AI Query Routing Rules Engine for Secure Endpoint Selection
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Solution Overview
Problem
Current systems lack the capability to dynamically and intelligently route user requests to the appropriate AI endpoint based on the content and context of the request, leading to inefficiencies and potential security breaches when personal and enterprise AI systems are used interchangeably.
Innovation Solution
A rules engine that evaluates user requests using management rules, user preferences, and contextual details to determine the most appropriate AI endpoint, incorporating a specialized routing AI Model for complex decisions and ensuring compliance with enterprise policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users access both personal and managed AI model endpoints through a unified interface, then user convenience and ease of operation are improved, but the risk of misrouting requests and security breaches increases
Solution Approach 1:
The patent introduces a rules engine as an intermediary component between the user interface and AI model endpoints. This rules engine evaluates user requests against management rules and contextual factors before routing to appropriate endpoints, acting as a mediator that prevents direct misrouting while maintaining unified interface access. The rules engine analyzes request characteristics, user profiles, and policy constraints to determine safe routing decisions.
Solution Approach 2:
The system performs preliminary evaluation and scoring of user requests before actual routing occurs. Management rules are applied in advance to assess request appropriateness for different endpoint types, and contextual factors are evaluated beforehand to prevent security issues. This preliminary action ensures that security checks are performed before requests reach vulnerable endpoints.
2Productivity
If enterprise AI resources are made accessible through a unified interface, then resource utilization efficiency is improved, but the consumption of enterprise computational power for personal requests increases
Solution Approach 1:
The rules engine implements a feedback mechanism that continuously monitors request patterns, endpoint performance, and resource consumption. Based on this feedback, the system dynamically adjusts routing decisions to optimize resource utilization. The system learns from historical data to distinguish between legitimate enterprise requests and personal requests, improving routing accuracy over time and reducing wasted computational power.
Solution Approach 2:
The system changes routing parameters dynamically based on request characteristics, user context, and resource availability. Instead of static routing rules, the system evaluates multiple parameters including request type, user role, time of day, and current endpoint load to make intelligent routing decisions. This parameter-based dynamic routing ensures enterprise resources are allocated efficiently to appropriate requests.
3Measurement precision
If a rules engine evaluates requests based on multiple management rules and contextual factors, then routing accuracy is improved, but system complexity increases
Solution Approach 1:
The rules engine is segmented into modular components that handle different aspects of request evaluation separately. Each management rule and contextual factor is processed as an independent module, making the complex evaluation process more manageable and maintainable. This segmentation allows the system to achieve high routing accuracy through multiple specialized evaluation modules rather than one monolithic complex system.
Solution Approach 2:
The rules engine is designed as a universal evaluation system that handles multiple types of management rules and contextual factors through a single unified framework. This multi-functional approach reduces overall system complexity by consolidating diverse evaluation functions into one versatile component rather than requiring separate systems for each evaluation type.
4Reliability
If personal requests are routed to managed AI models, then enterprise data security is compromised, but if strict routing controls are implemented, then user convenience decreases
Solution Approach 1:
The patent replaces mechanical routing controls (such as mandatory user selections or rigid access policies) with an intelligent automated evaluation system. The rules engine automatically analyzes requests and makes routing decisions based on management rules and contextual factors, eliminating the need for users to manually control routing while maintaining security. This substitution of automated intelligence for mechanical controls preserves user convenience through transparency and ease of use.
Solution Approach 2:
The system performs self-service routing evaluation by automatically assessing each request's appropriateness for managed or personal endpoints without requiring user intervention. The rules engine independently evaluates security requirements, user permissions, and request characteristics to make routing decisions, freeing users from complex security management while ensuring data protection through automated enforcement.
Data Source
AI summary
The invention provides a rules engine that manages user requests within an interface integrating multiple AI platforms and AI Models. Upon receiving a query, the engine assigns scores based on factors like management rules, user preferences, and contextual information. Determinative scores, such as those enforcing strict enterprise policies, can override others, leading the engine to block or reroute the query. If no score is determinative, the engine forwards the query and associated prompts to a specialized routing AI Model for contextual analysis. Based on this analysis, the rules engine directs the query to the most appropriate AI Model or defaults to the user-designated AI Model. This system balances user intent with rule enforcement, optimizing query processing across various AI platforms while ensuring compliance with enterprise guidelines.