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January 5, 2021

How McLaren Racing Counters Advanced Email Threats

Learn how Darktrace helps McLaren Racing counter advanced email threats, ensuring robust cybersecurity for their operations.
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Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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05
Jan 2021

Securing our team’s inboxes has long been a challenge at McLaren Racing. Even before COVID-19 hit, our workforce was incredibly dynamic; we’d been used to effectively setting up a remote office at tracks around the world every other weekend for the last 30 years. We were therefore used to people emailing at an extremely high rate while not always being centralised in a single location.

Collaboration is key for this team, with both our partners and key suppliers. Sharing data happens every day and a variety of methods are used – ranging from sensitive car designs to confidential track-side data.

The email attacks targeting our users have advanced considerably in the last year or two, with attackers seeking to solicit fraudulent payments, or trying to access our own intellectual property. Increasingly sophisticated social engineering attempts meant that our users continued to engage with these phishing and spoofing emails, despite having an array of tools and procedures in place to avoid such an eventuality.

Last year we extended Darktrace’s coverage to our inbox, and now have an intelligent AI security solution understanding ‘patterns of life’ for every Microsoft 365 user in order to spot attacks. Darktrace has allowed the security team here to stay ahead of the most advanced email threats, rather than respond retrospectively to attacks that manage to slip through traditional defences.

Training our workforce to spot attacks

Previously we relied on threat intelligence feeds and retrospective security tools that blocked malicious addresses, domains and URLs, but regardless, a small volume of phishing emails would still reach user mailboxes. Typically, these emails would be well-researched and highly contextualised, targeted to the recipient and sometimes indistinguishable from genuine communications. Despite running employee awareness programs, a proportion of these malicious emails would be acted on by users, leading to account compromises and fraud attempts. Our security resources were then consumed with reacting to these incidents rather than proactively improving security at McLaren Racing.

We run cyber awareness weeks, in collaboration with many of our partners, simulating our own phishing campaigns to teach our workforce how to spot attacks. But these education programs have become harder to communicate with the increase of remote working. Employee engagement was always key and that meant a larger resource strain on our security team, who would typically spend a lot of time with our senior stakeholders, helping them identify spoof emails and working with them on putting business processes in place.

This was a long and arduous process, and it’s difficult to expect our employees to spot the increasingly subtle signs of an email attack. With the sophistication of modern email attacks, the research that goes into them, and the level of social engineering in play, phishing attacks do inevitably still get through both humans and rudimentary defences.

Turning to cyber AI

Working with our partner Darktrace, we deployed their email security technology, Darktrace/Email, and worked on the configuration and installation together. We were able to see results in days. The volume of phishing emails reported by users fell substantially, and over time with Autonomous Response, the regular reviews of Darktrace/Email’s actions has led us to discover many phishing campaigns that we were previously unaware of.

Darktrace’s actions are taken in the context of the business, holding back emails only as a last resort (less than 1% in our environment), and catching only the genuinely malicious emails rather than producing a load of false positives. The actions are also targeted and proportionate, varying from moving emails to junk to converting attachments and locking links, giving us the flexibility we need.

With Darktrace/Email constantly learning and stopping advanced email attacks, the pressure has been taken off the rest of the team, who can now spend their time working with the business supporting new initiatives and collaborating on new areas of innovation.

Stopping a targeted credential-grabbing attack targeting the C-suite

As with many organisations, it’s often our C-suite that gets targeted by the most malicious mails, and Darktrace/Email recently detected an email sent to one of our executives, prompting them to sign a financial document. The email appeared to come from DocuSign, and contained a malicious link hidden behind the text ‘Review Document’.

Figure 1: An interactive snapshot of Darktrace/Email’s user interface surfacing the email
Figure 2: A screenshot of the email in question

If the link is clicked on, two types of scenarios usually follow from these kind of email attacks. Either the user is led to a fake (and often very convincing) login page which captures credentials, or the document itself contains a legitimate-looking invoice, but with one crucial element changed – the bank details. Accounts teams and CFOs are targeted with this kind of attack on a regular basis, but in this case, the attackers were after the executive’s credentials.

Had the executive clicked through and attempted to log in, they would unknowingly have been sending their credentials to the attacker, who then could have used this information to gather sensitive data from their inbox or other SaaS accounts, or send additional malicious emails from the account to make further inroads into our organisation.

The email was sent over the Imola GP race weekend, which was a high-pressured 48 hours for the whole team, as we ran in a new format without Friday practice, bringing a new intensity to the race weekend. However, Darktrace/Email was on guard, recognising the sender as a new contact and deeming the link to be suspicious. With suitable concerns over the email, Darktrace’s AI double locked the link and automatically moved the email to the executive’s Junk folder. All without having to alert the on-call cyber security team over the weekend.

With attacks like this coming in every day, relying on McLaren’s workforce to distinguish real from fake will never realistically protect us from every single threat. With credential harvesting and account takeover on the rise, it really felt like a matter of time before just one phishing email was successful and the floodgates were opened. But with Darktrace/Email, we can rest assured that we have a powerful AI solution keeping us safe, on and off the track.

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Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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June 26, 2026

How Darktrace Transformed Cybersecurity at Our Health Center: A CIO’s Perspective

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How Darktrace Transformed Cybersecurity at Our Health Center: A CIO’s Perspective

In my role as CIO, I bring years of experience leading IT for healthcare organizations. I’ve seen firsthand the unique cybersecurity challenges that nonprofit health centers face: limited budgets, small IT teams, and the constant pressure to prioritize patient care over technology investments. Yet, the threat landscape for health is relentless, and the stakes for protecting patient data and ensuring operational continuity have never been higher. It’s a balancing act.

The search for a better solution

Like many nonprofits, organizations I work at start with Microsoft’s security stack. The discounted pricing for nonprofits makes it an obvious choice, and Microsoft Defender provided a solid foundation for endpoint and email security. However, I quickly realized that relying on a single vendor, even one as robust as Microsoft, left gaps in our defenses. Cybersecurity is never one-size-fits-all, which is why my preference was to layer an additional solution on top of our native security to improve our security posture.

Teams needed a solution that could layer seamlessly on top of Microsoft, without adding complexity or draining limited resources. That’s when I found Darktrace. I had heard of their reputation after seeing how other organizations used Darktrace to secure their infrastructure and was impressed by their AI-native, agentless approach and agreed to a proof of value (POV).

Our goal was to elavate Microsoft with an additional layer of intelligence- one that could seamlessly integrate, operate autonomously, and support a small team without increasing overhead. We turned to Darktrace because its AI-native, agentless approach offered a fundamentally different way to detect and respond to threats, learning our environment in real time and filling gaps that traditional tools can miss. With a quick POV, we were able to validate how effectively Darktrace works alongside Microsoft to deliver a more complete and resilient security architecture.

Why Darktrace stood out

From the start, Darktrace differentiated itself in several critical ways:

  • Deep visibility: Unlike other solutions that rely simply on host-based monitoring with endpoint agents, Darktrace operates passively at the network layer and integrates via APIs for email and identity security. This gave full visibility into network traffic that we previously didn’t have, going beyond our existing endpoint-based tools without adding additional maintenance overhead for our small IT team.
  • AI-native from the ground up: Darktrace wasn’t just layering AI on top of an existing product; it was built with AI at its core. Their autonomous detection and response to threats immediately reduced the need for constant human supervision. In a world where cyber-attacks are increasingly sophisticated and subtle, having an AI that learns our environment and adapts in real time is invaluable.
  • Comprehensive coverage: We started with a POV focused on email security, but quickly expanded to full deployment across our entire infrastructure. Darktrace’s products now protect our email, network, and identity layers, providing visibility and defense against lateral movement and abnormal behavior that traditional tools often miss.

Integration and workflow: Smooth and simple

One of the most impressive aspects of Darktrace is how easy it was to integrate into an existing environment. For network security, it was as simple as plugging an appliance into our top-of-rack switch – no downtime, no complex configuration. For email and identity, API integrations meant we could be up and running in hours, not weeks.

This simplicity extended to day-to-day operations. Our IT team received regular security reports, and any time we had questions or needed to adjust policies, Darktrace’s support team was there with white-glove service. Their responsiveness- even in the middle of the night- gave us confidence that we had true partners, not just a vendor.

Real-world impact: Threats stopped, time saved

The results spoke for themselves. During the time with Darktrace, I did not experience any security incidents. The team slept better at night knowing that Darktrace was monitoring for anomalies and proactively blocking suspicious activity, alerting us even before we noticed anything was wrong.

A memorable example was during an Electronic Health Record (EHR) upgrade, when my team forgot to adjust the policy in advance. Darktrace’s autonomous response was so effective that it blocked our upgrade activities- proof that nothing, not even internal changes, could slip by unnoticed. This level of vigilance meant that ransomware, data exfiltration attempts, or insider threats would be detected and contained before causing harm.

While I can’t share specific ROI numbers, the value was clear: we’ve avoided costly breaches, reduced the time spent investigating alerts, and eliminated the performance drag of agent-based tools. With Darktrace layered on top of Microsoft, I’ve hit the right balance of maximum protection with minimal spending. The cost of Darktrace / EMAIL was competitive, especially when factoring in the included Managed Detection and Response (MDR) service, which provides expert human oversight on top of the AI.

