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January 14, 2025

RansomHub Ransomware: Darktrace’s Investigation of the Newest Tool in ShadowSyndicate's Arsenal

Between September and October 2024, Darktrace investigated several customer networks compromised by RansomHub attacks. Further analysis revealed a connection to the ShadowSyndicate threat group. Read on to discover how these entities are linked and the tactics, techniques, and procedures employed in these attacks.
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
Qing Hong Kwa
Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore
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14
Jan 2025

What is ShadowSyndicate?

ShadowSyndicate, also known as Infra Storm, is a threat actor reportedly active since July 2022, working with various ransomware groups and affiliates of ransomware programs, such as Quantum, Nokoyawa, and ALPHV. This threat actor employs tools like Cobalt Strike, Sliver, IcedID, and Matanbuchus malware in its attacks. ShadowSyndicate utilizes the same SSH fingerprint (1ca4cbac895fc3bd12417b77fc6ed31d) on many of their servers—85 as of September 2023. At least 52 of these servers have been linked to the Cobalt Strike command and control (C2) framework [1].

What is RansomHub?

First observed following the FBI's takedown of ALPHV/BlackCat in December 2023, RansomHub quickly gained notoriety as a Ransomware-as-a-Service (RaaS) operator. RansomHub capitalized on the law enforcement’s disruption of the LockBit group’s operations in February 2024 to market themselves to potential affiliates who had previously relied on LockBit’s encryptors. RansomHub's success can be largely attributed to their aggressive recruitment on underground forums, leading to the absorption of ex-ALPHV and ex-LockBit affiliates. They were one of the most active ransomware operators in 2024, with approximately 500 victims reported since February, according to their Dedicated Leak Site (DLS) [2].

ShadowSyndicate and RansomHub

External researchers have reported that ShadowSyndicate had as many as seven different ransomware families in their arsenal between July 2022, and September 2023. Now, ShadowSyndicate appears to have added RansomHub’s their formidable stockpile, becoming an affiliate of the RaaS provider [1].

Darktrace’s analysis of ShadowSyndicate across its customer base indicates that the group has been leveraging RansomHub ransomware in multiple attacks in September and October 2024. ShadowSyndicate likely shifted to using RansomHub due to the lucrative rates offered by this RaaS provider, with affiliates receiving up to 90% of the ransom—significantly higher than the general market rate of 70-80% [3].

In many instances where encryption was observed, ransom notes with the naming pattern “README_[a-zA-Z0-9]{6}.txt” were written to affected devices. The content of these ransom notes threatened to release stolen confidential data via RansomHub’s DLS unless a ransom was paid. During these attacks, data exfiltration activity to external endpoints using the SSH protocol was observed. The external endpoints to which the data was transferred were found to coincide with servers previously associated with ShadowSyndicate activity.

Darktrace’s coverage of ShadowSyndicate and RansomHub

Darktrace’s Threat Research team identified high-confidence indicators of compromise (IoCs) linked to the ShadowSyndicate group deploying RansomHub. The investigation revealed four separate incidents impacting Darktrace customers across various sectors, including education, manufacturing, and social services. In the investigated cases, multiple stages of the kill chain were observed, starting with initial internal reconnaissance and leading to eventual file encryption and data exfiltration.

Attack Overview

Timeline attack overview of ransomhub ransomware

Internal Reconnaissance

The first observed stage of ShadowSyndicate attacks involved devices making multiple internal connection attempts to other internal devices over key ports, suggesting network scanning and enumeration activity. In this initial phase of the attack, the threat actor gathers critical details and information by scanning the network for open ports that might be potentially exploitable. In cases observed by Darktrace affected devices were typically seen attempting to connect to other internal locations over TCP ports including 22, 445 and 3389.

C2 Communication and Data Exfiltration

In most of the RansomHub cases investigated by Darktrace, unusual connections to endpoints associated with Splashtop, a remote desktop access software, were observed briefly before outbound SSH connections were identified.

Following this, Darktrace detected outbound SSH connections to the external IP address 46.161.27[.]151 using WinSCP, an open-source SSH client for Windows used for secure file transfer. The Cybersecurity and Infrastructure Security Agency (CISA) identified this IP address as malicious and associated it with ShadowSyndicate’s C2 infrastructure [4]. During connections to this IP, multiple gigabytes of data were exfiltrated from customer networks via SSH.

