Verfahren und System zur Minderung von Unternehmensdatenlecks bei Anfragen auf Grosssprachenmodelle

Details

Veröffentlichungs-Nr.

EP4738168

Anmeldetag

22. September 2025

Veröffentlichung

6. Mai 2026

Rechtsraum

EP

Abstract

Unrestricted access to large language models (LLM) based services can lead to potential data leakages, especially for large enterprises providing products and services to clients that require legal confidentiality guarantees. However, a blanket restriction on such services is not ideal as these LLMs boost employee productivity. Objective of the present disclosure is to build a solution that enables enterprise employees to query such external LLMs, without leaking confidential internal and client information. QueryShield platform of the present disclosure is a platform that enterprises can use to interact with external LLMs without leaking data through queries. It detects if a query leaks data and rephrases it to minimize data leakage while limiting the impact to its semantics. A language model is chosen from a set of lightweight model candidates that are identified and fine tuned for this purpose using a huge dataset and evaluated using multiple metrics.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien