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November 15, 2021

Darktrace Defends McLaren Racing From Supply Chain Attacks

McLaren Racing chose Darktrace's self-learning AI to fight off supply chain attacks. Learn how Darktrace safeguards their organization with elite cybersecurity.
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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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15
Nov 2021

McLaren Racing has a track record of forming valuable and innovative partnerships. Without these partnerships and the web of organisations that make up our supply chain, it’s unlikely we could be where we are today.

Figure 1: The origins of the different components of McLaren’s 2021 car

Each component of the McLaren Formula 1 car – engine, tyres, brakes, suspension – has a long and complicated backstory, from the R&D labs where it was conceived, to the factory floor on which it was manufactured, to transport and logistics getting it to where it needs to be.

Looking at the entire organisation, the situation is even more complex. IT hardware and software, telemetry, and data analysis tools, each represent a critical component to McLaren Racing’s ecosystem. Without it, we couldn’t function at the top of our game.

But from a security perspective, each of these represent a potential chink in the team’s defensive armour, against a backdrop of a cyber-threat landscape which becomes more hostile every year. As we’ve seen this year from the likes of the SolarWinds hack and the Kaseya software exploit, attackers are waking up to the fact that the supply chain represents a significant opportunity.

A single supplier may represent a point of entry into thousands of organisations. For cyber-criminals, this means one successful compromise can result in more access, more data, and ultimately greater profit.

McLaren Racing is all too aware of recent shifts in the cyber security landscape. A successful cyber-attack on our organisation could have implications on race-day performance, as well as our wider reputation. Last year, we brought in a new line of defence with Darktrace’s Self-Learning AI technology, that learns our business from the ground up, and interrupts subtle and fast-moving cyber-threats wherever they emerge – including from our supply chain.

Threat find: Attacking through the inbox

In this attack, 12 employees were targeted in a systematic phishing attack, receiving an email from a long-established team supplier, notifying them that a voicemail had been left for them.

Figure 2: An extract of the phishing email coaxing the recipient to click

The link to play the voicemail led to a legitimate-looking voicemail service site.

When following the link to access the message, the site requested Office 365 credentials to authenticate the user, designed to harvest the McLaren Racing credentials that could be used to access our environment.

Figure 3: The fake login page

Of the 12 recipients, several key people within our team were targeted, including technical directors and purchase ledgers. The attackers behind this phishing campaign no doubt hand-picked these individuals both due to their authorization powers and the likelihood their accounts had access to sensitive data.

Had these accounts been compromised, the attackers would have had access to some of the highest sensitivity of intellectual property, finance information and executive level strategy within racing.

Darktrace’s email security technology, Antigena Email, assessed the content of these emails as they were delivered, and identified several unusual indicators of attack. While it recognised that the account was one familiar to McLaren, it compared this attack with previous emails sent from the supplier and recognised several risk indicators. Darktrace Antigena autonomously took the decision to hold the email from being delivered to users’ mailboxes.

Figure 4: Antigena Email reveals in plain language why the email was suspicious and the action it took

Legitimate communication between our team and the supplier was still flowing uninterrupted, as Darktrace Antigena was assessing each email’s indicators for risk. The following day, the supplier’s account manager in our team received an email from the supplier in question, informing them that one of their accounts had been compromised and was used to send phishing emails to some of their customers. This confirmed that Antigena Email had correctly identified the email as malicious.

Traditional email security tools rely on historical attack data to determine friend from foe, but this is only effective in cases where an email domain or a malicious URL has been previously encountered. In this case, traditional filtering allowed the email through. Only by having Darktrace’s understanding of ‘self’ and Autonomous Response was McLaren able to avoid exposure to risk on this occasion.

This is reflective of a wider pattern noticed by the security team. Darktrace determines that around 40% of emails going through Antigena Email would have been detected by our other security tools, suggesting that Darktrace is detecting an extra 60% of malicious emails and taking action to ensure we are protected 24/7.

This was just one example of an attempted attack on McLaren through the inbox. On another occasion, Antigena Email identified an email that was attempting to impersonate a sponsor. The email in question was requesting that a senior McLaren Racing figure reset their password and contained a suspicious link that led to a credential harvester. Again, Antigena took action on the emails at time of delivery, and our internal cyber team never had to respond to what could have been a serious incident. It’s through Darktrace taking autonomous action like this on a daily basis that we are able to focus our time on higher-value, strategic work, driving success for the wider team.

Why the supply chain demands a new approach to security

In today’s digitised world, it is impossible to operate as a fluid, dynamic organisation without interacting with suppliers and partners at every digital layer: from email, to file sharing services and technology partners delivered through the cloud. As McLaren grows and works with leading global organisations to improve its performance, its supply chain ecosystem will only get broader.

Attackers are targeting suppliers because they represent a single key that opens potentially dozens or even hundreds of locks, and email is just one avenue of attack. By partnering with Darktrace, McLaren experiences the value of self-learning protection on a daily basis, across its email systems, cloud services, and corporate network.

Whether it’s email or some other form of communication from a supplier, you cannot assume you know who’s on the other side of the keyboard. This is what so many existing security defences do – with static rules and signatures unable to truly tell friend from foe and reveal account takeovers and compromised systems. Modern organisations need a solution that is able to identify potentially malicious activity from suppliers by analysing a broad range of indicators and revealing subtle deviations that indicate threat, and this is where Self-Learning AI shines.

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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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December 18, 2025

Why organizations are moving to label-free, behavioral DLP for outbound email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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December 17, 2025

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

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What is an Adversary-in-the-middle (AiTM) attack?