Data exfiltration attempts were consistent across investigated cases; however, the method of egress varied from one attack to another, as one would expect with a RaaS strain being employed by different affiliates. In addition to transfers to ShadowSyndicate’s infrastructure, threat actors were also observed transferring data to the cloud storage and file transfer service, MEGA, via HTTP connections using the ‘rclone’ user agent – a command-line program used to manage files on cloud storage. In another case, data exfiltration activity occurred over port 443, utilizing SSL connections.

Lateral Movement

In investigated incidents, lateral movement activity began shortly after C2 communications were established. In one case, Darktrace identified the unusual use of a new administrative credential which was quickly followed up with multiple suspicious executable file writes to other internal devices on the network.

The filenames for this executable followed the regex naming convention “[a-zA-Z]{6}.exe”, with two observed examples being “bWqQUx.exe” and “sdtMfs.exe”.

Cyber AI Analyst Investigation Process for the SMB Writes of Suspicious Files to Multiple Devices' incident.
Figure 1: Cyber AI Analyst Investigation Process for the SMB Writes of Suspicious Files to Multiple Devices' incident.

Additionally, script files such as “Defeat-Defender2.bat”, “Share.bat”, and “def.bat” were also seen written over SMB, suggesting that threat actors were trying to evade network defenses and detection by antivirus software like Microsoft Defender.

File Encryption

Among the three cases where file encryption activity was observed, file names were changed by adding an extension following the regex format “.[a-zA-Z0-9]{6}”. Ransom notes with a similar naming convention, “README_[a-zA-Z0-9]{6}.txt”, were written to each share. While the content of the ransom notes differed slightly in each case, most contained similar text. Clear indicators in the body of the ransom notes pointed to the use of RansomHub ransomware in these attacks. As is increasingly the case, threat actors employed double extortion tactics, threatening to leak confidential data if the ransom was not paid. Like most ransomware, RansomHub included TOR site links for communication between its "customer service team" and the target.

Figure 2: The graph shows the behavior of a device with encryption activity, using the “SMB Sustained Mimetype Conversion” and “Unusual Activity Events” metrics over three weeks.

Since Darktrace’s Autonomous Response capability was not enabled during the compromise, the ransomware attack succeeded in its objective. However, Darktrace’s Cyber AI Analyst provided comprehensive coverage of the kill chain, enabling the customer to quickly identify affected devices and initiate remediation.

Figure 3: Cyber AI Analyst panel showing the critical incidents of the affected device from one of the cases investigated.

In lieu of Autonomous Response being active on the networks, Darktrace was able to suggest a variety of manual response actions intended to contain the compromise and prevent further malicious activity. Had Autonomous Response been enabled at the time of the attack, these actions would have been quickly applied without any human interaction, potentially halting the ransomware attack earlier in the kill chain.

Figure 4: A list of suggested Autonomous Response actions on the affected devices."

Conclusion

The Darktrace Threat Research team has noted a surge in attacks by the ShadowSyndicate group using RansomHub’s RaaS of late. RaaS has become increasingly popular across the threat landscape due to its ease of access to malware and script execution. As more individual threat actors adopt RaaS, security teams are struggling to defend against the increasing number of opportunistic attacks.

For customers subscribed to Darktrace’s Security Operations Center (SOC) services, the Analyst team promptly investigated detections of the aforementioned unusual and anomalous activities in the initial infection phases. Multiple alerts were raised via Darktrace’s Managed Threat Detection to warn customers of active ransomware incidents. By emphasizing anomaly-based detection and response, Darktrace can effectively identify devices affected by ransomware and take action against emerging activity, minimizing disruption and impact on customer networks.

