AI Model Routing Gateway with DLP Rule Enforcement
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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 hesitancy 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 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 they can access advanced AI capabilities, but sensitive data may be exposed to external services
Solution Approach 1:
The patent introduces an enterprise gateway as an intermediary component between internal applications and external AI services. The gateway enforces management rules including data loss prevention (DLP) policies, scanning inputs for sensitive information before transmission and filtering outputs for potential data leaks. This intermediary structure allows enterprises to access third-party AI capabilities while maintaining control over sensitive data through the gateway's rule enforcement mechanisms.
2Ease of manufacture
If enterprises use a single AI model, then implementation is simple, but cost efficiency and task matching are poor
Solution Approach 1:
The patent implements dynamic model routing that automatically selects appropriate AI models based on the characteristics of each input query and enterprise-defined management rules. The system evaluates inputs against multiple models and routing criteria, dynamically directing queries to the most suitable model for the task. This dynamic approach replaces static single-model deployment with adaptive multi-model orchestration, improving cost efficiency and task matching while maintaining simple implementation through centralized gateway control.
3Reliability
If enterprises implement comprehensive data protection rules, then data security is improved, but AI service adoption is delayed
Solution Approach 1:
The patent implements preliminary action by pre-configuring management rules, DLP policies, and model routing criteria in the enterprise gateway before AI service deployment. The system pre-establishes security protocols, data classification rules, and model selection criteria that automatically apply to all AI queries. This preliminary configuration eliminates the need for time-consuming security assessments and rule adjustments during deployment, enabling enterprises to rapidly adopt AI services with pre-vetted security measures in place.
Data Source
AI summary
The invention provides a rules engine that applies customizable security rules and artificial intelligence (AI) model routing to queries. The rules are evaluated with respect to the query, an anticipate result of the query, and the actual result. 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.