Self-driving vehicles ensure secure storage of AI model embeddings through a combination of encryption, access controls, and hardware-based security mechanisms. Embeddings—compact numerical representations of data used by AI models—are critical to vehicle perception and decision-making. Protecting them from unauthorized access or tampering is essential to maintain system integrity and safety. To achieve this, developers implement layers of security, starting with encrypting embeddings both at rest (stored in memory) and in transit (during updates or data sharing). For example, AES-256 encryption is commonly used to safeguard stored embeddings, while TLS protocols secure data transmitted between the vehicle and cloud servers. Hardware security modules (HSMs) or trusted platform modules (TPMs) are often integrated into onboard systems to manage encryption keys securely, ensuring they’re never exposed to potential attackers.
Access controls further restrict who or what can interact with stored embeddings. Role-based access policies ensure only authenticated components, like the perception system or over-the-air (OTA) update services, can read or modify embeddings. Multi-factor authentication (MFA) might be required for engineers accessing diagnostic tools, and secure boot processes ensure only signed, verified software runs on the vehicle’s hardware. For instance, Tesla’s vehicles use code signing and partition storage to isolate sensitive data like embeddings from non-critical systems. Additionally, OTA updates are cryptographically signed to prevent unauthorized code from altering the AI model or its embeddings. These measures create a “defense in depth” approach, where even if one layer is compromised, others remain intact to protect the system.
Finally, continuous monitoring and auditing help detect and mitigate threats. Intrusion detection systems (IDS) analyze patterns in data access to flag anomalies, such as unexpected attempts to read embeddings. Data anonymization techniques, like differential privacy, might also be applied to embeddings used for training updates, ensuring they can’t be reverse-engineered to reveal sensitive information. Companies like Waymo and Cruise conduct regular penetration testing and third-party audits to validate their security practices. By combining encryption, strict access controls, and proactive monitoring, self-driving vehicles maintain the confidentiality and integrity of AI model embeddings while enabling safe, reliable operation in real-world environments.