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September 19, 2021

Defending Tokyo Olympics: AI Neutralizes IoT Attack

Learn how Darktrace autonomously thwarted a cyber-attack on a national sporting body before the Tokyo Olympics in this detailed breakdown.
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
Oakley Cox
Director of Product
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19
Sep 2021

One of the greatest issues in security is how to deal with high-stress scenarios when there is a significant breach, and there is too much to do in too little time. The nightmare scenario for any CISO is when this happens during a critical moment for the organization: an important acquisition, a crucial news announcement, or in this case, a global sporting event attracting an audience of millions.

Threat actors often exploit the pressure of these events to cause disruption or extract hefty sums. Sporting occasions, especially Formula 1 races, the Super Bowl, and the Olympics, attract a great deal of criminal interest.

The games begin

There have been several recorded attacks and data breaches at the Olympics this year, including an incident when a volleyball commentator asked his colleague for his computer password – not realizing he was still on air.

In a more nefarious case discovered by Darktrace, a Raspberry Pi device was covertly implanted into a national sporting body directly involved in the Olympics, in an attempt to exfiltrate sensitive data. The events took place one week before the start of the Games, and a data breach at this time would have had significant ramifications for the reputation of the organization, the confidentiality of their plans, and potentially the safety of their athletes.

Darktrace AI recognized this activity as malicious given its evolving understanding of ‘self’ for the organization, and Antigena – Darktrace’s autonomous response capability – took action at machine speed to interrupt the threat, affording the human security team the critical time they needed to catch up and neutralize the attack.

In what follows, we break down the attack.

Figure 1: The overall dwell time was three days.

Breaking down the attack

July 15, 14:09 — Initial intrusion

An unauthorized Raspberry Pi device connected to the organization’s digital environment – disguised and named in a way which mimicked the corporate naming convention. As a small IoT device, Raspberry Pis can be easily hidden and are difficult to locate physically in large environments. They have been used in various high-profile hacks in the past including the 2018 NASA breach.

IoT devices – from printers to fish tanks – pose a serious risk to security, as they can be exploited to gather information, move laterally, and escalate privileges.

July 15, 15:25 — External VPN activity

Anomalous UDP connections were made to an external endpoint over port 1194 (Open VPN activity). URIs showed that the device downloaded data potentially associated with Open VPN configuration files. This could represent an attempt to establish a secure channel for malicious activity such as data exfiltration.

By establishing an outgoing VPN, the attacker obfuscated their activity and bypassed the organization’s signature-based security, which could not detect the encrypted traffic. Antigena immediately blocked the suspicious connectivity, regardless of the encryption, identifying that the activity was a deviation from the ‘pattern of life’ for new devices.

July 15, 16:04 — Possible C2 activity

The Raspberry Pi soon began making repeated HTTP connections to a new external endpoint and downloaded octet streams — arbitrary binary data. It seems the activity was initiated by a standalone software process as opposed to a web browser.

Darktrace revealed that the device was performing an unusual external data transfer to the same endpoint, uploading 7.5 MB which likely contained call home data about the new location and name of the device.

July 15, 16:41 — Internal reconnaissance

The device engaged in TCP scanning across three unique internal IP addresses over a wide range of ports. Although the network scan only targeted three internal servers, the activity was identified by Darktrace as a suspicious increase in internal connections and failed internal connections.

Antigena instantly stopped the Raspberry Pi from making internal connections over the ports involved in the scanning activity, as well as enforcing the device’s ‘pattern of life’.

Figure 2: Device event log showing the components which enable Darktrace to detect network scanning.

July 15, 18:14 — Multiple internal reconnaissance tactics

The Raspberry Pi then scanned a large number of devices on SMB port 445 and engaged in suspicious use of the outdated SMB version 1 protocol, suggesting more in-depth reconnaissance to find exploitable vulnerabilities.

Reacting to the scanning activity alongside the insecure protocol SMBv1, Antigena blocked connections from the source device to the destination IPs for one hour.

Four minutes later, the device engaged in connections to the open-source vulnerability scanner, Nmap. Nmap can be used legitimately for vulnerability scanning and so often is not alerted to by traditional security tools. However, Darktrace’s AI detected that the use of the tool was highly anomalous, and so blocked all outgoing traffic for ten minutes.

July 15, 22:03 — Final reconnaissance

Three hours later, the Raspberry Pi initiated another network scan across six unique external IPs – this was in preparation for the final data exfiltration. Antigena responded with instant, specific blocks to the external IPs which the device was attempting to connect to – before any data could be exfiltrated.

After 30 minutes, Darktrace detected bruteforcing activity from the Raspberry Pi using the SMB and NTLM authentication protocols. The device made a large number of failed login attempts to a single internal device using over 100 unique user accounts. Antigena blocked the activity, successfully stopping another wave of attempted SMB lateral movement.

By this stage, Antigena had bought the security team enough time to respond. The team applied an Antigena quarantine rule (the most severe action Antigena can take) to the Raspberry Pi, until they were able to find the physical location of the device and unplug it from the network.

How AI Analyst stitched together the incident

Cyber AI Analyst autonomously reported on three key moments of the attack:

  • Unusual External Data Transfer
  • Possible HTTP Command and Control
  • TCP Scanning of Multiple Devices (the attempted data exfiltration)

It tied together activities over the span of multiple days, which could have been easily missed by human analysis. The AI provided crucial pieces of information, including the extent of the scanning activity. Such insights are time-consuming to calculate manually.

Figure 3: A screenshot from Cyber AI Analyst summarizing potential C2 activity.

Autonomous Response

Antigena took targeted action throughout to neutralize the suspicious behavior, while allowing normal business operations to continue unhindered.

Rather than widespread blocking, Antigena implemented a range of nuanced responses depending on the situation, always taking the smallest action necessary to deal with the threat.

Figure 4: Darktrace’s UI reveals the attempted network reconnaissance, and Antigena actions a targeted response. All IP addresses have been randomized.

Raspberry Pi: IoT threats

In an event involving 206 countries and 11,000 athletes, facing attacks from hacktivists, criminal groups, and nation states, with many broadcasters working remotely and millions watching from home, organizations involved in the Olympics needed a security solution which could rise to the occasion.

Even with the largest affairs, threats can come from the smallest places. The ability to detect unauthorized IoT devices and maintain visibility over all activity in your digital estate is essential.

Autonomous Response protects against the unexpected, stopping malicious activity at machine speed without any user input. This is necessary for rapid response and remediation, especially for resource-stretched internal security teams. When it comes to defending systems and outpacing attackers, AI always wins the race.

Thanks to Darktrace analysts Emma Foulger and Greg Chapman for their insights on the above threat find.

Learn how two rogue Raspberry Pi devices infected a healthcare provider

Darktrace model detections:

  • Compromise / Ransomware / Suspicious SMB Activity
  • Tags / New Raspberry Pi Device
  • Device / Network Scan
  • Unusual Activity / Unusual Raspberry Pi Activity
  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Suspicious Network Scan Activity
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Device / Suspicious SMB Scanning Activity
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Device / Attack and Recon Tools
  • Device / New Device with Attack Tools
  • Device / Anomalous Nmap Activity
  • Device / External Network Scan
  • Device / SMB Session Bruteforce
  • Antigena / Network / Manual / Block All Outgoing Connections
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
Oakley Cox
Director of Product

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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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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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