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October 3, 2024

Introducing Real-Time Multi-Cloud Detection & Response Powered by AI

This blog announces the general availability of Microsoft Azure support for Darktrace / CLOUD, enabling real-time cloud detection and response across dynamic multi-cloud environments. Read more to discover how Darktrace is pioneering AI-led real-time cloud detection and response.
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
Adam Stevens
Senior Director of Product, Cloud | Darktrace
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03
Oct 2024

We are delighted to announce the general availability of Microsoft Azure support for Darktrace / CLOUD, enabling real-time cloud detection and response across dynamic multi-cloud environments. Built on Self-Learning AI, Darktrace / CLOUD leverages Microsoft’s new virtual network flow logs (VNet flow) to offer an agentless-first approach that dramatically simplifies detection and response within Azure, unifying cloud-native security with Darktrace’s innovative ActiveAI Security Platform.

As organizations increasingly adopt multi-cloud architectures, the need for advanced, real-time threat detection and response is critical to keep pace with evolving cloud threats. Security teams face significant challenges, including increased complexity, limited visibility, and siloed tools. The dynamic nature of multi-cloud environments introduces ever-changing blind spots, while traditional security tools struggle to provide real-time insights, often offering static snapshots of risk. Additionally, cloud security teams frequently operate in isolation from SOC teams, leading to fragmented visibility and delayed responses. This lack of coordination, especially in hybrid environments, hinders effective threat detection and response. Compounding these challenges, current security solutions are split between agent-based and agentless approaches, with agentless solutions often lacking real-time awareness and agent-based options adding complexity and scalability concerns. Darktrace / CLOUD helps to solve these challenges with real-time detection and response designed specifically for dynamic cloud environments like Azure and AWS.

Pioneering AI-led real-time cloud detection & response

Darktrace has been at the forefront of real-time detection and response for over a decade, continually pushing the boundaries of AI-driven cybersecurity. Our Self-Learning AI uniquely positions Darktrace with the ability to automatically understand and instantly adapt to changing cloud environments. This is critical in today’s landscape, where cloud infrastructures are highly dynamic and ever-changing.  

Built on years of market-leading network visibility, Darktrace / CLOUD understands ‘normal’ for your unique business across clouds and networks to instantly reveal known, unknown, and novel cloud threats with confidence. Darktrace Self-Learning AI continuously monitors activity across cloud assets, containers, and users, and correlates it with detailed identity and network context to rapidly detect malicious activity. Platform-native identity and network monitoring capabilities allow Darktrace / CLOUD to deeply understand normal patterns of life for every user and device, enabling instant, precise and proportionate response to abnormal behavior - without business disruption.

Leveraging platform-native Autonomous Response, AI-driven behavioral containment neutralizes malicious activity with surgical accuracy while preventing disruption to cloud infrastructure or services. As malicious behavior escalates, Darktrace correlates thousands of data points to identify and instantly respond to unusual activity by blocking specific connections and enforcing normal behavior.

Figure 1: AI-driven behavioral containment neutralizes malicious activity with surgical accuracy while preventing disruption to cloud infrastructure or services.

Unparalleled agentless visibility into Azure

As a long-term trusted partner of Microsoft, Darktrace leverages Azure VNet flow logs to provide agentless, high-fidelity visibility into cloud environments, ensuring comprehensive monitoring without disrupting workflows. By integrating seamlessly with Azure, Darktrace / CLOUD continues to push the envelope of innovation in cloud security. Our Self-learning AI not only improves the detection of traditional and novel threats, but also enhances real-time response capabilities and demonstrates our commitment to delivering cutting-edge, AI-powered multi-cloud security solutions.

