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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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May 8, 2025

Anomaly-based threat hunting: Darktrace's approach in action

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What is threat hunting?

Threat hunting in cybersecurity involves proactively and iteratively searching through networks and datasets to detect threats that evade existing automated security solutions. It is an important component of a strong cybersecurity posture.

There are several frameworks that Darktrace analysts use to guide how threat hunting is carried out, some of which are:

  • MITRE Attack
  • Tactics, Techniques, Procedures (TTPs)
  • Diamond Model for Intrusion Analysis
  • Adversary, Infrastructure, Victims, Capabilities
  • Threat Hunt Model – Six Steps
  • Purpose, Scope, Equip, Plan, Execute, Feedback
  • Pyramid of Pain

These frameworks are important in baselining how to run a threat hunt. There are also a combination of different methods that allow defenders diversity– regardless of whether it is a proactive or reactive threat hunt. Some of these are:

  • Hypothesis-based threat hunting
  • Analytics-driven threat hunting
  • Automated/machine learning hunting
  • Indicator of Compromise (IoC) hunting
  • Victim-based threat hunting

Threat hunting with Darktrace

At its core, Darktrace relies on anomaly-based detection methods. It combines various machine learning types that allows it to characterize what constitutes ‘normal’, based on the analysis of many different measures of a device or actor’s behavior. Those types of learning are then curated into what are called models.

Darktrace models leverage anomaly detection and integrate outputs from Darktrace Deep Packet Inspection, telemetry inputs, and additional modules, creating tailored activity detection.

This dynamic understanding allows Darktrace to identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign.  On top of machine learning models for detection, there is also the ability to change and create models showcasing the tool’s diversity. The Model Editor allows security teams to specify values, priorities, thresholds, and actions they want to detect. That means a team can create custom detection models based on specific use cases or business requirements. Teams can also increase the priority of existing detections based on their own risk assessments to their environment.

This level of dexterity is particularly useful when conducting a threat hunt. As described above, and in previous ‘Inside the SOC’ blogs such a threat hunt can be on a specific threat actor, specific sector, or a  hypothesis-based threat hunt combined with ‘experimenting’ with some of Darktrace’s models.

Conducting a threat hunt in the energy sector with experimental models

In Darktrace’s recent Threat Research report “AI & Cybersecurity: The state of cyber in UK and US energy sectors” Darktrace’s Threat Research team crafted hypothesis-driven threat hunts, building experimental models and investigating existing models to test them and detect malicious activity across Darktrace customers in the energy sector.

For one of the hunts, which hypothesised utilization of PerfectData software and multi-factor authentication (MFA) bypass to compromise user accounts and destruct data, an experimental model was created to detect a Software-as-a-Service (SaaS) user performing activity relating to 'PerfectData Software’, known to allow a threat actor to exfiltrate whole mailboxes as a PST file. Experimental model alerts caused by this anomalous activity were analyzed, in conjunction with existing SaaS and email-related models that would indicate a multi-stage attack in line with the hypothesis.

Whilst hunting, Darktrace researchers found multiple model alerts for this experimental model associated with PerfectData software usage, within energy sector customers, including an oil and gas investment company, as well as other sectors. Upon further investigation, it was also found that in June 2024, a malicious actor had targeted a renewable energy infrastructure provider via a PerfectData Software attack and demonstrated intent to conduct an Operational Technology (OT) attack.

The actor logged into Azure AD from a rare US IP address. They then granted Consent to ‘eM Client’ from the same IP. Shortly after, the actor granted ‘AddServicePrincipal’ via Azure to PerfectData Software. Two days later, the actor created a  new email rule from a London IP to move emails to an RSS Feed Folder, stop processing rules, and mark emails as read. They then accessed mail items in the “\Sent” folder from a malicious IP belonging to anonymization network,  Private Internet Access Virtual Private Network (PIA VPN) [1]. The actor then conducted mass email deletions, deleting multiple instances of emails with subject “[Name] shared "[Company Name] Proposal" With You” from the  “\Sent folder”. The emails’ subject suggests the email likely contains a link to file storage for phishing purposes. The mass deletion likely represented an attempt to obfuscate a potential outbound phishing email campaign.

The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.
Figure 1: The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.

A month later, the same user was observed downloading mass mLog CSV files related to proprietary and Operational Technology information. In September, three months after the initial attack, another mass download of operational files occurred by this actor, pertaining to operating instructions and measurements, The observed patience and specific file downloads seemingly demonstrated an intent to conduct or research possible OT attack vectors. An attack on OT could have significant impacts including operational downtime, reputational damage, and harm to everyday operations. Darktrace alerted the impacted customer once findings were verified, and subsequent actions were taken by the internal security team to prevent further malicious activity.

