Lorena Sundqvist

Private mode settings alone won't stop AI providers from retaining your conversations.

Self-hosted inference and encrypted enclaves make prompt exposure structurally impossible.

Cryptographic controls, not policy, must exclude operators from AI inference data.

Privacy mode restricts how vendors use your data.

Professionals must map which data enters AI tools and who reviews outputs before client delivery.

Hardware boundaries must protect every step of AI inference, from prompts to outputs.

Quasi-identifiers in AI telemetry can uniquely fingerprint individuals.

Federated learning gains a drift-correction mechanism for non-uniform client data.

Build purpose boundaries into your AI pipeline from data collection onward.

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

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

TEE overhead concentrates at memory boundaries, not compute.

Plaintext model weights in memory can only be protected by hardware, not policy.

Cryptographic logs can prove a model's training history wasn't altered after release.

Valid tokens become dangerous weapons when presented outside their intended time window.

Quantum computers threaten AI traffic captured today and decrypted later.

Kata Containers protects workloads from the infrastructure itself using hardware-encrypted memory.

OS-level adversaries can extract secrets from SGX enclaves through deterministic side channels.

NVIDIA's H100 encrypts GPU memory to shield AI model weights and data during computation.