Cloud service providers face a growing challenge: how to effectively audit increasingly complex AI workloads while protecting customer privacy. As AI systems become more sophisticated, traditional manual review processes struggle to scale. The solution lies in extending existing hardware security infrastructure to enable automated, privacy-preserving verification of compute workloads.

The Challenge of Scaling Trust

Modern cloud providers need to ensure their infrastructure isn’t used for harmful purposes, whether intentional or accidental. This requires understanding how customers use compute resources and what kinds of workloads they’re running. However, customers are understandably reluctant to share sensitive details about their algorithms, data, and compute allocation.

This creates a fundamental tension:

• Providers need sufficient visibility to prevent misuse
• Customers need to protect their intellectual property and maintain competitive advantages
• Both sides want efficient, scalable processes

Traditional approaches involving manual review and documentation are becoming unsustainable as AI workloads grow in complexity and scale. We need automated solutions that can verify compliance while preserving privacy.

Building on Existing Infrastructure

The good news is that we already have much of the foundational infrastructure needed. Modern hardware security features like secure enclaves, remote attestation, and confidential computing provide proven building blocks for trust and verification.

By extending these existing capabilities, we can create:

• Privacy-preserving compute auditing protocols
• Automated pre-deployment verification
• Scalable trust mechanisms that work with existing hardware

This approach offers several key advantages:

• Leverages battle-tested security infrastructure
• Minimizes disruption to existing workflows
• Provides immediate practical benefits
• Creates foundation for future safety capabilities

From Verification to Safety

While solving the immediate challenge of scalable auditing, this infrastructure extension also enables broader capabilities for safe AI development. The same mechanisms that allow privacy-preserving verification of compute workloads can support:

• Automated compliance checking
• Standardized safety evaluations
• Flexible governance mechanisms
• Multi-stakeholder trust protocols

The Path Forward

Implementing these extensions requires coordination across the hardware security ecosystem:

• Standards bodies defining interfaces and protocols
• Hardware vendors implementing security features
• Cloud providers deploying verification systems
• AI developers integrating with safety mechanisms

We’re actively working with partners across this ecosystem to:

1. Extend existing standards for compute verification
2. Develop privacy-preserving audit protocols
3. Create reference implementations
4. Build practical deployment paths

Get Involved

This effort needs expertise across multiple domains:

• Hardware security architecture
• Standards development
• Machine learning verification
• Policy and governance frameworks

If you’re interested in contributing to this work, we’d love to hear from you, whether you’re:

• A hardware security expert
• An AI safety researcher
• A standards body participant
• A cloud infrastructure provider

Together, we can build the infrastructure needed for safe and scalable AI development.

---

This is the first post in a series exploring hardware security extensions for safe AI development. Future posts will dive deeper into technical implementations, practical deployments, and broader implications.