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February 24, 2025

Detecting and Containing Account Takeover with Darktrace

Account takeovers are rising with SaaS adoption. Learn how Darktrace detects deviations in user behavior and autonomously stops threats before they escalate.
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
Min Kim
Cyber Security Analyst
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24
Feb 2025

Thanks to its accessibility from anywhere with an internet connection and a web browser, Software-as-a-Service (SaaS) platforms have become nearly universal across organizations worldwide. However, with this growing popularity comes greater responsibility. Increased attention attracts a larger audience, including those who may seek to exploit these widely used services. One crucial factor to be vigilant about in the SaaS landscape is safeguarding internal credentials. Minimal protection on accounts can lead to SaaS hijacking, which could allow further escalations within the network.

How does SaaS account takeover work?

SaaS hijacking occurs when a malicious actor takes control of a user’s active session with a SaaS application. Attackers can achieve this through various methods, including employees using company credentials on compromised or spoofed external websites, brute-force attacks, social engineering, and exploiting outdated software or applications.

After the hijack, attackers may escalate their actions by changing email rules and using internal addresses for additional social engineering attacks. The larger goal of these actions is often to steal internal data, damage reputations, and disrupt operations.

Account takeover protection

It has become essential to have security tools capable of outsmarting potential malicious actors. Traditional tools that rely on rules and signatures may not be able to identify new events, such as logins or activities from a rare endpoint, unless they come from a known malicious source.

Darktrace relies on analysis of user and network behavior, tailored to each customer, allowing it to identify anomalous events that the user typically does not engage in. In this way, unusual SaaS activities can be detected, and unwanted actions can be halted to allow time for remediation before further escalations.

The following cases, drawn from the global customer base, illustrate how Darktrace detects potential SaaS hijack attempts and further escalations, and applies appropriate actions when necessary.

Case 1: Unusual login after a phishing email

A customer in the US received a suspicious email that seemed to be from the legitimate file storage service, Dropbox. However, Darktrace identified that the reply-to email address, hremployeepyaroll@mail[.]com, was masquerading as one associated with the customer’s Human Resources (HR) department.

Further inspection of this sender address revealed that the attacker had intentionally misspelled ‘payroll’ to trick recipients into believing it was legitimate

Furthermore, the subject of the email indicated that the attackers were attempting a social engineering attack by sharing a file related to pay raises and benefits to capture the recipients' attention and increase the likelihood of their targets engaging with the email and its attachment.

Figure 1: Subject of the phishing email.
Figure 1: Subject of the phishing email.

Unknowingly, the recipient, who believed the email to be a legitimate HR communication, acted on it, allowing malicious attackers to gain access to the account. Following this, the recipient’s account was observed logging in from a rare location using multi-factor authentication (MFA) while also being active from another more commonly observed location, indicating that the SaaS account had been compromised.

Darktrace’s Autonomous Response action triggered by an anomalous email received by an internal user, followed by a failed login attempt from a rare external source.
Figure 2: Darktrace’s Autonomous Response action triggered by an anomalous email received by an internal user, followed by a failed login attempt from a rare external source.

Darktrace subsequently observed the SaaS actor creating new inbox rules on the account. These rules were intended to mark as read and move any emails mentioning the file storage company, whether in the subject or body, to the ‘Conversation History’ folder. This was likely an attempt by the threat actor to hide any outgoing phishing emails or related correspondence from the legitimate account user, as the ‘Conversation History’ folder typically goes unread by most users.

Typically, Darktrace / EMAIL would have instantly placed the phishing email in the junk folder before they reached user’s inbox, while also locking the links identified in the suspicious email, preventing them from being accessed. Due to specific configurations within the customer’s deployment, this did not happen, and the email remained accessible to the user.

Case 2: Login using unusual credentials followed by password change

In the latter half of 2024, Darktrace detected an unusual use of credentials when a SaaS actor attempted to sign into a customer’s Microsoft 365 application from an unfamiliar IP address in the US. Darktrace recognized that since the customer was located within the Europe, Middle East, and Africa (EMEA) region, a login from the US was unexpected and suspicious. Around the same time, the legitimate account owner logged into the customer’s SaaS environment from another location – this time from a South African IP, which was commonly seen within the environment and used by other internal SaaS accounts.

Darktrace understood that this activity was highly suspicious and unlikely to be legitimate, given one of the IPs was known and expected, while the other had never been seen before in the environment, and the simultaneous logins from two distant locations were geographically impossible.

Model alert in Darktrace / IDENTITY: Detecting a login from a different source while the user is already active from another source.
Figure 3: Model alert in Darktrace / IDENTITY: Detecting a login from a different source while the user is already active from another source.

