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September 21, 2022

Modern Extortion: Detecting Data Theft From the Cloud

Darktrace highlights a handful of data theft incidents on shared cloud platforms, showing that cloud computing can be a vulnerable place for modern extortion.
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
Adrianne Marques
Senior Research Analyst
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21
Sep 2022

Ransomware Industry

The ransomware industry has benefitted from a number of factors in recent years: inadequate cyber defenses, poorly regulated cryptocurrency markets, and geopolitical tensions have allowed gangs to extort increasingly large ransoms while remaining sheltered from western law enforcement [1]. However, one of the biggest success stories of the ransomware industry has been the adaptability and evolution of attacker TTPs (tactics, techniques and procedures). The WannaCry and NotPetya attacks of 2017 popularized a form of ransomware which used encryption algorithms to hold data to ransom in exchange for a decryption key. Last year in 2021, almost all ransomware strains evolved to use double extortion tactics: holding stolen data to ransom as well as encrypted data [2]. Now, some ransomware gangs have dropped encryption entirely, and are using data theft as their sole means of extortion. 

Using data theft for extortion is not new. In 2020 the Finnish psychotherapy center Vastaamo had over 40,000 patient records stolen. Impacted patients were told that their psychiatric transcripts would be published online if they failed to pay a Bitcoin ransom. [3]. A later report by BlackFog in May 2021 predicted data theft extortion would become one of the key emerging cybersecurity trends that year [4]. Adoption of offline back-ups and endpoint detection had made encryption harder, while a large-scale move to Cloud and SaaS platforms offered new vectors for data theft. By moving from data encryption to data exfiltration, ransomware attackers pivoted from targeting data availability within the CIA triad (Confidentiality, Integrity, Availability) to threatening data confidentiality.

In November 2021, Darktrace detected a data theft incident following the compromise of two SaaS accounts within an American tech customer’s Office365 environment. The client was a longstanding user of Darktrace DETECT/Network, and was in the process of expanding their coverage by trialing Darktrace DETECT+RESPOND/ Apps + Cloud.

Attack Overview

On November 23rd 2021, an Ask the Expert (ATE) ticket was raised prompting investigation into a breached SaaS model, ‘SaaS / Access / Unusual External Source for SaaS Credential Use’, and the activities of a user (censored as UserA) over the prior week.

1. Office365: UserA 

The account UserA had been logging in from an unusual location in Nigeria on November 21st. At the time of the incident there were no flags of malicious activity from this IP in widely used OSINT sources. It is also highly probable the attacker was not located in Nigeria but using Nigerian infrastructure in order to hide their true location. Regardless, the location of the login from this IP and ASN was considered highly unusual for users within the customer’s digital estate. The specific user in question most commonly accessed their account from IP ranges located in the US.

Figure 1: In the Geolocation tab of the External Sites Summary on the SaaS Console, UserA was seen logging in from Nigeria when previous logins were exclusively from USA

Further investigation revealed an additional anomaly in the Outlook Web activity of UserA. The account was using the Firefox browser to access their account for the first time in at least 4 weeks (the maximum period for which the customer stored such data). SaaS logs detailing the access of confidential folders and other suspicious actions were identified using the Advanced Search (AS) query:

@fields.saas_actor:"UserA@[REDACTED]" AND @fields.saas_software:"Firefox"

Most actions were ‘MailItemsAccessed’ events originating from IPs located in Nigeria [5,6] and one other potentially malicious IP located in the US [7].

‘MailItemsAccessed’ is part of the new Advanced Audit functionality from Microsoft and can be used to determine when email data is accessed by mail protocols and clients. A bind mail access type denotes an individual access to an email message [8]. 

Figure 2: AS logs shows UserA had not used Firefox to access Office365 for at least 4 weeks prior to the unusual login on the 21st November

Below are details of the main suspicious SaaS activities: 

·      Time: 2021-11-21 09:05:25 - 2021-11-22 16:57:39 UTC

·      SaaS Actor: UserA@[REDACTED]

·      SaaS Service: Office365

·      SaaS Service Product: Exchange

·      SaaS Software: Firefox

·      SaaS Office365 Parent Folders:

          o   \Accounts/Passwords
          o   \Invoices
          o   \Sent Items
          o   \Inbox
          o   \Recoverable Items\Deletions

·      SaaS Event:

          o   MailItemsAccessed
          o   UserLoggedIn
          o   Update

·      SaaS Office365 Mail Access Type: Bind (47 times)

·      Source IP addresses:

          o   105.112.59[.]83
          o   105.112.36[.]212
          o   154.6.17[.]16
          o   45.130.83[.]129

·      SaaS User Agents: 

          o   Client=OWA;Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:80.0) Gecko/20100101 Firefox/80.0;
          o   Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:80.0) Gecko/20100101 Firefox/80.0

·      Total SaaS logs: 57 

At the start of the month on the 5th November, the user had also been seen logging in from a potentially malicious endpoint [9] in Europe, performing ‘MailItemsAccessed’ and ‘Updates’ events with subjects and a resource location related to invoices and wire transfers from the Sent items folder. This suggests the initial compromise had been earlier in the month, giving the threat actor time to make preparations for the final stages of the attack.

Figure 3: Event log showing the activity of UserA from IP 45.135.187[.]108 

2. Office365: UserB 

Looking into the model breach ‘SaaS / Access / Suspicious Credential Use And Login User-Agent’, it was seen that a second account, UserB, was also observed logging in from a rare and potentially malicious location in Bangladesh [7]. Similar to UserA, this user had previously logged in exclusively from the USA, and no other accounts within the digital estate had been observed interacting with the Bangladeshi IP address. The login event appeared to bypass MFA (Multi-factor Authentication) and a suspicious user agent, BAV2ROPC, was used. Against misconfigured accounts, this Microsoft user agent is commonly used by attackers to bypass MFA on Office365. It targets Exchange’s Basic Authentication (normally used in POP3/IMAP4 conditions) and results in an OAuth flow which circumvents the additional password security brought by MFA [10].  

During the session, additional resources were accessed which appear to be associated with bill and invoice payments. In addition, on the 4th November, two new suspicious email rules named “..” were created from rare IPs (107.10.56[.]48 and 76.189.202[.]66). This type of behavior is commonly seen during SaaS compromises to delete or forward emails. Typically, an email rule created by a human user will be named to reflect the change being made, such as ‘Move emails from Legal to Urgent’. In contrast, malicious email rules are often short and undescriptive. The rule “..” is likely to blend in without arousing suspicion, while also being easy for the attacker to create and remember. 

Details of these rule changes are as follows:

·      Time: 2021-11-04 13:25:06, 2021-11-05 15:50:00 [UTC]
·      SaaS Service: Office365
·      SaaS Service Product: Exchange
·      SaaS Status Message: True
·      SaaS Source IP addresses: 107.10.56[.]48, 76.189.202[.]66
·      SaaS Account Name: O365
·      SaaS Actor: UserB@[REDACTED]
·      SaaS Event: SetInboxRule
·      SaaS Office365 Modified Property Names:
          o   AlwaysDeleteOutlookRulesBlob, Force, Identity, MoveToFolder, Name, FromAddressContainsWords, StopProcessingRules
          o   AlwaysDeleteOutlookRulesBlob, Force, Identity, Name, FromAddressContainsWords, StopProcessingRules
·      SaaS Resource Name: .. 

During cloud account compromises, attackers will often use sync operations to download emails to their local email client. During the operations, these clients typically download a large set of mail items from the cloud to a local computer. If the attacker is able to sync all mail items to their mail client, the entire mailbox can be compromised. The attacker is able to disconnect from the account and review and search the email without generating additional event logs. 

