Why Do F1 Teams Need Cybersecurity, and How Is AI Changing the Threat Landscape?

An F1 car doesn’t just burn fuel, it burns data. 

Across a race weekend, hundreds of onboard sensors generate hundreds of gigabytes of telemetry, and that stream moves constantly, from car to garage, garage to trackside systems, trackside to factory, factory back to the pit wall. The competitive edge lives inside those packets, which is why rivals, criminal groups, and even nation-state actors all have reasons to want in. The story here isn’t “sports security”, it’s modern enterprise security with a stopwatch.

F1 runs one of the most exposed data environments in professional sports

What is actually at risk

Once you picture F1 as a traveling engineering lab, the risk becomes obvious. Modern teams operate on live feedback loops: measure, decide, adjust, repeat. Telemetry isn’t “nice to have”, it’s the blueprint of the car while it’s still being drawn.

Teams protect:

• Live telemetry streams that reflect aerodynamic configuration, tire strategy signals, engine tuning trends, and even driver biometrics transmitted from car to trackside systems and back to the factory in near real time.
• Proprietary software and analytics that turn raw sensor output into decisions, e.g., setup recommendations, race simulations, and reliability predictions.
• Business data on the same rails: sponsor financials, contract information, internal planning, and operational documents that travel with the team.
• Global operational sprawl: teams compete across 20+ countries in a season. Each venue introduces new networks, new physical access opportunities, and new jurisdictions, meaning the threat profile shifts every few weeks.

The crown jewels aren't a single database. They're the services, identities, and workflows that move data through the system. That's where attackers focus.

Why third-party access makes it worse

A typical F1 team isn’t a closed system, it’s an ecosystem: dozens of technology vendors, suppliers, and partners, each providing critical capability. Every integration is also an exposure point, and each vendor relationship can quietly extend the attack surface beyond the team’s direct line of sight.

This matters because any savvy threat actor or group won’t hack a team “head-on”, so to speak. They will instead:

1. Find the softest adjacent party (supplier, partner, contractor).
2. Leverage their access or data flows.
3. Land inside the team’s environment with legitimate-looking credentials, sessions, or trusted connections.

Trackside teams operate in temporary, fast-moving environments where security takes a backseat to speed. Contractors, media, and sponsors need system access for hours or days, creating short-term exposures.

As such, “We’ll tighten it up later” is liable to become a habit, and these habits compound.

The threats are the same ones targeting every enterprise

IP theft, ransomware, and social engineering

Behind the speed, glamour, and heavy competition, the threat categories facing F1 look familiar to any security practitioner:

• IP theft has a long history in motorsport culture; engineers walking out with sensitive material is simply the human version. The digital version never sleeps: credentials reused, cloud shares misconfigured, data copied quietly, and access granted “temporarily” that becomes permanent.
• Ransomware becomes especially dangerous when time is the weapon. An enterprise can survive hours of downtime with financial loss and angry stakeholders, but a race team locked out of key systems hours before qualifying faces a different kind of pressure: pay fast, or lose the weekend.
• Social engineering thrives on routine and relevance. Race calendars, travel patterns, sponsor announcements, and internal schedules create a rich template for spear phishing. Traveling staff connecting from airports and hotels add exposure risk due to credentials and sessions can be intercepted or tricked, then carried back into more sensitive environments.

Get the 2026 Zscaler ThreatLabz Phishing and Initial Access Report here.

AI as an attack tool

AI doesn’t create new human weaknesses, it industrializes them.

Attackers can now:

• Generate highly convincing phishing content at speed, tuned to race-weekend timing, internal language, and real sponsor context.
• Use audio/video deepfakes to impersonate team principals or sponsor stakeholders, exploiting “voice trust” at near-zero cost.
• Run automated discovery and scanning to locate exposed systems faster than teams can respond—especially in temporary or rapidly changing race-weekend networks.

This is the part many organizations don’t want to admit: an attacker’s workflow is getting closer to “push button, get campaign” than ever before, driving security teams to defend at scale or get buried.

How AI and zero trust work together on defense

What AI does on the security side

At F1 telemetry scale, AI earns its keep by helping security teams see patterns and drift quickly, especially across distributed environments.

AI can help by:

• Establishing baselines of “normal” behavior across trackside systems, factory connectivity, and cloud endpoints, and flagging meaningful deviations fast.
• Tracking not just human users, but non-human identities too: automated pipelines, service accounts, and AI agents that increasingly act like “users” on the network.
• Correlating risks that don’t look severe in isolation but become dangerous in combination: misconfigurations, exposure, and overprivileged access.

But there’s a limitation worth saying out loud: AI detection becomes noisy when the environment is messy. Fragmented identity, inconsistent segmentation, and unclear ownership create false positives, and alert fatigue is how a good tool can get ignored.

Why perimeter security fails here and what replaces it

Perimeter security assumes there’s a stable “inside”, but F1 doesn’t have one. It’s global, partner-heavy, and built on fast-changing environments, meaning the moment you connect from a circuit in Singapore or a hotel in Austin, a “trusted location” becomes a myth.

Zero trust replaces the assumption with verification:

• Verify every session
• Verify every user and every device
• Grant least-privilege access
• Continuously re-evaluate trust as conditions change

This approach scales beyond motorsport; any enterprise with hybrid cloud, remote teams, and third-party access is living the same reality, just with fewer cameras pointed at it.

How Zscaler protects valuable F1 data and secures the use of AI

A partnership like Zscaler and Aston Martin F1 makes sense because the problem statement is clear: protect high-value data in a high-speed, high-change, high-adversary environment, while AI use accelerates across the workforce and development workflows.

Built on the Zscaler Zero Trust Exchange, Zscaler’s AI Security portfolio operates as consistent, scalable controls across every user, app, and data path, rather than isolated add-ons.

• Zscaler AI Access Security helps teams discover which AI apps are being used (including shadow AI), control access by user/group, extract and classify prompts/responses, and prevent sensitive data loss with inline DLP and content moderation.
• Zscaler AI Guardrails (AI Guard) bring inline inspection to AI interactions to block prompt injection and jailbreak-style attacks, stop data loss with DLP programs and predefined dictionaries, and filter outputs, all while providing dashboards and real-time alerting for visibility into AI use.
• Zscaler Automated AI Red Teaming supports continuous testing of AI systems from build to runtime using predefined probes, custom probes, and custom dataset uploads, with multi-modal testing (text, voice, images, documents). It also tracks and remediates issues via integrations like Jira and ServiceNow, and maps findings to frameworks (e.g., NIST AI RMF, OWASP LLM Top 10, MITRE ATLAS).
• Zscaler AI Asset Management (AI-SPM) focuses on getting a 360-degree view of AI models, agents, services, and connected data assets (datasets, vectors), then correlating risks like misconfigurations, exposure, entitlements, and poisoning risk, with guided remediation and compliance alignment (e.g., NIST AI RMF 600-1, EU AI Act, HIPAA, GDPR).

In F1, you don’t win by securing one laptop. You win by securing the system of work; users, vendors, apps, AI tools, models, data, and the pathways between them.

Schedule a custom demo of Zscaler AI Security today.