Cryptographic Operator Exclusion as a First-Class Design Requirement for AI Assistants
Cryptographic controls, not policy, must exclude operators from AI inference data.
Lorena Sundqvist
Section
Confidential AI Inference at Scale
11 stories in Confidential AI Inference at Scale.
Mapping the TEE Landscape for AI Inference Workloads
Hardware boundaries must protect every step of AI inference, from prompts to outputs.
Lorena Sundqvist
Architecting a Zero-Operator-Access AI Assistant on Confidential Computing Infrastructure
Hardware-rooted enclaves keep AI model data encrypted even from cloud operators.
Preston Nardone
Why Policy-Based Operator Restrictions Fail Enterprise Confidentiality Requirements
Policy cannot prevent data breaches that occur during model inference, only after.
Preston Nardone
How TEE Attestation Lets Users Verify No Operator Can Read Their Prompts
Users can cryptographically verify their AI prompts stay encrypted during processing.
Preston Nardone
Cold Start Attestation Latency in Serverless Confidential Inference
Attestation and key release, not model loading, drive confidential inference cold starts.
Kian Vance
Latency and Throughput Overhead of TEE-Protected Inference
TEE overhead concentrates at memory boundaries, not compute.
Lorena Sundqvist
Multi-Party Inference with Confidential Computing
Trusted hardware lets hospitals and AI firms compute together without exposing secrets.
Kian Vance
Model Weight Protection Inside Confidential Inference Servers
Plaintext model weights in memory can only be protected by hardware, not policy.
Lorena Sundqvist
Encrypting Prompts and Responses in Transit to a TEE
Hardware-sealed enclaves protect LLM prompts from cloud operators and hypervisors during inference.
Kian Vance
Threat Model for a Confidential LLM Inference Endpoint
Confidential computing alone cannot prevent all attacks on encrypted LLM inference.
Kian Vance