Credit to Kwa Qing Hong (Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore) and Signe Zahark (Principal Cyber Analyst, Japan)

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Appendices

Darktrace Model Detections

Antigena Models / Autonomous Response:

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Insider Threat / Antigena SMB Enumeration Block

Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity

Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach

Antigena / Network / Significant Anomaly / Antigena Significant Server Anomaly Block

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server 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 File then New Outbound Block


Network Reconnaissance:

Device / Network Scan

Device / ICMP Address Scan

Device / RDP Scan
Device / Anomalous LDAP Root Searches
Anomalous Connection / SMB Enumeration
Device / Spike in LDAP Activity

C2:

Enhanced Monitoring - Device / Lateral Movement and C2 Activity

Enhanced Monitoring - Device / Initial Breach Chain Compromise

Enhanced Monitoring - Compromise / Suspicious File and C2

Compliance / Remote Management Tool On Server

Anomalous Connection / Outbound SSH to Unusual Port


External Data Transfer:

Enhanced Monitoring - Unusual Activity / Enhanced Unusual External Data Transfer

Unusual Activity / Unusual External Data Transfer

Anomalous Connection / Data Sent to Rare Domain

Unusual Activity / Unusual External Data to New Endpoint

Compliance / SSH to Rare External Destination

Anomalous Connection / Application Protocol on Uncommon Port

Enhanced Monitoring - Anomalous File / Numeric File Download

Anomalous File / New User Agent Followed By Numeric File Download

Anomalous Server Activity / Outgoing from Server

Device / Large Number of Connections to New Endpoints

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

Anomalous Connection / Uncommon 1 GiB Outbound

Lateral Movement:

User / New Admin Credentials on Server

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / High Volume of New or Uncommon Service Control

Anomalous File / Internal / Executable Uploaded to DC

Anomalous Connection / Suspicious Activity On High Risk Device

File Encryption:

Compliance / SMB Drive Write

Anomalous File / Internal / Additional Extension Appended to SMB File

Compromise / Ransomware / Possible Ransom Note Write

Anomalous Connection / Suspicious Read Write Ratio

List of Indicators of Compromise (IoCs)

IoC - Type - Description + Confidence

83.97.73[.]198 - IP - Data exfiltration endpoint

108.181.182[.]143 - IP - Data exfiltration endpoint

46.161.27[.]151 - IP - Data exfiltration endpoint

185.65.212[.]164 - IP - Data exfiltration endpoint

66[.]203.125.21 - IP - MEGA endpoint used for data exfiltration

89[.]44.168.207 - IP - MEGA endpoint used for data exfiltration

185[.]206.24.31 - IP - MEGA endpoint used for data exfiltration

31[.]216.148.33 - IP - MEGA endpoint used for data exfiltration

104.226.39[.]18 - IP - C2 endpoint

103.253.40[.]87 - IP - C2 endpoint

*.relay.splashtop[.]com - Hostname - C2 & data exfiltration endpoint

gfs***n***.userstorage.mega[.]co.nz - Hostname - MEGA endpoint used for data exfiltration

w.api.mega[.]co.nz - Hostname - MEGA endpoint used for data exfiltration

ams-rb9a-ss.ams.efscloud[.]net - Hostname - Data exfiltration endpoint

MITRE ATT&CK Mapping

Tactic - Technqiue

RECONNAISSANCE – T1592.004 Client Configurations

RECONNAISSANCE – T1590.005 IP Addresses

RECONNAISSANCE – T1595.001 Scanning IP Blocks

RECONNAISSANCE – T1595.002 Vulnerability Scanning

DISCOVERY – T1046 Network Service Scanning

DISCOVERY – T1018 Remote System Discovery

DISCOVERY – T1083 File and Directory Discovery
INITIAL ACCESS - T1189 Drive-by Compromise

INITIAL ACCESS - T1190 Exploit Public-Facing Application

COMMAND AND CONTROL - T1001 Data Obfuscation

COMMAND AND CONTROL - T1071 Application Layer Protocol

COMMAND AND CONTROL - T1071.001 Web Protocols

COMMAND AND CONTROL - T1573.001 Symmetric Cryptography

COMMAND AND CONTROL - T1571 Non-Standard Port

DEFENSE EVASION – T1078 Valid Accounts

DEFENSE EVASION – T1550.002 Pass the Hash

LATERAL MOVEMENT - T1021.004 SSH

LATERAL MOVEMENT – T1080 Taint Shared Content

LATERAL MOVEMENT – T1570 Lateral Tool Transfer

LATERAL MOVEMENT – T1021.002 SMB/Windows Admin Shares

COLLECTION - T1185 Man in the Browser

EXFILTRATION - T1041 Exfiltration Over C2 Channel

EXFILTRATION - T1567.002 Exfiltration to Cloud Storage

EXFILTRATION - T1029 Scheduled Transfer

IMPACT – T1486 Data Encrypted for Impact

References

1.     https://www.group-ib.com/blog/shadowsyndicate-raas/

2.     https://www.techtarget.com/searchsecurity/news/366617096/ESET-RansomHub-most-active-ransomware-group-in-H2-2024