  • Integration with Microsoft Virtual network flow logs for enhanced visibility
    Darktrace / CLOUD integrates seamlessly with Azure to provide agentless, high-fidelity visibility into cloud environments. VNet flow logs capture critical network traffic data, allowing Darktrace to monitor Azure workloads in real time without disrupting existing workflows. This integration significantly reduces deployment time by 95%1 and cloud security operational costs by up to 80%2 compared to traditional agent-based solutions. Organizations benefit from enhanced visibility across dynamic cloud infrastructures, scaling security measures effortlessly while minimizing blind spots, particularly in ephemeral resources or serverless functions.
  • High-fidelity agentless deployment
    Agentless deployment allows security teams to monitor and secure cloud environments without installing software agents on individual workloads. By using cloud-native APIs like AWS VPC flow logs or Azure VNet flow logs, security teams can quickly deploy and scale security measures across dynamic, multi-cloud environments without the complexity and performance overhead of agents. This approach delivers real-time insights, improving incident detection and response while reducing disruptions. For organizations, agentless visibility simplifies cloud security management, lowers operational costs, and minimizes blind spots, especially in ephemeral resources or serverless functions.
  • Real-time visibility into cloud assets and architectures
    With real-time Cloud Asset Enumeration and Dynamic Architecture Modeling, Darktrace / CLOUD generates up-to-date architecture diagrams, giving SecOps and DevOps teams a unified view of cloud infrastructures. This shared context enhances collaboration and accelerates threat detection and response, especially in complex environments like Kubernetes. Additionally, Cyber AI Analyst automates the investigation process, correlating data across networks, identities, and cloud assets to save security teams valuable time, ensuring continuous protection and efficient cloud migrations.
Figure 2: Real-time visibility into Azure assets and architectures built from network, configuration and identity and access roles.

Unified multi-cloud security at scale

As organizations increasingly adopt multi-cloud strategies, the complexity of managing security across different cloud providers introduces gaps in visibility. Darktrace / CLOUD simplifies this by offering agentless, real-time monitoring across multi-cloud environments. Building on our innovative approach to securing AWS environments, our customers can now take full advantage of robust real-time detection and response capabilities for Azure. Darktrace is one of the first vendors to leverage Microsoft’s virtual network flow logs to provide agentless deployment in Azure, enabling unparalleled visibility without the need for installing agents. In addition, Darktrace / CLOUD offers automated Cloud Security Posture Management (CSPM) that continuously assesses cloud configurations against industry standards.  Security teams can identify and prioritize misconfigurations, vulnerabilities, and policy violations in real-time. These capabilities give security teams a complete, live understanding of their cloud environments and help them focus their limited time and resources where they are needed most.

This approach offers seamless integration into existing workflows, reducing configuration efforts and enabling fast, flexible deployment across cloud environments. By extending its capabilities across multiple clouds, Darktrace / CLOUD ensures that no blind spots are left uncovered, providing holistic, multi-cloud security that scales effortlessly with your cloud infrastructure. diagrams, visualizes cloud assets, and prioritizes risks across cloud environments.

Figure 3: Unified view of AWS and Azure cloud posture and compliance over time.

The future of cloud security: Real-time defense in an unpredictable world

Darktrace / CLOUD’s support for Microsoft Azure, powered by Self-Learning AI and agentless deployment, sets a new standard in multi-cloud security. With real-time detection and autonomous response, organizations can confidently secure their Azure environments, leveraging innovation to stay ahead of the constantly evolving threat landscape. By combining Azure VNet flow logs with Darktrace’s AI-driven platform, we can provide customers with a unified, intelligent solution that transforms how security is managed across the cloud.

Unlock advanced cloud protection

Darktrace / CLOUD solution brief screenshot

Download the Darktrace / CLOUD solution brief to discover how autonomous, AI-driven defense can secure your environment in real-time.

  • Achieve 60% more accurate detection of unknown and novel cloud threats.
  • Respond instantly with autonomous threat response, cutting response time by 90%.
  • Streamline investigations with automated analysis, improving ROI by 85%.
  • Gain a 30% boost in cloud asset visibility with real-time architecture modeling.
  • Learn More:

    References

    1. Based on internal research and customer data

    2. Based on internal research

    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
    Adam Stevens
    Senior Director of Product, Cloud | Darktrace

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    June 15, 2026

    Hola VPN Abuse: From Proxy Traffic to Malware and Cryptomining

    hola vpn malware cryptominingDefault blog imageDefault blog image

    Introduction

    In enterprise environments, non-compliant software traffic can introduce unexpected exposure by creating unmanaged paths for outbound connectivity. Hola VPN is a notable example because of its peer-to-peer design, which can effectively turn user devices into routing or exit nodes for other parties’ traffic, shifting the risk profile from that of a traditional virtual private network (VPN) to something closer to a distributed proxy.

