AI Model Gateway Integration for Secure Zero-ETL Data Clouds
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Solution Overview
Problem
Existing AI model integration across cloud-based platforms is inefficient and insecure, lacking standard methods for securely using AI models with proprietary data, leading to unstable and costly implementations.
Innovation Solution
A system and method for efficiently training AI models within an organization's data cloud, using a zero-ETL framework to harmonize data, import AI model inferences, and integrate them securely through standardized contracts and customization engines, enabling real-time, trusted AI experiences across applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are integrated across cloud-based platforms using traditional methods, then AI functionality can be provided, but the integration is inefficient and insecure, lacking standard methods for securely using AI models with proprietary data
Solution Approach 1:
The patent introduces an AI model gateway as an intermediary component that sits between the AI model service provider and the organization's data cloud. This gateway establishes secure connections, manages authentication, and handles data transmission protocols, thereby providing standardized and secure AI model integration without requiring complex custom implementations for each connection.
Solution Approach 2:
The AI model gateway is designed as a universal interface that can work with multiple different AI model service providers and various types of proprietary data. It provides a standardized method for integrating different AI models (both generative and predictive) across cloud-based platforms, eliminating the need for provider-specific integration code and security implementations.
2Productivity
If AI models are trained and utilized using proprietary data without standardized security methods, then AI functionality is achieved, but implementations become unstable and result in cost overruns
Solution Approach 1:
The system performs preliminary actions by establishing secure connections and validating data protocols before actual AI model training and utilization begins. The AI model gateway pre-configures security parameters, authentication mechanisms, and data transmission standards, ensuring that subsequent AI operations proceed smoothly without stability issues or cost overruns from rework.
Solution Approach 2:
The AI model gateway implements feedback mechanisms that monitor the integration process, tracking performance metrics, security compliance, and operational stability. This continuous feedback allows for real-time adjustments and ensures that AI model training and utilization maintain both efficiency and stability throughout the process.
Data Source
AI summary
Apparatus and method for integrating external AI services. For example, one embodiment of a method comprises: preparing training data received from various data streams on the cloud-based application service, wherein preparing includes categorizing, filtering, and curating data from the data streams; generating source data model objects (DMOs) based on training data; providing the source DMOs to the external AI service over a secure communication channel, the external AI service to register an AI model based on the source DMOs and to generate a corresponding AI model endpoint; executing an AI model builder on the cloud-based application service, the AI model builder to generate an AI model reference configurable with connection information to communicate with the AI model endpoint, the AI model builder configurable to automatically trigger an inference when data mapped to an input of the AI model is changed in one or more of the source DMOs.