3.     https://cyberint.com/blog/research/ransomhub-the-new-kid-on-the-block-to-know/

4.     https://www.cisa.gov/sites/default/files/2024-05/AA24-131A.stix_.xml

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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
Qing Hong Kwa
Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore

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July 22, 2026

When AI Agents Go Off Script: What the OpenAI and Hugging Face Incident Means for Defenders

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The recent security incident disclosed jointly by OpenAI and Hugging Face is quickly becoming one of the most discussed AI security stories of the year; it's one that has generated great discussion amongst our researchers and technical teams here at Darktrace.

During an internal evaluation of advanced OpenAI models, an autonomous agent reportedly escaped its intended testing boundaries, obtained internet access, and compromised Hugging Face infrastructure while attempting to evaluate and benchmark the model's cyber capabilities. OpenAI has described it as an unprecedented cyber incident and is collaborating with Hugging Face to investigate and share its findings publicly to help defenders understand the implications and improve AI safety across the community.

What this means for organizations deploying AI agents

The most important takeaway for security leaders is not that the AI model is capable of complex and effective chained attacks. It's what this tells us about the guardrails and protections that need to be put in place to ensure these increasingly capable models are safely used within our businesses.

According to OpenAI's investigation, the models were not acting with malicious intent. They were attempting to achieve a legitimate assigned objective. In pursuing that objective, they identified a path that involved obtaining information from a third party's environment and followed it to completion. From the model's perspective, that appears to have been an effective solution to the problem it was given.

Many organizations still hold the assumption that giving an AI agent a legitimate goal will naturally result in legitimate behavior. Increasingly, that is not something defenders can safely assume.

A human who encounters a "permission denied" message generally understands the technical restriction as well as the social meaning behind it. An agent may interpret that message only as evidence that one method to achieve its objective has failed. What makes this incident notable is the agent’s ability to keep pursuing the objective, revise its plan, and combine many individually useful actions into an outcome its designers did not intend.

This is not an isolated challenge. Recent research from the UK's AI Security Institute found that frontier models often attempt to "cheat" evaluations by finding unintended shortcuts to success. More interestingly, that behavior is not always reflected in chain-of-thought outputs and is not always admitted when models are questioned about their actions. One lesson for defenders is that understanding what an AI system says it is doing is not always the same as understanding its behavior and what it is actually doing.

Why behavioral security is an essential security foundation

That's the principle behind Darktrace / SECURE AI and our broader approach to behavioral security. Understanding intent or putting in place static guardrails is not enough; security teams need visibility into how agents behave over time, what resources they interact with, how their behavior changes, and whether they begin pursuing objectives in unexpected ways. Research released by NIST earlier this summer reinforced that rules alone are not sufficient to defend against AI threats, with one conclusion being that "there will always be a way to prompt an AI system to disregard its rules - it's just a matter of finding it."

Another interesting lesson is that nothing described in OpenAI's preliminary findings appears fundamentally impossible for a skilled human attacker. But it does highlight the accessibility and scale that could be unlocked for attackers as increasingly capable models become more widely available. Resource constraints that attackers deal with will decrease as frontier models can help execute elements of those attack workflows autonomously, persist through failure, and iterate towards an objective. Capabilities that were once limited to skilled attackers can increasingly be supplemented with these systems, lowering the barrier to entry to conduct complex cyber operations.

It is incredibly positive to see OpenAI and Hugging Face investigating this incident collaboratively and sharing their findings publicly. Transparency and collaboration around incidents like these helps the entire security community better understand how rapidly AI capabilities are evolving and where we need to improve safeguards. It also reinforces why Darktrace is excited to be partnering with OpenAI through the Daybreak Cyber Partner Program: to explore how advanced AI can be used safely and responsibly in support of defenders while developing the visibility, governance, and security controls needed for increasingly autonomous systems.

This incident is an important reminder that capable AI systems will not always behave in the ways we expect. As organizations deploy AI agents across critical workflows, understanding behavior is an even more important foundation for effective security.

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Dr. Tim Bazalgette
Chief AI Officer, Darktrace

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July 22, 2026

How to Secure AI in Stadium Operations?