    As a result, the appearance of Hola-related activity, whether from prior installation or unintended background connections, should be treated with caution.  Such activity may provide a foothold for malicious behavior, including lateral movement or command-and-control communication.

    This blog explores how Hola-associated activity appeared as part of broader patterns of suspicious behavior observed across the Darktrace customer base.

    The campaign

    In February and March 2026, Darktrace observed similar anomalous activity across multiple customer environments, with affected devices showing consistent behavioral patterns. These included connections to multiple *.hola[.]org endpoints using Hola-related user agents, suggesting interaction with Hola infrastructure rather than isolated or incidental traffic.

    Following these connections, affected customer environments showed downloads of suspicious executable files from rare external endpoints 188.241.219[.]55 and 184.241.218[.]111. Both endpoints have been flagged as potentially malicious by open-source intelligence (OSINT) [1][2].

    These downloads were conducted using consistent user agents across impacted customers, specifically ‘Hola svc_js_win32/1.249.408’ and ‘Hola svc_js_win32/1.251.389’, suggesting a possible association with Hola-related activity.

    Notably, this pattern aligns with recent reporting that, in some cases, Hola distributed an undeclared executable component, me[.]exe, which was later assessed to be a likely Monero-mining binary introduced via a compromised delivery pipeline [3].

    Case Study 1

    Darktrace first observed a new device on January 19, 2026, within a customer environment based in the Europe, Middle East, and Africa (EMEA) region. On the same day it appeared on the network, the device communicated with multiple pieces of Hola VPN-linked infrastructure before downloading a binary from a hola[.]org subdomain.

    Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.
    Figure 1: Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.

    Subsequent Darktrace telemetry revealed a recurring pattern of activity from the day the device was first observed through to March 4, 2026. During this period, the device repeatedly issued HTTP GET requests to the URI /bwfile?size=1048576, each returning a 200 OK response, indicating successful file retrieval.

    This behavior was accompanied by a POST request to /bwfile, followed by an additional GET request for a significantly larger file at /bwfile?size=26214400, suggesting a deliberate and structured file transfer pattern.

    Notably, the binary download activity was not tied to a single static host. Instead, it was observed across multiple URLs that changed over time while remaining within the same hola[.]org domain. This pattern suggests the use of rotating or distributed delivery infrastructure rather than a fixed endpoint.

    Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.
    Figure 2: Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.

    Across these events, the activity was consistently associated with the user agent Hola svc_js_win32/1.249.408, further linking the traffic to Hola-related service components. Amid these persistent and unusual connections, on February 22, Darktrace observed the device connecting to 188.241.219[.]55/proxy-peer-windows-amd64[.]exe, resulting in the download of an executable file.

     File transfer event showing the download of an executable  from the rare external endpoint 188.241.219[.]55.
    Figure 3: File transfer event showing the download of an executable  from the rare external endpoint 188.241.219[.]55.

    Based on its file hash, the downloaded file was assessed as a likely Trojan downloader [4], with import hash (imphash) values showing similarities to samples linked to Vidar, Rhadamanthys, and Stealc according to OSINT [5]. Overall, this sequence of activity suggests that Hola-related connectivity may have been leveraged as part of a broader malware delivery chain.

    Darktrace’s Autonomous Response

    Due to the highly unusual activity observed, Darktrace Autonomous Response was triggered by the device’s behavior. However, as the customer deployment was configured in “Human Confirmation” mode, manual approval was required before any action could be taken.

    Had the deployment been set to “Fully Autonomous” mode, Darktrace would have automatically:

    1. Blocked connections to the associated ports and external endpoints
    2. Prevented all outgoing network connections from the device
    3. Enforced the device’s established ‘pattern of life’, allowing normal activity to continue while restricting any anomalous behavior
    Figure 4: Example of a Darktrace Autonomous Response model highlighting the action that would have been taken, demonstrating how the system identifies anomalous behavior and applies targeted containment measures to restrict suspicious network activity.

