Auditable Zero-Retention AI with Attestation Receipts and Differential Privacy-Bounded Learning Updates

This report describes an architecture specification for running artificial intelligence (AI) large language model (LLM) workloads on regulated data with auditable non-persistence guarantees within an explicitly defined trust boundary. The core idea is to confine raw inputs to an ephemeral, Random Access Memory (RAM)-only execution environment and to emit a cryptographic attestation chain, plus a per-session “privacy receipt,” providing verifiable evidence that no non-volatile persistence occurred within the protected boundary. When learning is permitted, the system exports only differential privacy (DP) bounded numeric update artifacts (e.g., noised parameter deltas) together with an attestation-bound DP tuple. Transcripts, images, and un-noised gradients are discarded. A secure aggregator/verifier then admits updates only after validating the attestation chain and privacy budget constraints, and retains receipts only. The report introduces a threat model, trust-boundary assumptions for use with managed model endpoints, and practical design patterns for integrating these guarantees into regulated workflows. This report specifies the architecture; a reference implementation and empirical evaluation are planned as subsequent work.