Securing the Data Fueling AI's Growth
Protecting sensitive data is critical for organizations using AI, as AI relies heavily on data. Methods like confidential computing, hardware-based security features, and federated learning can help secure AI data while still allowing models access to the data they need.
Summary
- AI relies on data to function, so securing that data is important, especially when it's sensitive personal information.
- Confidential computing encrypts data and provides authentication to ensure only authorized systems/individuals can access it. This allows sharing and collaboration while preserving confidentiality.
- Intel's 4th gen Xeon processors have built-in security features like Intel SGX and Intel TDX that help protect data in use, storage, and transit. SGX isolates data at the application level, while TDX isolates entire virtual machines.
- Additional protections like remote attestation and the Intel Trust Authority service provide assurance that systems meet security standards before sharing sensitive data.
- Federated learning allows organizations to collaborate and aggregate data for analysis without having to centralize/expose the raw data.
- A layered security approach across the technology stack is needed to enable organizations to confidently deploy AI applications while meeting compliance requirements.