    Case Study 2

    While the first case focused on anomalous activity from a newly observed device, Darktrace also identified cases in which devices had already been communicating with Hola-related endpoints prior to the suspected campaign. This may suggest pre-existing Hola usage within the environment, potentially increasing exposure and creating an avenue for subsequent suspicious activity.

    One case involved three devices within a customer network based in the Americas (AMS). In this instance, a different payload was identified: me[.]exe, a potentially malicious cryptocurrency miner also referred to as HolaMonitorService[.]exe [6][7]. The downloads were observed from infrastructure similar to that seen in Case 1, including an IP address within the same 188.241.0.0/16 subnet.

    Connections to *.hola[.]org, alongside the use of potential Hola-related user agents consistent with those in Case 1, were also identified, further suggesting a link between the observed activity and Hola-associated infrastructure.

    Darktrace observed activity indicative of unusual VPN usage on the first affected device on February 2, followed by telemetry suggesting potential Tor usage. This was later followed by the download of me[.]exe on March 10 from 188.241.218[.]111. Notably, this device was the earliest among the three within the deployment to exhibit the presence of the suspicious executable.

    Figure 5: Cyber AI Analyst detection highlighting the download of a suspicious executable from a similar external endpoint in a separate deployment.

    On March 5, 2026, the second affected device exhibited a slightly different progression, initiating connections to http-test1[.]hola[.]org using the user agent ‘hola_get’. This activity was followed by the download of me[.]exe from the same endpoint on March 13, consistent with the broader pattern of Hola-related downloads observed across the environment.

     Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.
    Figure 6: Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.

    The final affected device within this customer’s network demonstrated a more limited but related pattern, also downloading me[.]exe on March 17 using the same ‘hola_get’ user agent.

    While the earlier Hola VPN usage observed across the deployment may not have been directly related to the suspected malware campaign, it may nonetheless have contributed to reduced visibility. The presence of pre-existing Hola-related traffic could have obscured malicious activity, making it more difficult to distinguish legitimate usage from attacker-driven behavior and, in turn, hindering the timely identification of the emerging compromise.

    Darktrace’s Autonomous Response

    For this deployment, the customer had their Autonomous Response capability configured in “Fully Autonomous” mode, allowing Darktrace to take action without human intervention. As a result, the system was able to autonomously disrupt the activity as soon as relevant events were identified through model detections.

    Figure 7: Darktrace Autonomous Response actions taken against suspicious activity linked to Hola VPN.

    Suspected cryptomining activity

    As previously noted, some of the observed executable payloads appear to be linked to cryptomining malware. Across a subset of affected customer environments, this assessment was further supported by subsequent device activity consistent with Monero mining. Affected devices established follow-on connections to multiple external endpoints aligned with known mining infrastructure, indicating post-download execution.

    Considering the broader sequence of activity, this pattern may point to a wider form of abuse in which legitimate VPN-related traffic is used to mask or facilitate malicious behavior following compromise.

    On several devices, the download of executable files, including a newly observed peer[.]exe, was followed by alerts indicative of cryptocurrency mining activity. Mining-related credentials such as ‘x’ were observed using the Minergate protocol to communicate with endpoints within the 89.125.255.0/24 subnet and 188.241.218[.]111, the same endpoint involved in earlier download activity. Additional credentials appeared to reflect device-specific CPU identifiers, for example ‘12th Gen Intel(R) Core (TM) i5-1235U’.

    Observed mining methods included login, submit, and job, consistent with active participation in a pool-based mining workflow rather than passive or incidental contact. The login method indicates that the host authenticated to the mining service as a worker, job reflects the assignment of computational tasks, and submit shows completed work being returned to the pool [8]. This sequence suggests that affected devices were actively contributing processing resources as part of an unauthorized distributed mining operation.

    The presence of unauthorized cryptominers can lead to degraded system performance and reduced device stability. Beyond the immediate resource impact, such activity often serves as an indicator of a broader compromise rather than an isolated issue. This may increase the risk of further malware deployment, persistence mechanisms, and lateral movement, particularly in environments where the initial intrusion has not been fully contained.

    Conclusion

    Across affected environments, detections such as unusual VPN usage, connections to Hola infrastructure, anomalous HTTP activity, suspicious file downloads, and subsequent cryptomining behavior were linked into a single, evolving incident narrative. This aggregation provided a clearer view of attack progression, enabling security teams to understand not just isolated alerts, but the full sequence of compromise from initial contact through to post-exploitation.

    Ultimately, these activities show that the risk posed by non-compliant software such as Hola VPN can extend far beyond simple policy violations. What began as traffic to Hola-related infrastructure was, in multiple cases, followed by behavior suggesting deliberate misuse, including suspicious executable downloads using Hola-related user agents and, in some instances, evidence of active cryptomining. These were not isolated anomalies, but elements of a broader pattern in which seemingly benign proxy or VPN-related communications may have created a pathway for malicious delivery and unauthorized resource exploitation.

    The significance of this activity lies not only in the downloads or mining, but in what it reveals about an attacker’s ability to blend malicious operations into traffic associated with software that may already have a foothold in the environment. When unapproved software operates within an enterprise, it can reduce visibility, blur the distinction between legitimate and malicious traffic, and create opportunities to extend compromise in ways that are persistent and difficult to detect. Darktrace’s anomaly-based approach enables these behavioral distinctions to be identified, regardless of whether the device is new or long established within the network.

    Credit to Min Kim (Associate Principal Analyst), Priya Thapa (Senior Cyber Analyst)
    Edited by Ryan Traill (Content Manager)

    Appendices

    References

    [1] https://www.virustotal.com/gui/ip-address/188.241.219.55

    [2]  https://www.virustotal.com/gui/ip-address/188.241.218.111

    [3] https://www.sophos.com/en-us/blog/you-do-surprise-me-exe-an-unexpected-executable-in-hola-browser

    [4] https://www.virustotal.com/gui/file/d275abca286cd75af971d0459fdf1df37c7b19c514abafae5d0b04bf42ccfb45/detection

    [5] https://bazaar.abuse.ch/sample/d275abca286cd75af971d0459fdf1df37c7b19c514abafae5d0b04bf42ccfb45/

    [6] https://any.run/report/4cdeb5df217764a8b6a20d518b76ccb30cbe623365a13d9dcd40900950f1ed99/de3a756a-3101-4369-8922-52c586c939fb

    [7] https://www.virustotal.com/gui/file/e3541caf708c075f0bb22fc68b03acd8457fea7cf0732ea935b1eb016d1c7721/community

    [8] https://bitcoinwiki.org/wiki/stratum

    Darktrace Model Detections

    ·      Anomalous File / EXE from Rare External Location

    ·      Anomalous File / Multiple EXE from Rare External Locations

    ·      Compromise / Crypto Currency Mining Activity

    ·      Compromise / High Priority Crypto Currency Mining (EM)

    ·      Device / New User Agent

    ·      Anomalous Connection / New User Agent to IP Without Hostname

    ·      Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

    ·      Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

    ·      Antigena / Network / External Threat / Antigena Tor Block

    ·      Antigena / Network / External Threat / Antigena File then New Outbound Block

    ·      Antigena / Network / External Threat / Antigena Suspicious Activity Block

    ·      Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

    ·      Antigena / Network / External threat / Antigena Suspicious File Block

    Indicators of Compromise (IoCs)

    IoC –Type -Description + Confidence

    188.241.219[.]55 - IP Address - Malware distribution source

    188.241.218[.]111 - IP Address -Malware distribution source

    hxxp://188.241.218[.]111:8080/me[.]exe - URI - Malicious payload

    hxxp://188.241.219[.]55:9000/proxy-peer-windows-amd64[.]exe - URI - Malicious payload

