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May 3, 2021

Understanding Modern-Day Cyber Attacks

Discover how Darktrace detects and mitigates threats in IoT ecosystems and globalized supply chains that are constantly evolving.
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager
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03
May 2021

It’s ten to five on a Friday afternoon. A technician has come in to perform a routine check on an electronic door. She enters the office with no issues – she works for a trusted third-party vendor, employees see her every week. She opens her laptop and connects to the Door Access Control Unit, a small Internet of Things (IoT) device used to operate the smart lock. Minutes later, trojans have been downloaded onto the company network, a crypto-mining operation has begun, and there is evidence of confidential data being exfiltrated. Where did things go wrong?

Threats in a business: A new dawn surfaces

As organizations keep pace with the demands of digital transformation, the attack surface has become broader than ever before. There are numerous points of entry for a cyber-criminal – from vulnerabilities in IoT ecosystems, to blind spots in supply chains, to insiders misusing their access to the business. Darktrace sees these threats every day. Sometimes, like in the real-world example above, which will be examined in this blog, they can occur in the very same attack.

Insider threats can use their familiarity and level of access to a system as a critical advantage when evading detection and launching an attack. But insiders don’t necessarily have to be malicious. Every employee or contractor is a potential threat: clicking on a phishing link or accidentally releasing data often leads to wide-scale breaches.

At the same time, connectivity in the workspace – with each IoT device communicating with the corporate network and the Internet on its own IP address – is an urgent security issue. Access control systems, for example, add a layer of physical security by tracking who enters the office and when. However, these same control systems imperil digital security by introducing a cluster of sensors, locks, alarm systems, and keypads, which hold sensitive user information and connect to company infrastructure.

Furthermore, a significant proportion of IoT devices are built without security in mind. Vendors prioritize time-to-market and often don’t have the resources to invest in baked-in security measures. Consider the number of start-ups which manufacture IoT – over 60% of home automation companies have fewer than ten employees.

Insider threat detected by Cyber AI

In January 2021, a medium-sized North American company suffered a supply chain attack when a third-party vendor connected to the control unit for a smart door.

Figure 1: The attack lasted 3.5 hours in total, commencing 16:50 local time.

The technician from the vendor’s company had come in to perform scheduled maintenance. They had been authorized to connect directly to the Door Access Control Unit, yet were unaware that the laptop they were using, brought in from outside of the organization, had been infected with malware.

As soon as the laptop connected with the control unit, the malware detected an open port, identified the vulnerability, and began moving laterally. Within minutes, the IoT device was seen making highly unusual connections to rare external IP addresses. The connections were made using HTTP and contained suspicious user agents and URIs.

Darktrace then detected that the control unit was attempting to download trojans and other payloads, including upsupx2.exe and 36BB9658.moe. Other connections were used to send base64 encoded strings containing the device name and the organization’s external IP address.

Cryptocurrency mining activity with a Monero (XMR) CPU miner was detected shortly afterwards. The device also utilized an SMB exploit to make external connections on port 445 while searching for vulnerable internal devices using the outdated SMBv1 protocol.

One hour later, the device connected to an endpoint related to the third-party remote access tool TeamViewer. After a few minutes, the device was seen uploading over 15 MB to a 100% rare external IP.

Figure 2: Timeline of the connections made by an example device on the days around an incident (blue). The connections associated with the compromise are a significant deviation from the device’s normal pattern of life, and result in multiple unusual activity events and repeated model breaches (orange).

Security threats in the supply chain

Cyber AI flagged the insider threat to the customer as soon as the control unit had been compromised. The attack had managed to bypass the rest of the organization’s security stack, for the simple reason that it was introduced directly from a trusted external laptop, and the IoT device itself was managed by the third-party vendor, so the customer had little visibility over it.

Traditional security tools are ineffective against supply chain attacks such as this. From the SolarWinds hack to Vendor Email Compromise, 2021 has put the nail in the coffin for signature-based security – proving that we cannot rely on yesterday’s attacks to predict tomorrow’s threats.

International supply chains and the sheer number of different partners and suppliers which modern organizations work with thus pose a serious security dilemma: how can we allow external vendors onto our network and keep an airtight system?

The first answer is zero-trust access. This involves treating every device as malicious, inside and outside the corporate network, and demanding verification at all stages. The second answer is visibility and response. Security products must shed a clear light into cloud and IoT infrastructure, and react autonomously as soon as subtle anomalies emerge across the enterprise.

IoT investigated

Darktrace’s Cyber AI Analyst reported on every stage of the attack, including the download of the first malicious executable file.

Figure 3: Example of Cyber AI Analyst detecting anomalous behavior on a device, including C2 connectivity and suspicious file downloads.

Cyber AI Analyst investigated the C2 connectivity, providing a high-level summary of the activity. The IoT device had accessed suspicious MOE files with randomly generated alphanumeric names.

Figure 4: A Cyber AI Analyst summary of C2 connectivity for a device.

Not only did the AI detect every stage of the activity, but the customer was also alerted via a Proactive Threat Notification following a high scoring model breach at 16:59, just minutes after the attack had commenced.

Stranger danger

Third parties coming in to tweak device settings and adjust the network can have unintended consequences. The hyper-connected world which we’re living in, with the advent of 5G and Industry 4.0, has become a digital playground for cyber-criminals.

In the real-world case study above, the IoT device was unsecured and misconfigured. With rushed creations of IoT ecosystems, intertwining supply chains, and a breadth of individuals and devices connecting to corporate infrastructure, modern-day organizations cannot expect simple security tools which rely on pre-defined rules to stop insider threats and other advanced cyber-attacks.

The organization did not have visibility over the management of the Door Access Control Unit. Despite this, and despite no prior knowledge of the attack type or the vulnerabilities present in the IoT device, Darktrace detected the behavioral anomalies immediately. Without Cyber AI, the infection could have remained on the customer’s environment for weeks or months, escalating privileges, silently crypto-mining, and exfiltrating sensitive company data.

Thanks to Darktrace analyst Grace Carballo for her insights on the above threat find.

Learn more about insider threats

Darktrace model detections:

  • Anomalous File/Anomalous Octet Stream
  • Anomalous Connection/New User Agent to IP Without Hostname
  • Unusual Activity/Unusual External Connectivity
  • Device/Increased External Connectivity
  • Anomalous Server Activity/Outgoing from Server
  • Device/New User Agent and New IP
  • Compliance/Cryptocurrency Mining Activity
  • Compliance/External Windows Connectivity
  • Anomalous File/Multiple EXE from Rare External Locations
  • Anomalous File/EXE from Rare External Location
  • Device/Large Number of Model Breaches
  • Anomalous File/Internet Facing System File Download
  • Device/Initial Breach Chain Compromise
  • Device/SMB Session Bruteforce
  • Device/Network Scan- Low Anomaly Score
  • Device/Large Number of Connections to New Endpoint
  • Anomalous Server Activity/Outgoing from Server
  • Compromise/Beacon to Young Endpoint
  • Anomalous Server Activity/Rare External from Server
  • Device/Multiple C2 Model Breaches
  • Compliance/Remote Management Tool on Server
  • Anomalous Connection/Data Sent to New External Device

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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager

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May 21, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

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Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

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Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

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About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response

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May 21, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

Sign up today to stay informed about innovations across securing AI.

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About the author
Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
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