Darktrace detected several unusual login attempts, including a successful login from an uncommon US source. Subsequently, Darktrace / NETWORK identified the device associated with this user making external connections to rare endpoints, some of which were only two weeks old. As this customer had integrated Darktrace with Microsoft Defender, the Darktrace detection was enriched by Defender, adding the additional context that the user had likely been compromised in an Adversary-in-the-Middle (AiTM) phishing attack. AiTM phishing attacks occur when a malicious attacker intercepts communications between a user and a legitimate authentication service, potentially leading to account hijacking. These attacks are harder to identify as they can bypass security measures like MFA.

Following this, Darktrace observed the attacker using the now compromised credentials to access password management and change the account's password. Such behavior is common in account takeover incidents, as attackers seek to maintain persistence within the SaaS environment.

While Darktrace’s Autonomous Response was not fully configured on the customer’s SaaS environment, they were subscribed to the Managed Threat Detection service offered by Darktrace’s Security Operations Center (SOC). This 24/7 service ensures that Darktrace’s analysts monitor and investigate emerging suspicious activity, informing customers in real-time. As such, the customer received notification of the compromise and were able to quickly take action to prevent further escalation.

Case 3: Unusual logins, new email rules and outbound spam

Recently, Darktrace has observed a trend in SaaS compromises involving unusual logins, followed by the creation of new email rules, and then outbound spam or phishing campaigns being launched from these accounts.

In October, Darktrace identified a SaaS user receiving an email with the subject line "Re: COMPANY NAME Request for Documents" from an unknown sender using a freemail  account. As freemail addresses require very little personal information to create, threat actors can easily create multiple accounts for malicious purposes while retaining their anonymity.

Within the identified email, Darktrace found file storage links that were likely intended to divert recipients to fraudulent or malicious websites upon interaction. A few minutes after the email was received, the recipient was seen logging in from three different sources located in the US, UK, and the Philippines, all around a similar time. As the customer was based in the Philippines, a login from there was expected and not unusual. However, Darktrace understood that the logins from the UK and US were highly unusual, and no other SaaS accounts had connected from these locations within the same week.

After successfully logging in from the UK, the actor was observed updating a mailbox rule, renaming it to ‘.’ and changing its parameters to move any inbound emails to the deleted items folder and mark them as read.

Figure 4: The updated email rule intended to move any inbound emails to the deleted items folder.

Malicious actors often use ambiguous names like punctuation marks, repetitive letters, and unreadable words to name resources, disguising their rules to avoid detection by legitimate users or administrators. Similarly, attackers have been known to adjust existing rule parameters rather than creating new rules to keep their footprints untracked. In this case, the rule was updated to override an existing email rule and delete all incoming emails. This ensured that any inbound emails, including responses to potential phishing emails sent by the account, would be deleted, allowing the attacker to remain undetected.

Over the next two days, additional login attempts, both successful and failed, were observed from locations in the UK and the Philippines. Darktrace noted multiple logins from the Philippines where the legitimate user was attempting to access their account using a password that had recently expired or been changed, indicating that the attacker had altered the user’s original password as well.

Following this chain of events, over 500 emails titled “Reminder For Document Signed Agreement.10/28/2024” were sent from the SaaS actor’s account to external recipients, all belonging to a different organization within the Philippines.

These emails contained rare attachments with a ‘.htm’ extension, which included programming language that could initiate harmful processes on devices. While inherently not malicious, if used inappropriately, these files could perform unwanted actions such as code execution, malware downloads, redirects to malicious webpages, or phishing upon opening.

Outbound spam seen from the hijacked SaaS account containing a ‘.htm’ attachment.
Figure 5: Outbound spam seen from the hijacked SaaS account containing a ‘.htm’ attachment.

As this customer did not have Autonomous Response enabled for Darktrace / IDENTITY, the unusual activity went unattended, and the compromise was able to escalate to the point of a spam email campaign being launched from the account.

In a similar example on a customer network in EMEA, Darktrace detected unusual logins and the creation of new email rules from a foreign location through a SaaS account. However, in this instance, Autonomous Response was enabled and automatically disabled the compromised account, preventing further malicious activity and giving the customer valuable time to implement their own remediation measures.

Conclusion

Whether it is an unexpected login or an unusual sequence of events – such as a login followed by a phishing email being sent – unauthorized or unexpected activities can pose a significant risk to an organization’s SaaS environment. The threat becomes even greater when these activities escalate to account hijacking, with the compromised account potentially providing attackers access to sensitive corporate data. Organizations, therefore, must have robust SaaS security measures in place to prevent data theft, ensure compliance and maintain continuity and trust.