Both accounts UserA and UserB were observed using ‘MailItemsAccessed’ sync operations between the 1st and 23rd November when this attack occurred. However, based on the originating IP of the sync operations, the activity is likely to have been initiated by the legitimate, US-based users. Once the security team were able to confirm the events were expected and legitimate, they could establish that the contents of the mailbox were not a part of the data breach. 

Accomplish Mission

After gaining access to the Office365 accounts, sensitive data was downloaded by the attackers to their local system. Either on or before 14th December, the attacker had seemingly uploaded the documents onto a data leak website. In total, 130MB of data had been made available for download in two separate packages. The packages included audit and accounting financial documents, with file extensions such as DB, XLSX, and PDF.

Figure 4: The two data packages uploaded by the attacker and the extracted contents

In a sample of past SaaS activity of UserA, the subject and attachments appear related to the ‘OUTSTANDING PREPAY WIRES 2021’ excel document found from the data leak website in Figure 4, suggesting a further possibility that the account was associated with the leaked data. 

Historic SaaS activity associated with UserA: 

·      Time: 2021-11-05 21:21:18 [UTC]
·      SaaS Office365 Logon Type: Owner
·      Protocol: OFFICE365
·      SaaS Account Name: O365
·      SaaS Actor: UserA@[REDACTED].com
·      SaaS Event: Send
·      SaaS Service: Office365
·      SaaS Service Product: Exchange
·      SaaS Status Message: Succeeded
·      SaaS Office365 Attachment: WIRE 2021.xlsx (92406b); image.png (9084b); image.png (1454b); image.png (1648b); image.png (1691b); image.png (1909b); image.png (2094b)
·      SaaS Office365 Subject: Wires 11/8/21
·      SaaS Resource Location: \Drafts
·      SaaS User Agent: Client=OWA;Action=ViaProxy 

Based on the available evidence, it is highly likely that the data packages contain the data stolen during the account compromise the previous month.  

Once the credentials of an Office365 account are stolen, an attacker can not only access the user's mailbox, but also a full range of Office365 applications such as SharePoint folders, Teams Chat, or files in the user's OneDrive [11]. For example, files shared in Teams chat are stored in OneDrive for Business in a folder named Microsoft Teams Chat Files in the default Document library on SharePoint. One of the files visible on the data leak website, called ‘[REDACTED] CONTRACT.3.1.2020.pdf’, was also observed in the default document folder of a third user account (UserC) within the victim organization, suggesting the compromised accounts may have been able to access shared files stored on other accounts by moving laterally via other O365 applications such as Teams. 

One example can be seen in the below AS logs: 

·      Time: 2021-11-11 01:58:35 [UTC]
·      SaaS Resource Type: File
·      Protocol: OFFICE365
·      SaaS Account Name: 0365
·      SaaS Actor: UserC@[REDACTED]
·      SaaS Event: FilePreviewed
·      SaaS Service Product: OneDrive
·      SaaS Metric: ResourceViewed
·      SaaS Office365 Application Name: Media Analysis and Transformation Service
·      SaaS Office365 File Extension: pdf
·      SaaS Resource Location: https://[REDACTED]-my.sharepoint.com/personal/userC_[REDACTED]_com/Documents/Microsoft Teams Chat Files/[REDACTED] CONTRACT 3.1.2020.pdf
·      SaaS Resource Name: [REDACTED] CONTRACT 3.1.2020.pdf
·      SaaS Service: Office365
·      SaaS Service Product: OneDrive
·      SaaS User Agent: OneDriveMpc-Transform_Thumbnail/1.0 

In the period between the 1st and 30th November, the customer’s Darktrace DETECT/Apps trial had raised multiple high-level alerts associated with SaaS account compromise, but there was no evidence of file encryption.  

Establish Foothold 

Looking back at the start of the attack, it is unclear exactly how the attacker evaded the customer’s pre-existing security stack. At the time of the incident, the victim was using a Barracuda email gateway and Microsoft 365 Threat Management for their cloud environment. 

Darktrace detected no indication the accounts were compromised via credential bruteforcing, which would have enabled the attacker to bypass the Azure Active Directory smart lockout (if enabled). The credentials may have been harvested via a phishing campaign which successfully evaded the list of known ‘bad’ domains maintained by their email gateway.  

Upon gaining access to the account, the Microsoft Defender for Cloud Apps anomaly detection policies would have been expected to raise an alert [12]. In this instance, the unusual login from Nigeria occurred over 16 hours after the previous login from the US, potentially evading anomaly detection policies such as the ‘Impossible Travel’ rule. 

Figure 5: Event log showing the user accessing mail from USA a day before the suspicious usage from Nigeria 

Darktrace Coverage

Darktrace DETECT 

Throughout this event, high scoring model breaches associated with the attack were visible in the customer’s SaaS Console. In addition, there were two Cyber AI Analyst incidents for ‘Possible Account Hijack’ associated with the two compromised SaaS Office365 accounts, UserA and UserB. The visibility given by Darktrace DETECT also enabled the security team to confirm which files had been accessed and were likely part of the data leak.

Figure 6: Example Cyber AI Analyst incident of UserB SaaS Office365 account

Darktrace RESPOND

In this incident, the attackers successfully compromised O365 accounts in order to exfiltrate customer data. Whilst Darktrace RESPOND/Apps was being trialed and suggested several actions, it was configured in human confirmation mode. The following RESPOND/Apps actions were advised for these activities:  

·      ‘Antigena [RESPOND] Unusual Access Block’ triggered by the successful login from an unusual IP address, would have actioned the ‘Block IP’ inhibitor, preventing access to the account from the unusual IP for up to 24 hours
·      ‘Suspicious Source Activity Block’, triggered by the suspicious user agent used to bypass MFA, would have actioned the ‘Disable User’ inhibitor, disabling the user account for up to 24 hours 

During this incident, Darktrace RESPOND/Network was being used in fully autonomous mode in order to prevent the threat actor from pivoting into the network. The security team were unable to conclusively say if any attempts by the attacker to do this had been made. 

Concluding Thoughts  

Data theft extortion has become a widely used attack technique, and ransomware gangs may increasingly use this technique alone to target organizations without secure data encryption and storage policies.  

This case study describes a SaaS data theft extortion incident which bypassed MFA and existing security tools. The attacker appeared to compromise credentials without bruteforce activity, possibly with the use of social engineering through phishing. However, from the first new login, Darktrace DETECT identified the unusual credential use in spite of it being an existing account. Had Darktrace RESPOND/Apps been configured, it would have autonomously responded to halt this login and prevent the attacker from accomplishing their data theft mission.

Thanks to Oakley Cox, Brianna Leddy and Shuh Chin Goh for their contributions.

Appendices

References 

[1] https://securelist.com/new-ransomware-trends-in-2022/106457/

[2] https://www.itpro.co.uk/security/ransomware/367624/the-rise-of-double-extortion-ransomware

[3] https://www.malwarebytes.com/blog/news/2020/10/vastaamo-psychotherapy-data-breach-sees-the-most-vulnerable-victims-extorted

[4] https://www.blackfog.com/shift-from-ransomware-to-data-theft-extortion/

[5] https://www.abuseipdb.com/check/105.112.59.83

[6] https://www.abuseipdb.com/check/105.112.36.212

[7] https://www.abuseipdb.com/check/45.130.83.129

[8] https://docs.microsoft.com/en-us/microsoft-365/compliance/mailitemsaccessed-forensics-investigations?view=o365-worldwide

[9] https://www.abuseipdb.com/check/45.135.187.108

[10] https://www.virustotal.com/gui/ip-address/45.137.20.65/details

[11] https://tidorg.com/new-bec-phishing-attack-steals-office-365-credentials-and-bypasses-mfa/

[12] https://docs.microsoft.com/en-us/microsoft-365/security/office-365-security/responding-to-a-compromised-email-account?view=o365-worldwide

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
Adrianne Marques
Senior Research 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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