Anouk Mercer

Five connected controls stop compromised AI agents before they cause harm.

Technical controls prevent data exposure where contracts can only promise to prohibit it.

Encryption alone can't prevent vendors from reading your prompts during inference.

Noise added during training protects individual privacy without exposing sensitive data.

Privacy built into AI systems from the start, not patched on later.

Runtime enforcement separates AI policy control from the model itself.

Hardware-enforced enclaves prevent cloud providers from reading AI prompts during inference.

Consumer AI tools' standard terms make client data retention indefensible under ethics rules.

Synthetic data's privacy value depends entirely on generation method, not marketing claims.

Soft deletion in vector databases leaves personal data physically intact and recoverable.

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

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

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

Attestation proves what code is running; TLS only proves you reached the right domain.

Configure Ollama's network and model settings to keep sensitive data fully local.

Configuration gaps and overpermissioned access drive most data leaks through enterprise AI copilots.

Choosing the right FHE scheme determines whether ML inference stays private or stays feasible.

AWS isolates sensitive data processing below the operating system.

Hardware isolation alone won't protect your secrets without careful boundary design.

Patching confidential computing requires tearing down running enclaves to update attestation claims.

Hardware-rooted sealing encrypts enclave secrets for storage, but binds them to a single processor.

Intel centralizes security in firmware, while AMD distributes it throughout the hardware.

Zero-trust moves security from perimeters to every workload request in AI inference.