The Darktrace suite of products is well placed to detect and contain SaaS hijack attempts at multiple stages of an attack. Darktrace / EMAIL identifies initial phishing emails that attackers use to gain access to customer SaaS environments, while Darktrace / IDENTITY detects anomalous SaaS behavior on user accounts which could indicate they have been taken over by a malicious actor.

By identifying these threats in a timely manner and taking proactive mitigative measures, such as logging or disabling compromised accounts, Darktrace prevents escalation and ensures customers have sufficient time to response effectively.

Credit to Min Kim (Cyber Analyst) and Ryan Traill (Analyst Content Lead)

[related-resource]

Appendices

Darktrace Model Detections Case 1

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

SaaS / Compromise / Unusual Login and New Email Rule

SaaS / Compliance / Anomalous New Email Rule

SaaS / Unusual Activity / Multiple Unusual SaaS Activities

SaaS / Access / Unusual External Source for SaaS Credential Us

SaaS / Compromise / Login From Rare Endpoint While User is Active

SaaS / Email Nexus / Unusual Login Location Following Link to File Storage

Antigena / SaaS / Antigena Email Rule Block (Autonomous Response)

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (Autonomous Response)

Antigena / SaaS / Antigena Enhanced Monitoring from SaaS User Block (Autonomous Response)

List of Indicators of Compromise (IoCs)

176.105.224[.]132 – IP address – Unusual SaaS Activity Source

hremployeepyaroll@mail[.]com – Email address – Reply-to email address

MITRE ATT&CK Mapping

Cloud Accounts – DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS – T1078

Outlook Rules – PERSISTENCE – T1137

Cloud Service Dashboard – DISCOVERY – T1538

Compromise Accounts – RESOURCE DEVELOPMENT – T1586

Steal Web Session Cookie – CREDENTIAL ACCESS – T1539

Darktrace Model Detections Case 2

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

SaaS / Compromise / Unusual Login and Account Update

Security Integration / High Severity Integration Detection

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Compromise / Login From Rare Endpoint While User Is Active

SaaS / Compromise / Login from Rare High Risk Endpoint

SaaS / Access / M365 High Risk Level Login

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (Autonomous Response)

Antigena / SaaS / Antigena Enhanced Monitoring from SaaS user Block (Autonomous Response)

List of IoCs

74.207.252[.]129 – IP Address – Suspicious SaaS Activity Source

MITRE ATT&CK Mapping

Cloud Accounts – DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS – T1078

Cloud Service Dashboard – DISCOVERY – T1538

Compromise Accounts – RESOURCE DEVELOPMENT – T1586

Steal Web Session Cookie – CREDENTIAL ACCESS – T1539

Darktrace Model Detections Case 3

SaaS / Compromise / Unusual Login and Outbound Email Spam

SaaS / Compromise / New Email Rule and Unusual Email Activity

SaaS / Compromise / Unusual Login and New Email Rule

SaaS / Email Nexus / Unusual Login Location Following Sender Spoof

SaaS / Email Nexus / Unusual Login Location Following Link to File Storage

SaaS / Email Nexus / Possible Outbound Email Spam

SaaS / Unusual Activity / Multiple Unusual SaaS Activities

SaaS / Email Nexus / Suspicious Internal Exchange Activity

SaaS / Compliance / Anomalous New Email Rule

List of IoCs

95.142.116[.]1 – IP Address – Suspicious SaaS Activity Source

154.12.242[.]58 – IP Address – Unusual Source

MITRE ATT&CK Mapping

Cloud Accounts – DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS – T1078

Compromise Accounts – RESOURCE DEVELOPMENT – T1586

Email Accounts – RESOURCE DEVELOPMENT – T1585

Phishing – INITIAL ACCESS – T1566

Outlook Rules – PERSISTENCE – T1137

Internal Spear phishing – LATERAL MOVEMENT - T1534

Get the latest insights on emerging cyber threats

This report explores the latest trends shaping the cybersecurity landscape and what defenders need to know in 2025.

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
Min Kim
Cyber Security Analyst

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July 17, 2026

AI Is Taking on Stadium Operations. How Can Security Teams Keep it Protected?

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How to Secure AI in Stadium Operations

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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July 15, 2026

Security After Signatures: Operating in a World of Pre‑CVE Disclosure Exploitation, Collapsed Trust Boundaries, and Autonomous Systems

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Three shifts have reshaped what it means to defend an enterprise securely.  

