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October 30, 2024

Post-Exploitation Activities on Fortinet Devices: A Network-Based Analysis

This blog explores recent findings from Darktrace's Threat Research team on active exploitation campaigns targeting Fortinet appliances. This analysis focuses on the September 2024 exploitation of FortiManager via CVE-2024-47575, alongside related malicious activity observed in June 2024.
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 Potter
Senior Cyber Analyst
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30
Oct 2024

Introduction: Uncovering active exploitation of Fortinet vulnerabilities

As part of the Darktrace Threat Research team's routine analysis of October's Patch Tuesday vulnerabilities, the team began searching for signs of active exploitation of a critical vulnerability (CVE-2024-23113) affecting the FortiGate to FortiManager (FGFM) protocol.[1]

Although the investigation was prompted by an update regarding CVE 2024-23113, results of the inquiry yielded evidence of widespread exploitation of Fortinet devices in both June and September 2024 potentially via multiple vulnerabilities including CVE 2024-47575. Analysts identified two clusters of activity involving overlapping indicators of compromise (IoCs), likely constituting unique campaigns targeting Fortinet appliances.

This blog will first highlight the finding and analysis of the network-based indicators of FortiManager post-exploitation activity in September, likely involving CVE 2024-47575. The article will then briefly detail a similar pattern of malicious activity observed in June 2024 that involved similar IoCs that potentially comprises a distinct campaign targeting Fortinet perimeter devices.

Fortinet CVE Disclosures

FortiManager devices allow network administrators to manage Fortinet devices on organizations’ networks.[2] One such subset of devices managed through this method are Fortinet firewalls known as FortiGate. These manager and firewall devices communicate with each other via a custom protocol known as FortiGate to FortiManager (FGFM), whereby devices can perform reachability tests and configuration-related actions and reporting.[3] By default, FortiManager devices operate this protocol via port 541.[4]

Fortinet Product Security Incident Response Team released multiple announcements revealing vulnerabilities within the daemon responsible for implementing operability of the FGFM service. Specifically, CVE 2024-23113 enables attackers to potentially perform arbitrary remote command execution through the use of a specially crafted format string to a FortiGate device running the “fgfm daemon”.[5][6]  Similarly, the exploitation of CVE 2024-47575  could also allow remote command execution due to a missing authentication mechanism when targeting specifically FortiManager devices.[7][8]  Given how prolific both FortiGate and FortiManager devices are within the global IT security ecosystem, Darktrace analysts hypothesized that there may have been specific targeting of such devices within the customer base using these vulnerabilities throughout mid to late 2024.

Campaign Analysis

In light of these vulnerability disclosures, Darktrace’s Threat Research team began searching for signs of active exploitation by investigating file download, lateral movement or tooling activity from devices that had previously received suspicious connections on port 541. The team first noticed increases in suspicious activity involving Fortinet devices particularly in mid-September 2024. Further analysis revealed a similar series of activities involving some overlapping devices identified in June 2024. Analysis of these activity clusters revealed a pattern of malicious activity against likely FortiManager devices, including initial exploitation, payload retrieval, and exfiltration of probable configuration data.

Below is an overview of malicious activity we have observed by sector and region:

Sector and region affected by malicious activity on fortigate devices
The sectors of affected customers listed above are categorized according to the United Kingdom’s Standard Industrial Classification (SIC).

Initial Exploitation of FortiManager Devices

Across many of the observed cases in September, activity began with the initial exploitation of FortiManager devices via incoming connectivity over TLS/SSL. Such activity was detected due to the rarity of the receiving devices accepting connections from external sources, particularly over destination port 541. Within nearly all investigated incidents, connectivity began with the source IP, 45.32.41[.]202, establishing an SSL session with likely FortiManager devices.  Device types were determined through a combination of the devices’ hostnames and the noted TLS certificate issuer for such encrypted connections.

