Policy Layer for Machine Learning Model Interaction Control
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Solution Overview
Problem
Existing machine learning systems face challenges in controlling and auditing interactions with internal or external models, leading to data leakage risks and difficulties in deploying organization-wide logic.
Innovation Solution
A policy layer is implemented to control interactions between applications and machine learning systems, applying comprehensive policies to all communications, including input and response modifications, and providing a centralized monitoring and audit mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning systems use actual input and response pairs to refine the underlying model, then model performance and efficiency are improved, but data leakage risk and confidentiality loss increase
Solution Approach 1:
The patent introduces a policy layer as an intermediary component between applications and machine learning systems. This policy layer intercepts, monitors, and controls all data flows to and from machine learning models, enabling organizations to prevent sensitive information from being leaked while still allowing legitimate data to be used for model refinement. The policy layer acts as a mediator that filters and manages data exposure.
2Ease of operation
If organizations deploy machine learning systems without centralized control, then ease of operation and adoption are improved, but visibility and audit capability deteriorate
Solution Approach 1:
The policy layer is designed as a universal control mechanism that can be applied across multiple applications and machine learning systems simultaneously. It provides centralized policy management, monitoring, and audit capabilities that work consistently across the entire organization, enabling both easy adoption and comprehensive visibility through a single unified interface.
3Reliability
If comprehensive policies are applied to control machine learning communications, then data security and governance are improved, but system complexity and deployment difficulty increase
Solution Approach 1:
The patent segments the control mechanism into a separate, modular policy layer that is distinct from both applications and machine learning systems. This segmentation allows policies to be developed, configured, and managed independently, reducing overall system complexity while maintaining comprehensive security control. The policy layer can be implemented as a standalone component that interfaces with existing systems without requiring fundamental architectural changes.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a policy layer for controlling how applications interact with internal or external machine learning models. One of the methods includes receiving an original input from an application. One or more input matching processes are performed to identify one or more matching input policy routines. One or more actions are performed according to the one or more matching input policy routines to generate a modified input. The modified input is provided instead of the original input to the machine learning system.