Generative AI Access Control for Confidential Data Training
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Solution Overview
Problem
Generative AI models trained with limited access to confidential corporate data produce less valuable outputs, and corporations are hesitant to share proprietary data due to security concerns, leading to inefficiencies and potential data leaks.
Innovation Solution
Implement systems and methods for managing generative AI engines that allow for weighted training and querying based on proprietary and public data, using differential analysis and access management techniques to maintain data security and confidentiality, enabling accurate output generation from corporate data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generative AI models are trained with confidential corporate data, then output accuracy and value are improved, but data security and confidentiality are compromised
Solution Approach 1:
The patent introduces a data exchange management system as an intermediary layer between corporate data sources and generative AI models. This system includes data exchange policies, access controls, and monitoring mechanisms that enable secure data sharing while maintaining confidentiality. The intermediary manages the trade-off by controlling what data is shared, with whom, and under what conditions, thus improving model accuracy without compromising security.
Solution Approach 2:
The system dynamically adjusts data sharing parameters such as access permissions, data transformation levels, and exchange conditions based on security requirements and model training needs. By changing these parameters, the system can optimize the balance between providing sufficient data for accurate model outputs and maintaining appropriate security constraints to protect confidential information.
2Measurement precision
If corporations share proprietary data with external entities, then model training quality is improved, but risk of data leaks increases
Solution Approach 1:
The patent implements local quality control by applying different security measures and data transformation levels to different portions of proprietary data based on sensitivity classifications. Not all data is treated uniformly - critical confidential data receives higher protection while less sensitive data can be shared more freely. This enables model training with sufficient data quality while minimizing exposure of highly sensitive information.
Solution Approach 2:
The system performs preliminary actions by establishing data exchange policies, access controls, and security measures before data is actually shared with external entities. Data is prepared, classified, and protected in advance according to predefined security requirements, reducing the risk of data leaks during the training process while still enabling high-quality model training.
3Reliability
If access to confidential data is restricted, then data security is maintained, but model output value decreases
Solution Approach 1:
The patent implements dynamic access control where data sharing permissions are not static but adapt based on the specific training needs, security requirements, and context of each data exchange. The system can dynamically adjust what data is accessed, by whom, and for what purposes, allowing flexible optimization between security maintenance and model value enhancement without rigid restrictions.
Data Source
AI summary
Embodiments of the disclosed systems and methods provide for techniques for managing generative AI services that allow for generative AI models to better use confidential, proprietary, sensitive, and/or otherwise managed data while maintaining the security of such data. Various embodiments may provide for generative AI model training based on variable and/or otherwise tuned reliance on input training data sets and queries may leverage this differential training. Differential analysis techniques may be further employed to identify confidential data included in model outputs. In further embodiments, query and/or output labeling may be used in connection with access rights management techniques to manage access to proprietary information that may be included in model outputs. Embodiments of the disclosed systems and methods may be used in a variety of applications, use cases, and/or contexts, including in personalized medicine and differential diagnosis applications.