Artificial intelligence is transforming every industry, but it comes with a critical challenge: how do you leverage sensitive data without exposing it? Confidential computing offers a powerful answer.
The Problem
Traditional cloud computing requires trusting the infrastructure provider with your data. Even with encryption at rest and in transit, data must be decrypted in memory during processing — creating a window of vulnerability.
For AI workloads, this is particularly concerning. Training and inference on private datasets — medical records, financial transactions, proprietary business data — means exposing that data to potential breaches, insider threats, or regulatory violations.
How Confidential Computing Helps
Confidential computing uses hardware-based Trusted Execution Environments (TEEs) to protect data even while it's being processed. The data remains encrypted in memory, and the processing environment is isolated from the host operating system, hypervisor, and other tenants.
This means:
- No one can access your data — not even the cloud provider or infrastructure operator.
- Cryptographic attestation proves that the code running inside the enclave hasn't been tampered with.
- Regulatory compliance becomes easier when you can demonstrate that data is never exposed.
Confidential AI at Privasys
At Privasys, we've built our entire platform around confidential computing. Our Confidential AI solution combines secure enclaves on CPUs and GPUs to guarantee privacy for the full AI pipeline — from data ingestion and RAG to model inference.
We believe privacy isn't a feature. It's the foundation.
Want to learn more? Contact us to discuss how confidential computing can protect your AI workloads.