Conclusion

Harnessing the power of different tools in a security stack is a key element to cyber defense. The above hypothesis-based threat hunt and custom demonstrated intent to conduct an experimental model creation demonstrates different threat hunting approaches, how Darktrace’s approach can be operationalized, and that proactive threat hunting can be a valuable complement to traditional security controls and is essential for organizations facing increasingly complex threat landscapes.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO at Darktrace) and Zoe Tilsiter (EMEA Consultancy Lead)

References

  1. https://spur.us/context/191.96.106.219

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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May 6, 2025

Combatting the Top Three Sources of Risk in the Cloud

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With cloud computing, organizations are storing data like intellectual property, trade secrets, Personally Identifiable Information (PII), proprietary code and statistics, and other sensitive information in the cloud. If this data were to be accessed by malicious actors, it could incur financial loss, reputational damage, legal liabilities, and business disruption.

Last year data breaches in solely public cloud deployments were the most expensive type of data breach, with an average of $5.17 million USD, a 13.1% increase from the year before.

So, as cloud usage continues to grow, the teams in charge of protecting these deployments must understand the associated cybersecurity risks.

What are cloud risks?

Cloud threats come in many forms, with one of the key types consisting of cloud risks. These arise from challenges in implementing and maintaining cloud infrastructure, which can expose the organization to potential damage, loss, and attacks.

There are three major types of cloud risks:

1. Misconfigurations

As organizations struggle with complex cloud environments, misconfiguration is one of the leading causes of cloud security incidents. These risks occur when cloud settings leave gaps between cloud security solutions and expose data and services to unauthorized access. If discovered by a threat actor, a misconfiguration can be exploited to allow infiltration, lateral movement, escalation, and damage.

With the scale and dynamism of cloud infrastructure and the complexity of hybrid and multi-cloud deployments, security teams face a major challenge in exerting the required visibility and control to identify misconfigurations before they are exploited.

Common causes of misconfiguration come from skill shortages, outdated practices, and manual workflows. For example, potential misconfigurations can occur around firewall zones, isolated file systems, and mount systems, which all require specialized skill to set up and diligent monitoring to maintain

2. Identity and Access Management (IAM) failures

IAM has only increased in importance with the rise of cloud computing and remote working. It allows security teams to control which users can and cannot access sensitive data, applications, and other resources.

Cybersecurity professionals ranked IAM skills as the second most important security skill to have, just behind general cloud and application security.

There are four parts to IAM: authentication, authorization, administration, and auditing and reporting. Within these, there are a lot of subcomponents as well, including but not limited to Single Sign-On (SSO), Two-Factor Authentication (2FA), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

Security teams are faced with the challenge of allowing enough access for employees, contractors, vendors, and partners to complete their jobs while restricting enough to maintain security. They may struggle to track what users are doing across the cloud, apps, and on-premises servers.

When IAM is misconfigured, it increases the attack surface and can leave accounts with access to resources they do not need to perform their intended roles. This type of risk creates the possibility for threat actors or compromised accounts to gain access to sensitive company data and escalate privileges in cloud environments. It can also allow malicious insiders and users who accidentally violate data protection regulations to cause greater damage.

3. Cross-domain threats

The complexity of hybrid and cloud environments can be exploited by attacks that cross multiple domains, such as traditional network environments, identity systems, SaaS platforms, and cloud environments. These attacks are difficult to detect and mitigate, especially when a security posture is siloed or fragmented.  

Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.  

Challenges in securing against cross-domain threats often come from a lack of unified visibility. If a security team does not have unified visibility across the organization’s domains, gaps between various infrastructures and the teams that manage them can leave organizations vulnerable.

Adopting AI cybersecurity tools to reduce cloud risk

For security teams to defend against misconfigurations, IAM failures, and insecure APIs, they require a combination of enhanced visibility into cloud assets and architectures, better automation, and more advanced analytics. These capabilities can be achieved with AI-powered cybersecurity tools.

Such tools use AI and automation to help teams maintain a clear view of all their assets and activities and consistently enforce security policies.

Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that makes cloud security accessible to all security teams and SOCs by using AI to identify and correct misconfigurations and other cloud risks in public, hybrid, and multi-cloud environments.

It provides real-time, dynamic architectural modeling, which gives SecOps and DevOps teams a unified view of cloud infrastructures to enhance collaboration and reveal possible misconfigurations and other cloud risks. It continuously evaluates architecture changes and monitors real-time activity, providing audit-ready traceability and proactive risk management.

Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.
Figure 1: Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.

Darktrace / CLOUD also offers attack path modeling for the cloud. It can identify exposed assets and highlight internal attack paths to get a dynamic view of the riskiest paths across cloud environments, network environments, and between – enabling security teams to prioritize based on unique business risk and address gaps to prevent future attacks.  

Darktrace’s Self-Learning AI ensures continuous cloud resilience, helping teams move from reactive to proactive defense.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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