Context Protocol (MCP) usage, helping teams understand where AI tools and agents are connecting to external services. Through Darktrace platform integrations, organizations can block unsanctioned AI usage or quarantine affected devices when needed, helping them reduce exposure and guide users toward approved options.

AI prompt analysis

Prompts reveal what users are asking AI to do, what information they are sharing, and the outcomes they are trying to produce. Darktrace / SECURE AI provides visibility into prompts, sessions, and responses across supported platforms, including Microsoft Copilot, Copilot Studio, ChatGPT Enterprise, Claude, Salesforce, and AWS Bedrock2. Behavioral analysis detects activity such as attempted jailbreaks, sensitive data exposure, and potential indirect prompt injection, while risk scoring helps analysts prioritize the sessions that need attention. Policy Manager maps organizational AI policies against prompt activity and surfaces potential violations, helping teams govern AI use with direct evidence instead of relying on fragmented logs or inferred intent.

AI agent identities and actions

AI agents have their own permissions, roles, relationships, and access to systems and data. Darktrace / SECURE AI brings agent identities together with the users interacting with them, giving security teams visibility into access, sessions, and connections across supported environments. A real-time audit trail helps teams continuously evaluate whether an agent’s activity remains aligned with its intended purpose, so they can identify excessive access or behavioral drift before it becomes a security issue.

AI agent development risk management

Many AI risks are introduced during development, when agents are created, permissions are assigned, and connections to data sources are established. Darktrace / SECURE AI provides visibility across both low-code and high-code environments. In platforms such as AWS Bedrock, teams can examine AI architecture and the agent identities involved in development and deployment. In low-code environments, such as Copilot Studio, they can observe agents as they are created, monitor whether behavior stays aligned with their intended purpose, and connect prompt activity during development with behavior in production. This helps organizations address misconfigurations and excessive permissions before they reach production.

The promise of AI, secured

The question isn't whether to adopt AI, it's whether security teams can enable secure adoption that allows every business to benefit from AI’s potential.

Darktrace / SECURE AI is built to solve this problem – extending a decade of behavioral understanding to the newest, fastest-moving part of the enterprise, using the same principles which already protect people and hybrid infrastructure.

Darktrace / SECURE AI is generally available now. Discover the product, or get a demo today.

[related-resource]

[1] Based on aggregated Darktrace product telemetry across a fleet of ~8,200 observed deployments, measuring generative AI service usage across accounts monitored in August 2026. The 80%+ figure reflects the share of monitored accounts with any generative AI service usage during the same period.

[2] Microsoft, Copilot, ChatGPT, Claude, Salesforce, and Amazon Bedrock are trademarks of their respective owners.

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Brittany Woodsmall
Senior Product Marketing Manager, AI

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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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