Due to the encrypted nature of the connection, it was not possible to ascertain the exploit used in the analyzed cases. However, given the similarity of activities targeting FortiManager devices and research conducted by outside firms, attackers likely utilized CVE 2024-47575.[9] For example, the source IP initiating the SSL sessions also has been referenced by Mandiant as engaging in CVE 2024-47575 exploitation. In addition to a consistent source IP for the connections, a similar JA3 hash was noted across multiple examined accounts, suggesting a similarity in source process for the activity.

In most cases observed by Darktrace, the incoming connectivity was followed by an outgoing connection on port 443 to the IP 45.32.41[.]202. Uncommon reception of encrypted connections over port 541, followed by the initiation of outgoing SSL connections to the same endpoint would suggest probable successful exploitation of FortiManager CVEs during this time.

Model alert logs highlighting the incoming connectivity over port 541 to the FortiManager devices followed by outgoing connection to the external IP.
Figure 1: Model alert logs highlighting the incoming connectivity over port 541 to the FortiManager devices followed by outgoing connection to the external IP.

Payload Retrieval

Investigated devices commonly retrieved some form of additional content after incoming connectivity over port 541. Darktrace’s Threat Research team noted how affected devices would make HTTP GET requests to the initial exploitation IP for the URI: /dom.js. This URI, suggestive of JavaScript content retrieval, was then validated by the HTTP response content type. Although Darktrace could see the HTTP content of the connections, usage of destination port 443 featured prominently during these HTTP requests, suggesting an attempt at encryption of the session payload details.

Figure 2: Advanced Search HTTP log to the exploitation IP noting the retrieval of JavaScript content using the curl user agent.

Cyber AI Analyst investigation into the initial exploitation activity. This incident emphasizes the rare external connectivity over port 443 requesting JavaScript content following the incoming connections over port 541.
Figure 3: Cyber AI Analyst investigation into the initial exploitation activity. This incident emphasizes the rare external connectivity over port 443 requesting JavaScript content following the incoming connections over port 541.

The operators of the campaign also appear to have used a consistent user agent for payload retrieval: curl 8.4.0. Usage of an earlier version of the curl (version 7 .86.0) was only observed in one instance. The incorporation of curl utility to establish HTTP connections therefore suggests interaction with command-line utilities on the inspected Fortinet hosts. Command-line interaction also adds validity to the usage of exploits such as CVE 2024-47575 which enable unauthenticated remote command execution. Moreover, given the egress of data seen by the devices receiving this JavaScript content, Darktrace analysts concluded that this payload likely resulted in the configuration aggregation activity noted by external researchers.

Data Exfiltration

Nearly all devices investigated during the September time period performed some form of data exfiltration using the HTTP protocol. Most frequently, devices would initiate these HTTP requests using the same curl user agent already observed during web callback activity.  Again, usage of this tool heavily suggests interaction with the command-line interface and therefore command execution.

The affected device typically made an HTTP POST request to one or both of the following two rare external IPs: 104.238.141[.]143 and 158.247.199[.]37. One of the noted IPs, 104.238.141[.]143, features prominently within external research conducted by Mandiant during this time. These HTTP POST requests nearly always sent data to the /file endpoint on the destination IPs. Analyzed connections frequently noted an HTTP mime type suggestive of compressed archive content. Some investigations also revealed specific filenames for the data sent externally: “.tm”. HTTP POST requests occurred without a specified hostname. This would suggest the IP address may have already been cached locally on the device from a running process or the IP address was hardcoded into the details of unwarranted code running on the system. Moreover, many such POSTs occurred without a GET request, which can indicate exfiltration activity.

Model alert logs noting both the connection to the IP 158.247.199[.]37 over port 443 without a hostname, and the unusual activity metric describing how the request was made without a prior HTTP GET request. Such activity can indicate malicious data exfiltration.
Figure 4: Model alert logs noting both the connection to the IP 158.247.199[.]37 over port 443 without a hostname, and the unusual activity metric describing how the request was made without a prior HTTP GET request. Such activity can indicate malicious data exfiltration.

