Hardware and infrastructure security for AI

Confidential computing and environment isolation

Using trusted execution environments (such as secure enclaves) to ensure that model weights and computations remain confidential and tamper-proof during large-scale AI inference and training. This also involves reducing the attack surface through sandboxed, code-minimal deployments, specialized hardware/firmware stacks, and maintaining verifiable runtime integrity checks.

Supply chain integrity and secure development

Ensuring end-to-end verification of hardware and software supply chains through source-verified firmware, SLSA compliance, and secure software development lifecycles tailored for ML-specific infrastructure. This also includes developing automated tooling to continuously verify the provenance and integrity of model components, dependencies, and third-party code used in training and inference pipelines.

Continuous monitoring, advanced threat detection, and incident response

Developing ML-driven anomaly detection and logging systems capable of flagging and responding to subtle infiltration attempts or insider threats in real-time. This also includes red-teaming and automated penetration testing frameworks specialized for AI systems, including simulations of zero-day attacks and insider compromises.

Hardware-integrated monitoring and verification

Integrating monitoring capabilities directly into hardware, such as secure counters and tamper-evident seals, along with deploying specialized firmware that can detect and respond to attempts at parameter theft or physical attacks. This also includes verification tools, such as hardware-level logging and secured audit trails that remain verifiable under sophisticated tampering attempts for rapid, evidence-based incident response.

Specialized chips to compute encrypted data

Designing and deploying hardware accelerators optimized for computations on encrypted data, such as homomorphic encryption schemes, to facilitate efficient encrypted training and inference without exposing plaintext model parameters or sensitive input data outside the protected hardware boundary.

Tamper-evidence and tamper-proofing

Implementing tamper-resistant enclosures, seals, and other tamper-evident mechanisms to ensure that any unauthorized physical access or modification attempts are detectable. Such measures help maintain the integrity of hardware components and prevent adversaries from compromising the system at a physical level.

Datacenter security

Relevant research focuses on designing and deploying resilient hardware- and software-based defenses to prevent model theft and sabotage. This includes methods like encrypted computation, secure enclaves, continuous anomaly detection, zero-trust architectures, and rigorous supply chain verification to protect against both external intrusions and insider threats.