AI Pipeline Rule Engine for Secure Model Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprises face challenges in using large language models (LLMs) due to concerns over 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) and AI model routing, allowing enterprises to define granular management rules for secure and efficient use of third-party AI services, with a gateway for easy integration and customizable rule enforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If enterprises use third-party AI services, then AI functionality is improved, but sensitive data exposure risk increases
Solution Approach 1:
The patent introduces a gateway as an intermediary component between enterprise applications and third-party AI services. This gateway enforces management rules that prevent sensitive data from being transmitted to external AI services, thereby enabling enterprises to utilize AI functionality while protecting sensitive data from exposure risks
2Device complexity
If a single AI model is used, then device complexity is reduced, but productivity decreases due to inappropriate model handling
Solution Approach 1:
The patent implements dynamic model routing capabilities that allow the system to automatically select the most appropriate AI model based on the specific query characteristics, sensitivity level, and enterprise policies. This dynamic approach enables multiple models to be utilized efficiently without requiring complex manual routing configurations, thereby improving productivity while maintaining manageable complexity
3Reliability
If granular management rules are implemented, then data security is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal gateway component that handles multiple functions including DLP rule enforcement, model routing, query modification, and logging. By consolidating these diverse security and routing functions into a single multi-functional gateway, the system achieves granular data security control without proportionally increasing overall system complexity
Data Source
AI summary
The invention includes a UI that allows addition of functional placeholders to an AI pipeline, and a rules engine that chooses pipeline objects during or before AI pipeline execution. The rules engine can select pipeline objects meeting the same function type and object rules of the functional placeholder. The rules engine also applies customizable security rules and artificial intelligence (AI) model routing to queries. Remedial actions are taken when the rule evaluation exceeds a threshold. The remedial actions include transforming the query by replacing sensitive information with a reversible placeholder. The actual result can be modified by reversing the placeholder and redacting additional sensitive information. 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.