Context-Aware Augmented Analytics for Enterprise Data Privacy
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Solution Overview
Problem
Conventional AI-based foundational models often provide generalized summaries instead of direct answers, suffer from inaccuracies, hallucinations, and bias due to stale training data, and raise privacy concerns by requiring sensitive enterprise data for relevant responses.
Innovation Solution
An enterprise-specific context-aware dialogue management system that uses an information model to provide contextual and supplemental data to a foundational model, ensuring data privacy and accuracy by limiting access to enterprise-specific data and using techniques like k-anonymity and masking to prevent data breaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI foundational models use extensive training data to improve accuracy and relevance, then response accuracy improves, but data privacy risks increase
Solution Approach 1:
The system segments data processing by separating enterprise-specific sensitive data from the AI model training process. The information model queries only necessary enterprise data through controlled interfaces, preventing direct exposure of sensitive information to the foundational model while maintaining response accuracy through context-aware augmentations.
Solution Approach 2:
An information model acts as an intermediary between enterprise data and the foundational AI model. This mediator queries enterprise data through controlled interfaces, processes only necessary information, and provides augmented context to the AI model, thereby preventing direct data exposure while maintaining response accuracy.
2Adaptability or versatility
If AI models provide detailed enterprise-specific responses, then relevance improves, but hallucinations increase
Solution Approach 1:
The system incorporates feedback mechanisms where the information model validates AI-generated responses against enterprise data constraints and guidelines. This feedback loop allows the system to correct hallucinations by comparing generated responses with actual enterprise data patterns and correcting inaccuracies before delivery to the user.
Solution Approach 2:
The system applies local quality by providing enterprise-specific context and constraints only where needed in the response generation process. The foundational model receives targeted augmentations from the information model specific to each query context, ensuring relevance while maintaining overall reliability through localized quality control.
3Measurement precision
If the system accesses enterprise data for contextual accuracy, then response accuracy improves, but data security risks increase
Solution Approach 1:
The system extracts only the necessary information from enterprise data stores through the information model's controlled queries. By taking out only the specific data needed for each query rather than exposing the entire dataset to the AI model, the system maintains response accuracy while minimizing data security risks through selective data extraction.
Data Source
AI summary
Data characterizing a query can be received. A dataset specific to an enterprise and a parameter set specific to the enterprise can be determined using an information model. A query response using a foundational model, the dataset specific to the enterprise, and the parameter set specific to the enterprise can be determined. The query response can be provided to a user. Related apparatus, systems, techniques, and articles are also described.