Interestingly, in many investigations, analysts noticed a lag period between the initial access and exploitation, and the exfiltration of data via HTTP. Such a pause, sometimes over several hours to over a day, could reflect the time needed to aggregate data locally on the host or as a strategic pause in activity to avoid detection. While not present within every compromise activity logs inspected, the delay could represent slight adjustments in behavior during the campaign by the threat actor.

Figure 5: Advanced search logs showing both the payload retrieval and exfiltration activity, emphasizing the gap in time between payload retrieval and exfiltration via HTTP POST request.

HTTP and file identification details identified during this time also directly correspond to research conducted by Mandiant. Not only do we see overlap in IPs identified as receiving the posted data (104.238.141[.]143) we also directly observed an overlap in filenames for the locally aggregated configuration data. Moreover, the gzip mime type identified in multiple customer investigations also corresponds directly to exfiltration activity noted by Mandiant researchers.

Advanced search logs noting the filename and URL of the posted data to one of the exfiltration IPs. The .tm filename corresponds to the locally stored file on affected FortiManager devices analyzed by external researchers.
Figure 6: Advanced search logs noting the filename and URL of the posted data to one of the exfiltration IPs. The .tm filename corresponds to the locally stored file on affected FortiManager devices analyzed by external researchers.

Activity detected in June 2024

Common indicators

Analysts identified a similar pattern of activity between June 23 and June 25. Activity in this period involved incoming connections from the aforementioned IP 45.32.41[.]202 on either port 541 or port 443 followed by an outgoing connection to the source. This behavior was then followed by HTTP POSTs to the previously mentioned IP address 158.247.199[.]37 in addition to the novel IP: 195.85.114[.]78  using same URI ‘/file’ noted above. Given the commonalties in indicators, time period, and observed behaviors, this grouping of exploitation attempts appears to align closely with the campaign described by Mandiant and may represent exploitation of CVE 2024-47575 in June 2024. The customers targeted in June fall into the same regions and sectors as seen those in the September campaign.

Deviations in behavior

Notably, Darktrace detected a different set of actions during the same June timeframe despite featuring the same infrastructure. This activity involved an initial incoming connection from 158.247.199[.]37 to an internal device on either port 541 or port 443. This was then followed by an outgoing HTTP connection to 158.247.199[.]37 on port 443 with a URI containing varying external IPs. Upon further review, analysts noticed the IPs listed may be the public IPs of the targeted victim, suggesting a potential form device registration by the threat actor or exploit validation. While the time period and infrastructure closely align with the previous campaign described, the difference in activity may suggest another threat actor sharing infrastructure or the same threat actor carrying out a different campaign at the same time. Although the IP 45.32.41[.]202 was contacted, paralleling activity seen in September, analysts did notice a different payload received from the external host, a shell script with the filename ver.sh.

Figure 7: AI Analyst timeline noting the suspicious HTTP behavior from a FortiManager device involving the IP 158.247.199[.] 37.

Darktrace's depth of detection and investigation

Darktrace detected spikes in anomalous behavior from Fortinet devices within the customer base between September 22 and 23, 2024. Following an in-depth investigation into affected accounts and hosts, Darktrace identified a clear pattern where one, or multiple, threat actors leveraged CVEs affecting likely FortiManager devices to execute commands on the host, retrieve malicious content, and exfiltrate sensitive data. During this investigation, analysts then identified possibly related activity in June 2024 highlighted above.

The gathering and exfiltration of configuration data from network security management or other perimeter hosts is a technique that can enable future access by threat actors. This parallels activity previously discussed by Darktrace focused on externally facing devices, such as Palo Alto Networks firewall devices.  Malicious entities could utilize stolen configuration data and potentially stored passwords/hashes to gain initial access in the future, irrespective of the state of device patching. This data can also be potentially sold by initial access brokers on illicit sites. Moreover, groups can leverage this information to establish persistence mechanisms within devices and host networks to enable more impactful compromise activity.