Key differentiators over the competition

  • Extending visibility beyond the endpoint: Traditional host-based monitoring solutions, such as EDR, play a critical role in securing individual devices. By adding a network detection and response (NDR) layer, we gained visibility into activity across our wider digital environment, surfacing threats that move laterally, operate between devices, or bypass endpoint controls. Darktrace also stood out for its ability to learn our normal patterns of behavior and identify subtle deviations in real time, not just known indicators of compromise. Because this is delivered through passive, non-disruptive monitoring, we were able to strengthen our defenses without adding complexity or impacting performance.
  • Layered security without complexity: Darktrace elevated our Microsoft foundation without creating conflicts or requiring us to disable existing protections. This layered approach maximized our security posture without adding operational burden.
  • Expert partnership: Beyond technology, Darktrace’s team acted as true partners, guiding us through deployment, providing ongoing support, and helping us interpret findings. This partnership was as valuable as the technology itself.

Advice for other nonprofits

If you’re an IT leader in a nonprofit, my advice is simple: look for solutions that are easy to deploy, intelligent in their response, and cost-effective. Don’t settle for more endpoint based tools that overlap with what you already have. Seek out a layered approach that covers your blind spots – especially at the network and email layers- at a price point that suits your organization.

Most importantly, don’t be afraid to evaluate new solutions. Even if you’re inundated with vendor pitches, you owe it to your organization to explore options that could save you time, money, and sleepless nights.

For organizations I work at, combining Microsoft’s security stack with Darktrace’s AI-native, platform struck the right balance between protection and practicality. We gained enterprise-grade security without sacrificing performance or stretching our budget. In the end, that meant more resources for what matters most: delivering care to our patients. If you’re facing similar challenges, I encourage you to consider how Darktrace could transform your security posture, and give your team the peace of mind they deserve.

For the organization I work in, combining Microsoft with Darktrace delivered a clear step-change in our security posture. Microsoft provided the foundation, while Darktrace’s behavioral intelligence added visibility into the unknown, surfacing emerging threats based on deviations in real-time activity, not just known indicators.

The result was enterprise-grade protection without added overhead, allowing us to stay focused on patient outcomes, not security operations. For organizations facing similar pressures, this layered approach offers a smarter, more efficient path to securing modern environments.

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About the author
Mice Chen
Chief Information Security Officer

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June 25, 2026

Shadow AI Detection: The First Step Toward Securing AI

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Why shadow AI is emerging  

Imagine you’re an employee under pressure, deadlines stacking up, repetitive tasks piling higher by the day. You find a free AI tool online that promises to automate the work in seconds; no approvals are needed. It feels like a simple win, paste in some data, write a quick prompt, and move faster.

But in that moment, something changed.  

Sensitive customer information is entered into a tool your organization doesn’t monitor, doesn’t govern, and can’t see and suddenly, that data is no longer where it should be, and no one knows where it’s gone.

This is the reality of Shadow AI: employees using unsanctioned AI tools to move faster, while unintentionally creating risk that exists entirely outside visibility and control.  

This is not just a one off case, research across businesses indicate that nearly half of employees report using unsanctioned AI tools, often prioritizing speed and productivity over security. Additionally, 51% of employees report connecting AI tools to work systems or apps without IT approval, creating significant operational risk where the average cost of security incidents in organizations with a high level of shadow AI usage can reach $670k.

While shadow AI is often top of mind for security professionals, it is just one component of how AI use can increase risk. Understanding and managing shadow AI use should be considered as part of a broader, comprehensive risk management strategy that aims to secure AI systems, including human and agent identities, interactions, human-AI partnerships, and behaviors operating across the digital enterprise from visibility and governance through detection, response, and recovery.  

Effective risk management calls for a layered and interdisciplinary strategy. It requires addressing issues across governance and visibility; identity, access and agent control, data security and privacy, secure MLOps / LLMOps, runtime security, behavior-based detection, autonomous response and recovery.  

This blog explores a specific governance and visibility use case linked to shadow AI and reveals the challenges it presents as well as the defensive strategies that security teams can adopt.

Why shadow AI is hard to detect  

When it comes to AI, what organizations can easily see does not always reflect the full scope of AI activity occurring within the tools, applications, and workflows used across an enterprise. As a result, organizations using traditional rule-based methods to flag unusual activity may struggle to distinguish unsanctioned AI usage from legitimate operational behavior, particularly as SaaS applications, APIs, and orchestration layers increasingly have AI embedded into normal business workflows. Identifying threats using previously observed intelligence or depending on hard to maintain allow and block lists does not provide a dynamic enough strategy to manage risk. Also, many organizations are focusing on identifying Shadow AI in their governed infrastructure, like gateways, endpoints, or SASE, which is foundational. But, organizations require visibility and Shadow AI detection across all networked infrastructure from on-prem, hybrid, data centers, and cloud infrastructure that may not have endpoint agent visibility. This uncovers the utilization of MCP, data flows, and autonomous agents across these domains.