Adversary-in-the-Middle (AiTM) attacks are a sophisticated technique often paired with phishing campaigns to steal user credentials. Unlike traditional phishing, which multi-factor authentication (MFA) increasingly mitigates, AiTM attacks leverage reverse proxy servers to intercept authentication tokens and session cookies. This allows attackers to bypass MFA entirely and hijack active sessions, stealthily maintaining access without repeated logins.

This blog examines a real-world incident detected during a Darktrace customer trial, highlighting how Darktrace / EMAILTM and Darktrace / IDENTITYTM identified the emerging compromise in a customer’s email and software-as-a-service (SaaS) environment, tracked its progression, and could have intervened at critical moments to contain the threat had Darktrace’s Autonomous Response capability been enabled.

What does an AiTM attack look like?

Inbound phishing email

Attacks typically begin with a phishing email, often originating from the compromised account of a known contact like a vendor or business partner. These emails will often contain malicious links or attachments leading to fake login pages designed to spoof legitimate login platforms, like Microsoft 365, designed to harvest user credentials.

Proxy-based credential theft and session hijacking

When a user clicks on a malicious link, they are redirected through an attacker-controlled proxy that impersonates legitimate services.  This proxy forwards login requests to Microsoft, making the login page appear legitimate. After the user successfully completes MFA, the attacker captures credentials and session tokens, enabling full account takeover without the need for reauthentication.

Follow-on attacks

Once inside, attackers will typically establish persistence through the creation of email rules or registering OAuth applications. From there, they often act on their objectives, exfiltrating sensitive data and launching additional business email compromise (BEC) campaigns. These campaigns can include fraudulent payment requests to external contacts or internal phishing designed to compromise more accounts and enable lateral movement across the organization.

Darktrace’s detection of an AiTM attack

At the end of September 2025, Darktrace detected one such example of an AiTM attack on the network of a customer trialling Darktrace / EMAIL and Darktrace / IDENTITY.

In this instance, the first indicator of compromise observed by Darktrace was the creation of a malicious email rule on one of the customer’s Office 365 accounts, suggesting the account had likely already been compromised before Darktrace was deployed for the trial.

Darktrace / IDENTITY observed the account creating a new email rule with a randomly generated name, likely to hide its presence from the legitimate account owner. The rule marked all inbound emails as read and deleted them, while ignoring any existing mail rules on the account. This rule was likely intended to conceal any replies to malicious emails the attacker had sent from the legitimate account owner and to facilitate further phishing attempts.

Darktrace’s detection of the anomalous email rule creation.
Figure 1: Darktrace’s detection of the anomalous email rule creation.

Internal and external phishing

Following the creation of the email rule, Darktrace / EMAIL observed a surge of suspicious activity on the user’s account. The account sent emails with subject lines referencing payment information to over 9,000 different external recipients within just one hour. Darktrace also identified that these emails contained a link to an unusual Google Drive endpoint, embedded in the text “download order and invoice”.

Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Figure 2: Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.
Figure 3: Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.

As Darktrace / EMAIL flagged the message with the ‘Compromise Indicators’ tag (Figure 2), it would have been held automatically if the customer had enabled default Data Loss Prevention (DLP) Action Flows in their email environment, preventing any external phishing attempts.

Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.
Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.

Darktrace analysis revealed that, after clicking the malicious link in the email, recipients would be redirected to a convincing landing page that closely mimicked the customer’s legitimate branding, including authentic imagery and logos, where prompted to download with a PDF named “invoice”.

Figure 5: Download and login prompts presented to recipients after following the malicious email link, shown here in safe view.

After clicking the “Download” button, users would be prompted to enter their company credentials on a page that was likely a credential-harvesting tool, designed to steal corporate login details and enable further compromise of SaaS and email accounts.

Darktrace’s Response

In this case, Darktrace’s Autonomous Response was not fully enabled across the customer’s email or SaaS environments, allowing the compromise to progress,  as observed by Darktrace here.

Despite this, Darktrace / EMAIL’s successful detection of the malicious Google Drive link in the internal phishing emails prompted it to suggest ‘Lock Link’, as a recommended action for the customer’s security team to manually apply. This action would have automatically placed the malicious link behind a warning or screening page blocking users from visiting it.

Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.
Figure 6: Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.

Furthermore, if active in the customer’s SaaS environment, Darktrace would likely have been able to mitigate the threat even earlier, at the point of the first unusual activity: the creation of a new email rule. Mitigative actions would have included forcing the user to log out, terminating any active sessions, and disabling the account.

Conclusion

AiTM attacks represent a significant evolution in credential theft techniques, enabling attackers to bypass MFA and hijack active sessions through reverse proxy infrastructure. In the real-world case we explored, Darktrace’s AI-driven detection identified multiple stages of the attack, from anomalous email rule creation to suspicious internal email activity, demonstrating how Autonomous Response could have contained the threat before escalation.

MFA is a critical security measure, but it is no longer a silver bullet. Attackers are increasingly targeting session tokens rather than passwords, exploiting trusted SaaS environments and internal communications to remain undetected. Behavioral AI provides a vital layer of defense by spotting subtle anomalies that traditional tools often miss

Security teams must move beyond static defenses and embrace adaptive, AI-driven solutions that can detect and respond in real time. Regularly review SaaS configurations, enforce conditional access policies, and deploy technologies that understand “normal” behavior to stop attackers before they succeed.

Credit to David Ison (Cyber Analyst), Bertille Pierron (Solutions Engineer), Ryan Traill (Analyst Content Lead)

Appendices

Models

SaaS / Anomalous New Email Rule

Tactic – Technique – Sub-Technique  

Phishing - T1566

Adversary-in-the-Middle - T1557

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About the author
David Ison
Cyber Analyst
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