Private AI Review — Page 4

Vectors marked deleted stay recoverable and searchable inside production databases.

A buyer's checklist for pressure-testing differential privacy claims before signing a contract.

API output design is a security decision, not a feature choice.

Cryptographic proof replaces procedural assurance for deleting personal data from trained models.

Data residency requires controlling six deployment surfaces, not just where the model runs.

Hardware-enforced privacy protects federated learning where cryptography alone falls short.

Encryption protects data between GPUs, but the performance cost on current hardware is steep.

NVIDIA's closed-source driver creates a trust gap in confidential GPU security.

Researchers show how to encrypt GPU memory channels in confidential AMD VMs.

Verify a GPU's identity and firmware integrity before processing sensitive AI workloads.

Attestation and key release, not model loading, drive confidential inference cold starts.