For example, employees interact with AI assistants across approved SaaS platforms every day. However, browser extensions and other types of plug-ins can route prompts that include enterprise data to embedded AI services in ways that are not visible to the security team. AI enabled workflows may invoke multiple APIs, orchestration layers, and cloud services behind the scenes, making it difficult for traditional security tooling to determine where data is processed, stored, or retransmitted. Because much of this activity occurs within trusted browser sessions and encrypted SaaS traffic, conventional network monitoring, DLP, and application allowlisting controls often lack the context needed to accurately identify or govern these interactions

Identifying AI tools in the environment is one part of the equation. Understanding the behavior surrounding their use is where the real challenge lies. An AI application is not inherently risky, but the way users or other assets interact with it may be. Sensitive data exposure, abnormal access patterns, and misuse of AI-assisted workflows often appear legitimate in isolation and only become visible through behavioral analysis across the broader environment.  

What Shadow AI visibility does and doesn’t show

Comprehensive Shadow AI visibility allows organizations to answer several important questions:

  • What types of AI are we using? What AI platforms, agents, MCP clients/servers, and services are active across the enterprise?  
  • Who is using AI services? Which users, business units, or systems are interacting with those AI services?  
  • Is our data safe? Is sensitive or regulated data being exposed through prompts, workflows, or integrations?  
  • Are AI systems behaving as expected? Are AI systems behaving anomalously or operating outside approved governance processes?  
  • Are our AI systems under attack? Is an attacker attempting to manipulate prompts, influence agent behavior, or abuse AI-enabled workflows?

Answering these questions is foundational to broader AI governance efforts. However, it is limited to helping teams understand initial interactions and fails to offer insight into dependencies and outcomes that are critical to securing AI across an enterprise.  

Deeper visibility that includes the ability to understand dependencies and outcomes are not always available in AI security point products. Answering the questions below requires understanding runtime behavior and operational outcomes:  

  • What actions did the AI interaction trigger?  
  • What systems, applications, or data did it access? Did the AI operate beyond its intended permissions or scope?  
  • Could a low-risk interaction lead to high-risk outcomes?  
  • What is the risk and context understanding of an anomalous activity to assist in prioritization of analysis and autonomous response action?

The distinction between these two sets of questions offers two different layers of AI security. The first set of questions focuses on discovery and interaction visibility. The second set focuses on providing visibility that includes the context and outcomes that are critical for managing follow-on risks associated with obfuscated downstream activities.  

Together, these layers help organizations move beyond simply identifying AI usage toward understanding how AI behaves operationally across the enterprise.

How organizations are addressing shadow AI

Most organizations still approach shadow AI as an application control problem, relying on policies, browser restrictions, and allow/block lists. However, AI adoption is evolving faster than most governance processes can realistically keep pace with. New assistants, plugins, and embedded AI features appear continuously, creating pressure to enable business productivity while simultaneously containing risk.  

Existing governance processes were designed for a more traditional SaaS adoption cycle, where new applications could be reviewed, approved, and monitored over longer time horizons. AI adoption operates differently. New capabilities can appear overnight inside existing platforms employees already use, making it difficult for security and governance teams to maintain an accurate understanding of enterprise AI exposure. This means that many organizations are experiencing significant operational overhead, particularly in large environments where AI usage is decentralized across teams, departments, and third-party services.  

Where should organizations start when securing their AI systems?

Shadow AI identification is an on-going critical component for AI Risk/Governance Boards as well as security organizations. As organizations seek AI certifications like ISO 42001 AI Management Systems, visibility into all AI adoption from enterprise use to custom innovation and development is crucial. Shadow AI identification provides organizations with the visibility needed to decide whether an AI tool should be brought into governed environments to reduce data loss (DLP) risks or whether policies should be established and enforced to restrict their use.

As organizations rapidly innovate and adopt AI, they are taking on more and more risk. Organizations need to have a strategy in place to mitigate the assumed risk, especially with third-party adoption. Visibility, monitoring, governance enforcement, behavioral-based detection of non-deterministic systems, and autonomous investigation and containment becomes critical to mitigating the risk of AI systems.  

How Darktrace secures AI and shadow AI

Attackers are using AI to move faster, scale tactics, and make threats more adaptive and convincing. Internally, organizations are grappling with new forms of risk created by generative AI, autonomous agents, shadow AI, and increasingly complex digital environments.

Darktrace helps organizations protect both people and AI in a world where AI is now central to how business gets done. Darktrace / SECURE AI helps organizations discover and control shadow AI by surfacing unsanctioned or unexpected AI activity where it appears – including MCP detections, distinguishing misuse of legitimate tools and unapproved services, and applying policy to contain data exposure while guiding users toward sanctioned options.

Stay up to date on AI security

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

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About the author
Nicole Carignan
SVP, Security & AI Strategy, Field CISO
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