    hxxp://188.241.219[.]55:9000/peer[.]exe - URI - Malicious payload

    C8088f3c8bc3542eb1ad78a7cc5306d866c8ac81 - SHA1 - Malicious payload, me[.]exe

    b595a6de0f6a18975b29e6f8ebe604956a173478 - SHA1 - Malicious payload, me[.]exe

    e9139a2e0839e8b9e5c9787ea936347ae56e5460 - SHA1 - Possible malicious payload

    c2e80073e4cafe757d5643bd8fd45f28ad89bff9 - SHA1 - Possible malicious payload

    695355eceedcdd337d8fcbd35e6a531cda75b847 - SHA1 - Possible malicious payload

    f0b0d8068a1b9ab5d68a8a46842d72b870b292e7 - SHA1 - Possible malicious payload

    a21c8b8cabc7670ea45bc175e185a0f9bfcf4733 - SHA1 - Malicious payload, me[.]exe

    0353ca44b9f397d8f492db0b2f7a1d00a9e4406a - SHA1 - Possible malicious payload

    56824c8a110e35ab303dc27a6c758cd50c36174c - SHA1 - Malicious payload, peer[.]exe

    c141fa0fa505fe7f9ad5dd21d9d4d6d411739682 - SHA1 - Malicious payload, peer[.]exe

    0417ec988b16f1267065185a6eea98f0bd2e17cd - SHA1 - Possible malicious payload

    c54f7eaaeb3e0b528cd2584bdcb3a4b13cc0f8a2 - SHA1 - Malicious payload, peer[.]exe

    11c78f15fafd53f8cc5a52b828d7cbf2a99e0b09 - SHA1 - Malicious payload, peer[.]exe

    0258bf7dbb0123247db29e8799991140bbdbd9bb - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

    b46043a06dd9bbd63e4214d5fbc7fd56e1ff0618 - SHA1 - Possible malicious payload

    753afdecd9f5402d004e8e5f768170ae9a468ca5 - SHA1 - Possible malicious payload

    8f533c7cb1524b00f7b0311c2ea8603298d6b2ca - SHA1 - Possible malicious payload

    3a3bc6a5b4db1a4e961abcb002d26fe9d5e5c349 - SHA1 - Possible malicious payload

    897f70eb41d302b045fcb05ed0693675e778ce57 - SHA1 - Possible malicious payload

    6ddd5644809606e3dc1e2cc06059c3f5e6176f85 - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

    68a94f7cdcaf8853ea99251c1ecc67ae9b32eba8 - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

    MITRE ATT&CK Mapping

    T1659 -Initial Access, Command and Control -Content Injection

    T1588.001 -Resource Development -Malware

    T1189 -Initial Access -Drive-by Compromise

    T1105 -Command and Control -Ingress Tool Transfer

    T1657 -Impact -Financial Theft

    T1497.001 -Impact -Compute Hijacking

    T1496 -Impact -Resource Hijacking

    T1210 -Lateral Movement -Exploitation of Remote Services

    T1036.012 -Stealth -Browser Fingerprint

    T1071.001 -Command and Control -Web Protocols

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    About the author
    Min Kim
    Cyber Security Analyst

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    June 12, 2026

    Cybersecurity for the Sports Sector: The Threats Facing a Digitized Industry in 2026

    Sports Stadium cybersecurityDefault blog imageDefault blog image

    Securing sporting events in 2026

    When you walk into a stadium on game day, you are entering a small smart city. Ticketing, turnstiles, payments, public Wi-Fi for tens of thousands of fans, CCTV, lighting, even the HVAC all run on connected systems. The experience for fans has become unmatched, but that dependency has created a much larger attack surface than people may realize.

    Our latest threat research backs that up. In the past year, a survey that Darktrace commissioned found that 84% of respondents from professional sports organizations had at least one cyber incident, and 57% were hit more than once. For a sector that relies on the impact of the live moment, those numbers translate directly into operational risk.

    Why sports is a target for cyber attacks

    Sport is a highly visible target with fixed timelines, so attackers know exactly when disruption will have the most impact. It also holds valuable data, athlete medical records, contracts, sponsorship deals, which carry financial, reputational, and regulatory risk if exposed. At the same time, delivery depends on a wide set of third parties: ticketing providers, broadcasters, cloud services, stadium technology. Any of those connections can become an entry point. Put visibility, timing, data, and dependency together, and you get an environment where even a small foothold can turn into a visible, time-critical incident.