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Key takeaways

  • AI is entering high-impact stadium functions such as access control, crowd management, ticketing, facilities, and surveillance.  
  • Shadow AI and third-party AI use can create risks that stadium security teams cannot readily see.  
  • Security teams must understand not only which AI systems exist, but also what they can access and what actions they can take.  
  • Live-event resilience requires continuous monitoring and response across AI, IT, OT, identities, and third parties.

Modern stadiums are infrastructure unlike any other. I’ve written before on event day sparking stadiums into life with shops and food stands, transport hubs, vast telecommunications infrastructure, field-side technology and beyond, acting as one super-sized, connected ecosystem. Stadiums’ scale and complexity make them some of the toughest environments in cybersecurity. Now, we’re adding AI to those operations and bringing a new dimension of risk.

The benefits of AI in stadium operations are easy to see. It can help stadium operators move fans safely through crowded gates, forecast demand at concession stands, support biometric entry, identify suspicious behavior on CCTV, and manage heating and ventilation. Used well, it can make live events safer, faster, and more efficient.

But it also changes the security model.

In Darktrace’s recent research into the threat landscape surrounding sports, we asked cybersecurity professionals protecting professional sports organizations where in their footprint a cyber compromise would have the greatest impact. The area they named most, highlighted by 34% of the professionals we spoke to, was stadium operations. At the same time, 35% said their organizations are already using AI in stadium operations, or plan to do so in the next 12 months.

Security teams are no longer just protecting traditional IT systems around a stadium. They are increasingly being asked to protect AI systems that are operating in the stadium’s most fundamental functions.

Approved AI vs. shadow AI in stadium operations

There is a clear difference between AI a stadium’s security team knows about and AI it does not.

Approved AI is the AI that has been reviewed, tested, and integrated into the venue’s operating environment. It may support CCTV analytics, access control, facility management, ticketing, logistics, broadcast operations, or anti-piracy monitoring. It should have clear ownership, access controls, logging, vendor review, and data protection rules. That does not make it risk-free, but it allows security teams to institute proper governance.

Shadow AI is different. It is the unapproved use of AI tools by employees, contractors, or suppliers. It often starts with good intent. Someone wants to work faster. A staff member pastes internal information into a public AI tool to draft a briefing. A developer uses an AI assistant to debug ticketing code. A supplier connects an AI scheduling tool to delivery routes. A designer uploads unreleased venue plans or sponsor material to generate a mockup.

None of those actions may feel like a security decision to the person doing them. But each one can move sensitive operational data into an environment the stadium does not control, creating hidden risk.

The approved AI stack may be visible to security teams. The shadow AI stack often is not.

Why game day increases AI cybersecurity risk

In a typical enterprise environment, a security team may have hours to investigate a strange login or an unexpected connection to a third-party service. Within a stadium, the moment an incident is likely to occur is also the moment when teams are at their most stretched and the incident can have the greatest repercussions: game day.

If an AI system used for crowd management behaves unexpectedly, the issue is not only technical. It may affect physical movement inside the venue.

If a supplier tool is sending operational data to an unapproved AI platform, the issue is not only data governance. It may expose delivery routes, restricted access schedules, or staffing plans.

The most dangerous scenario is not always a loud, dramatic attack but a hidden dependency that no one has mapped such as a vendor adding an AI feature through a software update or a staff workflow using an unapproved tool.

By the time the venue is live, those hidden connections can become operational risk.

The supply chain is part of the stadium attack surface

Any major sporting event is made by its supply chain and partnerships: catering firms, transport providers, broadcast systems, facilities teams. Every piece is necessary and each creates a security channel. The risk of supply chain compromise has been well established for some time and has been the source of some of the most high-profile breaches we’ve seen. The data breach at MSG Entertainment, owner of Madison Square Garden, that was widely reported in March, originated in a breach of Oracle’s E-Business Suite, used in MSG Entertainment’s back-office systems, while the 2018 Olympic Destroyer attack on the Pyeongchang Winter Olympics reportedly began with the compromise of the main IT service provider for the Games. The addition of AI is heightening the risk.

A stadium can have strict rules for its own AI systems, but its vendors may be using separate tools. Some may use AI to manage staffing, delivery windows, inventory, or customer communications. Others may not realize that AI features have been added into software they already use.