First, exploitation often begins before defenders have a Common Vulnerabilities and Exposures (CVE) identifier, a security advisory, or an entry in the Cybersecurity and Infrastructure Security Agency's (CISA) Known Exploited Vulnerabilities (KEV) catalog.

Secondly, the trust boundary has moved beyond the network edge into identities, tokens, APIs, and Software-as-a-Service (SaaS) workflows.  

Third, an increasing share of business activity is executed through automation, integrations, and AI agent-like systems that can act faster than teams can verify intent.  

If your security model still relies on detecting known bad artefacts, triaging isolated alerts, and waiting for confirmation before acting, you are already behind the threat.  

This is not a failure of security teams; it’s a failure of the operating model to keep pace with how the environment has changed.

A SOC built around alerts and signatures assumes that malicious activity will eventually surface as an event. In real incidents, however, the decisive evidence is rarely a single event. Instead, it is a chain of individually explainable actions that only appears malicious once you connect the dots across identity, non-human identity, cloud, email, SaaS, operational technology (OT), and network telemetry.

The defenders succeeding today observe behaviors, link them into sequences, understand what those sequences mean, and contain impact before the full story unfolds. That is the operating model the current threat environment demands.  

Exploitation before disclosure

The first shift is the straightforward: the time to exploit has dropped to nearly zero.  

In one example, Darktrace observed a sequence of subtle but strategically significant anomalies within a customer environment that later aligned with exploitation of CVE‑2025‑0994 in Trimble Cityworks by likely Chinese-nexus threat actors. Behavioral indicators were visible at least 18 days before public disclosure, with related anomalies emerging 40 to 50 days earlier during the intrusion window.  

This case illustrates a familiar pattern: clusters of weak‑signal anomalies combing to form an actionable picture of intrusion long before a CVE is published. Such activity reflects long‑horizon, option‑preserving operator models often associated with mature state‑linked activity.  

Figure 1: Darktrace’s detection of malicious exploitation of CVE 2025-0994, later tied to Chinese-nexus threat actors targeting critical national infrastructure (CNI) in the US, weeks before public disclosure.

Throughout 2025 and 2026, Darktrace has continued to observe the value of anomaly-based detections across a range of incidents.

CVE CVE Public Disclosure Date Darktrace Detection Date Days Between Detection of Exploitation and CVE Public Disclosure
CVE 2025 0994
(Trimble City Works)
2025-02-06 2025-01-19 18 Days
CVE 2025-24183
(Apache)
2025-03-10 2025-02-18 20 days
CVE 2025-10035
(Fortra GoAnywhere)
2025-09-18 2025-09-11 7 days

Identity is the real control plane

The second shift is that identity has replaced perimeter as the primary control plane. As Darktrace’s Annual Threat Report 2026 illustrated, identity remains the main challenge in defending against modern intrusions. A clear example is the Adversary-in-the-Middle (AiTM) case published by Darktrace in December 2025. A phishing email led to the compromise of an Office 365 account. Session hijacking bypassed multi-factor authentication (MFA), and the compromised account was used for follow-on phishing and persistence activities including the creation of malicious email rules.  

Every step in that sequence mattered. A successful login alone does not prove legitimacy. An inbox rule, on its own, may not appear catastrophic. Mail activity, viewed in isolation, may seem operationally normal. But the behavioral chain tells a different story: credential theft, token abuse, persistence, and onward compromise through a trusted identity.  

This is why the question is no longer “Did the user authenticate successfully”. The more important question is, “Does this identity action make sense right now, in this context, given what came before it?” The AiTM case shows how identity can be compromised. In practice, however, attacks rarely remained confined to identity alone.  

In another Darktrace case, a compromised SaaS account triggered activity across the email, SaaS, and network layers, including inbox rule changes, phishing propagation, and connections to suspicious infrastructure. Viewed in isolation, none of these events were decisive. Together, however,  they formed a behavioral sequence that revealed the intrusion, with the full attack story automatically correlated and surfaced to defenders by Darktrace’s Cyber AI Analyst.  

Figure 2: Cyber AI Analyst correlated and appended additional events to the incident, including other users who connected to the suspicious redirect link after outbound phishing emails were sent.

AI accelerates the threat  

The third shift is the one many teams still underestimate: trusted tooling, integrations, and AI agent-like systems can create actions that appear legitimate but are strategically dangerous.  

The shift becomes clearer when examining how governments are now framing AI risk. In 2026, guidance published by CISA, UK’s National Cyber Security Centre (NCSC) and Five Eyes partners warned that agentic systems expand attack surfaces, accumulate privilege, and can behave in ways that are difficult to predict or explain [1]. The advice is simple: assume unexpected behavior and design controls around it.  

