Abstract
Using generative artificial intelligence with sensitive data may present challenges, as transmitting personally identifiable information or protected health information to third-party providers can introduce security risks, and some data masking techniques can reduce reasoning capabilities. A described system uses a proxy, masking layer that can intercept data within an enterprise's secure perimeter. This layer can substitute sensitive strings with persistent, structured semantic tokens that may be enriched with non-sensitive metadata hints to help preserve context. An external artificial intelligence can perform reasoning on this abstracted data, and its tokenized response can be re-hydrated into readable text on a client device (e.g., a smartphone, computer, or wearable device). This approach may allow third-party models to reason on proprietary information without direct access to the underlying plaintext data, which can assist organizations in managing data sovereignty while maintaining functional utility.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
N/A, "Method for Secure AI Grounding using Semantic Token Substitution and Client-Side Re-Hydration", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9845