This is one of the hardest parts of securing AI in stadium operations. The risk does not always come from a tool the venue selected. It may come from a tool a supplier selected or a feature the supplier did not know had been turned on.

Security teams need to treat vendor AI the same way they treat vendor access. They need to know what suppliers can connect to, what data they can see, what tools they use, and whether those tools introduce new routes for data exposure or lateral movement.

A third-party AI tool does not need deep access to create risk. Sometimes it only needs the right operational detail at the wrong time.

Four questions for securing AI in stadium operations

As AI becomes part of stadium operations, security teams need to move beyond basic approval lists. There are four questions they need to ask:

1. Where is AI being used?

This includes obvious tools, such as computer vision, access control, ticketing, logistics, and facility management. But it also includes less visible AI inside SaaS platforms, vendor tools, browser extensions, developer workflows, smart building systems, and collaboration tools.

2. What can the AI access?

Can it see incident logs, staffing plans, ticketing data, video feeds, building controls, fan information, credentials, or supplier systems? Can it only analyze information, or can it also trigger actions?

3. What can the AI do?

AI agents are not just passive tools. Some can call APIs, update records, generate instructions, trigger workflows, or act with the permissions of a user or service account. In a stadium, that distinction is critical. There is a big difference between an AI system that recommends an action and one that can take an action.

4. What does normal look like?

In your security architecture, static rules will not be enough. AI use changes quickly: tools appear inside existing platforms, vendors add new services, and staff find workarounds when they are under pressure. Security teams need to understand normal behavior across people, identities, devices, networks, cloud services, suppliers, and AI tools so they can spot when something changes.

That is especially important in live-event environments, where small anomalies can matter. A connection to an unapproved AI service may be harmless in one context and serious in another, and an AI agent taking action at 3 a.m. may be expected during setup but suspicious during a match. Context is what turns raw activity into useful security insight. It’s also what enables rapid response. Your own AI-based security systems can respond to threats at machine speed if they can build the live context to know action needs to be taken.

AI can make stadiums safer, but only if it is secured

AI has a real role to play in stadium operations. It can help teams detect crowd pressure earlier, reduce bottlenecks, manage facilities more efficiently, improve the fan experience, and support event teams during high-pressure moments.

The answer is not to slow all AI adoption. That's not the goal. The answer is to make AI visible, governed, and secure before it becomes part of match-day operations.

For stadium operators and event organizers, that means mapping AI use across the venue and supplier ecosystem. It means understanding what each AI system can access and what actions it can take. It means giving staff approved tools that meet their needs, rather than leaving them to find workarounds. It means writing AI use into vendor contracts and audits. And it means monitoring behavior across the full environment, not only the systems that are easiest to see. A stadium cannot secure what it cannot see.

When AI becomes part of how a stadium moves people, controls access, manages facilities, supports suppliers, and protects media rights, it stops being a side project. It becomes part of the event infrastructure.

Event infrastructure must be thoroughly prepared before venue gates open and sustained with the operational resilience required to support a secure, seamless, and reliable event experience.

How Darktrace helps secure AI in stadium operations

Darktrace brings more than a decade of behavioral AI expertise, built on an enterprise‑wide platform designed to operate in complex, ambiguous environments. We protect the large-scale integrated IT and OT environments that underpin stadium operations from the 2022 FIFA World Cup in Qatar, to Formula 1 Grand Prixes around the world and stadiums across the USA.

Other cybersecurity technologies try to predict each new attack based on historical attacks. The problem is that AI operates like humans do. Every action introduces new information that changes how AI behaves, making it unpredictable in nature. Historical attack tactics are now only a small part of the equation, forcing vendors to retrofit unproven acquisitions to secure AI.  

Darktrace is fundamentally different. Our Adaptive AI continuously learns how your people and AI behave, building an understanding of your organization so it can detect and respond autonomously when behavior deviates. Our Behavioral Defense Platform secures your AI, people, and infrastructure as you onboard new workflows, agents, and applications, enabling your AI transformation at scale.

As AI changes what organizations can do, Darktrace helps them move forward with confidence. We give the security teams defending the people and technology within stadium infrastructure the understanding, visibility, and autonomous action they need to protect new technologies as they are integrated into operations, so their organizations drive the progress that will define the AI era.

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Karim Benslimane
VP, Field CISO
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