The real risk is not AI usage. It is unknown autonomy: systems with credentials, data access, and action paths that can execute workflow steps without sufficient behavioral validation, traceability, or human oversight. Darktrace’s Model Context Protocol (MCP) risk analysis provides a useful framework for understanding this challenge. Over-privileged agents, content injection, and tool abuse become high-consequence risks when connected systems can dynamically retrieve data, execute actions, and communicate externally.  

Whether security teams like it or not, AI is already in the enterprise. It will help drive innovation, but it will also be abused, whether accidentally or maliciously. In each of the cases below, AI either scaled the attacker, built the tooling, or existed within the environment as something to exploit or misuse.

1. AI as an Attack Multiplier

In one campaign targeting Mexican government entities, a single operator used commercial AI platforms to generate exploits, automate reconnaissance, and process large volumes of data, compressing work that would traditionally have required an entire team into a single workflow [2].  

Darktrace is also observing this trend further down the stack. In one case, Darktrace identified AI-generated malware exploiting React2Shell, where an attacker used a Large Language Model (LLM) to produce working exploit code and deploy it at scale.  

[darktrace.com], [darktrace.com]

2. AI as an Attack Surface

Attempted AI exploitation is now appearing within customer environments. In one case involving an automation technology manufacturer, a compromised LLM proxy was seemingly used as a stepping stone to access additional AI services. When that attempt failed, the attacker pivoted to cryptomining.

What is clear is that the AI layer has already become an asset worth probing, exploiting, and pivoting through. It is also clear that defenders benefit from rapidly understanding how these activities connect. In this case, Cyber AI Analyst automatically pieced together the intrusion, while Darktrace’s Managed Threat Detection service alerted to the customer, enabling the activity to be contained before it could progress further.

Figure 3: Cyber AI Analyst's investigation into a compromised LLM proxy that was abused for cryptomining activity.

AI as a trusted but dangerous actor

This does not require a cinematic vision of “rogue AI.” The Salesloft incident provides a more grounded example, where AI and automation operate with legitimate access but served malicious intent. In that case, attackers abused compromised OAuth tokens associated with the Drift AI chat agent to export significant volumes of data from Salesforce environments.  

The activity resembled legitimate API usage and relied on trusted SaaS integrations rather than malware or other obvious signs of intrusion. That is precisely the challenge. Traditional security controls are good at detecting forced entry, but far less effective when a trusted application integration behaves in a way that is technically permitted yet operationally harmful.  

In these scenarios, the security challenge shifts from validating access to validating behavior.

This is what that looks like in practice: AI-linked identities executing legitimate actions that require behavioral validation rather than access validation.

Figure 4: Darktrace / SECURE AI highlights anomalous activity across AI identities, surfacing critical behavior that requires validation and containment.

Early observations from Darktrace / SECURE AI deployments reinforce this reality. Across Darktrace's observed fleet, AI service connections per deployment increased 13% during the first half of 2026, reaching over 16 million connections overall. The typical organisation now interacts with seven different AI providers, evidence that AI is no longer operating at the edges of the enterprise. It is increasingly woven into day-to-day business activity.

The most common risks are not compromised models or advanced AI attacks. Instead, they stem from employees and business functions exposing sensitive information through entirely legitimate-looking interactions. Darktrace has observed repeated submission of personally identifiable information (PII), tax information, identification documents, and medical data into LLM prompts, alongside widespread use of unsanctioned (shadow) AI services and growing AI activity from mobile devices.  

For defenders, the challenge is increasingly one of context: understanding when legitimate business use crosses into material risk, while preserving privacy and user trust.

Conclusion

Across all three shifts, the pattern is the same: behavior precedes understanding. Security teams are not losing because adversaries have become invisible. An increasingly outdated security model assumes that malicious activity will reveal itself cleanly and early. It no longer does.  

In 2026 and beyond, defenders win by understanding behavioral sequences, continuously validating trust, and acting before certainty becomes hindsight. That is security after signatures. That is security in the AI era.

Credit to: Daniel Levy, Threat Hunting Data Scientist

Edited by: Ryan Traill, Content Manager

References

[1] https://www.cyber.gov.au/business-government/secure-design/artificial-intelligence/careful-adoption-of-agentic-ai-services  

[2]https://www.latimes.com/business/story/2026-02-26/hacker-used-anthropics-claude-ai-to-steal-mexican-government-data

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
Your data. Our AI.
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