    How attackers target email and identity

    Email and identity remain the front door. From October 2025 through March 2026, Darktrace / EMAIL™ detected more than 116,000 phishing emails aimed at sports organizations across our customer base, and our sports customers received 19% more phishing emails than organizations in other sectors. The numbers tell the story:

    BY THE NUMBERS

    • 21% of phishing emails were aimed at VIPs.
    • 37% used novel social engineering.
    • 84% of malicious emails passed DMARC authentication

    A large proportion of these emails passed authentication checks, which means traditional security controls are no longer a reliable barrier. Attackers are not relying on spoofed domains – they're using legitimate infrastructure and trusted platforms. Behavior matters. Once an account is compromised, the behavior shifts quickly. Login patterns change, inbox rules are created to hide responses, and accounts start being used for internal discovery or further phishing. These aren’t high-noise events. They sit in normal workflows, which is why they’re often missed.

    Ransomware tells a similar story. In one case inside a sports deployment, attackers had quietly been moving data to an outside server for a full two weeks before they triggered encryption. By the time the ransom note appeared, the outcome was already set. That sequence shows up consistently is access first, movement next, disruption last. If detection starts at encryption, it’s already too late.

    Why AI is an emerging blind spot in sports

    The increasing adoption of AI is expanding the potential attack surface. 72% of the security professionals we surveyed expect AI to increase their cyber risk over the next year, and yet 35% are already using or planning to use it in stadium operations, the most critical functions to protect. In addition to prompt injection and AI build risks, shadow AI is becoming a more immediate issue. Staff are already putting sensitive data—performance metrics, scouting reports, contracts, health data—into tools with little or no governance. The upside is clear, but so is the exposure—and it is happening before most organizations have any visibility or control. At the same time, attackers are using the same technology to scale phishing and social engineering. The net effect is simple: more exposure, at higher speed.

    How can cybersecurity professionals prepare

    Across high profile events, Darktrace’s experience shows that effective cyber defense includes preparation, real‑time visibility, and the ability to respond dynamically and decisively when timing, complexity, and public exposure converge.

    There are a few strategic implications for cybersecurity teams:

    • Get behavioral visibility across IT and OT, not just corporate systems.
    • Treat identity as your control plane. Most attacks in this sector start with credentials, not malware. MFA with behavioral detection helps solve that challenge.
    • Control third party and AI access the same way you control your own environment.
    • Rehearse response for live conditions, where decisions happen in minutes. Detection and response need to account for non-ideal conditions when engineers are under pressure and time constrained. In sport, timing is what turns small issues into major incidents. The same activity that would be manageable midweek becomes critical during a live event.

    Why 2026 raises the cybersecurity stakes for sports

    With the 2026 World Cup about to stretch across three countries and dozens of host cities, the attack surface is wide and the schedule is unforgiving.

    Geopolitical signaling is raising the threat profile further. Previous international sporting events have demonstrated that nation‑state actors use the cyber domain to signal intent, influence narratives, or retaliate symbolically. In the context of the 2026 World Cup, Russia’s continued exclusion from international sport, the ongoing conflict in Ukraine, US defensive support to Ukraine, and Iran’s likely participation in the tournament introduce additional motivations for state‑aligned and non‑traditional affiliated actors to operate below the threshold of armed conflict. This doesn’t require new techniques—just the right timing and visibility.

    In practice, this comes down to preparation: knowing what normal looks like across IT and OT, controlling third-party access, and spotting when behavior shifts.

    In sport, disruption does not build slowly—it happens in real time and in public. By that point, the groundwork has already been set, long before the whistle goes.

    About this research

    Findings are based on Darktrace threat-research telemetry across sports-sector customer deployments (Q4 2025–Q1 2026) and a survey of 875 IT cybersecurity professionals in the US, UK, Australia, and Germany, fielded by Opinion Matters between May 28 and June 3, 2026. Read the full report for complete methodology, incident analysis, and strategic recommendations.

    [related-resource]

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    About the author
    Nathaniel Jones
    VP, Security & AI Strategy, Field CISO
    Your data. Our AI.
    Elevate your network security with Darktrace AI