Abstract
Large language models (LLMs) and other types of generative artificial intelligence can be used in a wide variety of business applications. However, there is a possibility of data leakage from LLM responses when an LLM is used in shared multi-tenant environments where each tenant has respective private datasets. Deploying individual adapter layers for each tenant can provide data isolation. However, such implementations can be complex and costly. This disclosure describes techniques to create and maintain a single model that can serve multiple tenants, with security controls for multi-tenancy services to isolate customer data efficiently. Data for different tenants is signed with their respective tenant-specific keys and is then appended with the tenant-specific signature prior to training/tuning a model or use by the model at inference time. When a business application of a particular tenant requests a response from the LLM, the response is generated using the adapter layer. The response includes data citations that are verified prior to the response being provided to the business application. The verification is based on the tenant-specific signature in the citation to ensure that only data that belongs to the particular tenant that requested the response is included.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Namer, Assaf; Miller, Jim; Kulkarni, Prashant; Vagts, Hauke; Bisson, Jason; and Maltzman, Brandon, "Tenant Data Security for LLM Applications in a Multi-Tenancy Environment", Technical Disclosure Commons, (January 12, 2024)
https://www.tdcommons.org/dpubs_series/6596