Uncover threat pattens before they strike your network

Network and endpoint management services are essential tools for network administrators and will remain a critical part of IT infrastructure. However, these devices are often configured as internet-facing systems, which can unintentionally expose organizations networks' to attacks. Internet exposure provides malicious groups with novel entry routes into target environments. Although threat actors can swap vulnerabilities to access target networks, the exploitation process leaves behind unusual traffic patterns, making their presence detectable with the right network detection tools.

By detecting the unusual patterns of network traffic which inevitably ensue from exploitation of novel vulnerabilities, Darktrace’s anomaly-based detection and response approach can continue to identify and inhibit such intrusion activities irrespective of exploit used. Eulogizing the principle of least privilege, configuration and asset management, and maintaining the CIA Triad across security operations will continue to help security teams boost their defense posture.

See how anomaly-based detection can enhance your security operations—schedule a personalized demo today.

Get a demo button for Darktrace

Credit to Adam Potter (Senior Cyber Analyst), Emma Foulger (Principal Cyber Analyst), Nahisha Nobregas (Senior Cyber Analyst), Hyeongyung Yeom (Principal Cyber Analyst & Analyst Team Lead, East Asia), Sam Lister (Senior Cyber Analyst)

Appendix

Model Alerts

  • Anomalous Connection / Posting HTTP to IP without Hostname
  • Anomalous Connection / Callback on Web Facing Device
  • Anomalous Server Activity / New Internet Facing Server
  • Anomalous Server Activity / Outgoing from Server

Cyber AI Analyst Incidents

  • Possible HTTP Command and Control
  • Possible HTTP Command and Control to Multiple Endpoints

IoCs

Indicator – Type - Description

104.238.141[.]143 -  IP Address  - C2 infrastructure

158.247.199[.]37 - IP Address - C2 infrastructure

45.32.41[.]202 - IP Address - C2 infrastructure

104.238.141[.]143/file – URL - C2 infrastructure

158.247.199[.]37/file  - URL - C2 infrastructure

45.32.41[.]202/dom.js – URL - C2 infrastructure

.tm – Filename - Gzip file

MITRE Attack Framework

  • Initial Access
    T1190 Exploiting Public-Facing Application
  • Execution:
    T1059 Command and Scripting Interpreter  (Sub-Techniques: T1059.004 Unix Shell, T1059.008 Network Device CLI)
  • Discovery:
    T1083 File and System Discovery
    T1057 Process Discovery
  • Collection:
    T1005 Data From Local System
  • Command and Control:
    T1071 Application Layer Protocols (Sub-Technique:
    T1071.001 Web Protocols)
    T1573  Encrypted Channel
    T1573.001  Symmetric Cryptography
    T1571 Non-Standard Port
    T1105 Ingress Tool Transfer
    T1572 Protocol Tunnelling 
  • Exfiltration:
    T1048.003 Exfiltration Over Unencrypted Non-C2 Protocol

References

{1} https://cloud.google.com/blog/topics/threat-intelligence/fortimanager-zero-day-exploitation-cve-2024-47575/

{2} https://docs.fortinet.com/document/fortimanager/6.4.0/ports-and-protocols/606094/fortigate-fortimanager-protocol#:~:text=The%20FortiGate%2DFortiManager%20(FGFM),by%20using%20the%20FGFM%20protocol.

{3)https://docs.fortinet.com/document/fortigate/6.4.0/ports-and-protocols/373486/fgfm-fortigate-to-fortimanager-protocol
{4} https://www.fortiguard.com/psirt/FG-IR-24-029
{5} https://www.fortiguard.com/psirt/FG-IR-24-423
{6}https://www.fortinet.com/content/dam/fortinet/assets/data-sheets/fortimanager.pdf

{7} https://doublepulsar.com/burning-zero-days-fortijump-fortimanager-vulnerability-used-by-nation-state-in-espionage-via-msps-c79abec59773

{8} https://darktrace.com/blog/post-exploitation-activities-on-pan-os-devices-a-network-based-analysis

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 Potter
Senior Cyber Analyst

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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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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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