Rules Engine for Secure AI Pipeline Input Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprises face challenges in securely using large language models (LLMs) due to concerns about sensitive data exposure and inefficiencies in model routing, leading to hesitation in adopting AI technologies.
Innovation Solution
A rule engine that combines data loss prevention (DLP) with AI model routing, allowing enterprises to define granular management rules for secure AI service usage, route queries intelligently, and provide a gateway for easy integration with existing applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If enterprises use LLMs to assist with tasks, then productivity is improved, but sensitive data may be exposed or leaked
Solution Approach 1:
The patent introduces a gateway as an intermediary component between enterprise applications and AI service providers. This gateway enforces management rules that prevent sensitive data from being transmitted to external AI services while still allowing legitimate business tasks to proceed. The gateway acts as a mediator that filters and controls data flow, enabling productivity gains without direct exposure of sensitive information to third-party services.
Solution Approach 2:
The system performs preliminary evaluation of inputs against management rules before they reach the AI service. By pre-assessing whether content contains sensitive information or violates enterprise policies, the system can block problematic requests in advance, redact sensitive data, or route them appropriately. This preliminary action prevents sensitive data exposure before it occurs, while still allowing safe, productive interactions to proceed.
2Device complexity
If enterprises use a single AI model, then device complexity is reduced, but cost efficiency and task matching deteriorate
Solution Approach 1:
The patent implements dynamic model routing that automatically selects the most appropriate AI service provider based on the specific input characteristics, enterprise rules, and current conditions. Rather than statically assigning a single model, the system dynamically evaluates each request and routes it to the optimal provider, whether that's a third-party service or an on-premises model. This dynamic approach improves cost efficiency and task matching without requiring enterprises to manually manage multiple models.
Solution Approach 2:
The gateway serves multiple functions simultaneously: it enforces data loss prevention rules, performs model routing, evaluates inputs against management policies, and manages communications with various AI service providers. By consolidating these diverse functions into a single universal component, the system achieves cost efficiency and intelligent routing without significantly increasing overall system complexity for the enterprise.
3Reliability
If enterprises implement strict data protection measures, then data security is improved, but adoption of AI technologies slows down
Solution Approach 1:
The system enables AI services to serve themselves by automatically evaluating inputs against management rules and routing requests appropriately without requiring manual security reviews. The gateway autonomously determines whether content should be blocked, redacted, or forwarded to specific providers based on pre-configured enterprise policies. This self-service capability maintains strong data security through automated enforcement while keeping technology adoption rapid by eliminating manual approval bottlenecks.
Data Source
AI summary
The invention provides a rules engine that evaluates, modifies, and dynamically routes inputs to AI models based on provider restrictions and management rules. Provider restrictions are stored. Based on management rules and the detected provider restrictions, the input can be modified with contextual placeholders and prompts can be injected for input into the destination model. Reversal of the modifications can occur to the outputs. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.