Adaptable Framework for Generative AI Query Management
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Solution Overview
Problem
Businesses face challenges in standardizing security, access controls, and observability for generative AI systems due to varying vendor-specific implementations, making it difficult to detect and prevent misuse, which poses risks to entity security and workflow integrity.
Innovation Solution
An adaptable framework that integrates generative AI systems by managing queries and responses through a request management system, implementing security controls, governance, and observability protocols, using a ruleset to filter content and authenticate systems, and employing machine learning models to update these protocols dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If businesses rely on individual generative AI systems for security controls and governance, then each system can operate with its own vendor-specific standards, but this results in a lack of standardization across the organization and difficulty in detecting and preventing misuse
Solution Approach 1:
The patent introduces an intermediary layer (gateway or proxy) between client systems and generative AI systems that enforces standardized security controls, authentication, and monitoring. This intermediary captures and standardizes interactions with multiple vendor-specific systems, allowing the organization to maintain consistent security policies while supporting diverse AI systems.
Solution Approach 2:
The framework provides universal security controls and governance mechanisms that work across multiple vendor-specific generative AI systems. By creating a common interface and standardized protocols at the organizational level, the system achieves multi-functionality that handles authentication, authorization, and monitoring uniformly across different AI vendors.
2Reliability
If businesses implement standardized security protocols across all generative AI systems, then security control consistency is improved, but this increases the complexity of integrating and managing multiple vendor-specific systems
Solution Approach 1:
The intermediary layer absorbs the complexity of integrating multiple vendor-specific systems by providing a standardized interface to client systems. It handles the variations in vendor protocols and implementations internally, shielding the organization from integration complexity while maintaining consistent security controls.
Solution Approach 2:
The framework segments the system into distinct layers: client systems, the intermediary gateway/proxy, and vendor-specific generative AI systems. This segmentation allows each layer to operate independently with its own complexity management, reducing the overall integration burden on the organization.
3Ease of operation
If businesses allow individual generative AI systems to control their own security and governance, then system autonomy is maintained, but this creates risk because adherence to security standards is left to individual systems rather than being centrally controlled
Solution Approach 1:
The framework implements centralized monitoring and observability that provides feedback on the behavior of individual generative AI systems. The intermediary layer captures usage patterns, detects anomalies, and enforces security policies by monitoring system outputs and interactions, creating a feedback loop that maintains security without eliminating system autonomy.
Solution Approach 2:
The intermediary layer acts as a security-enforcing mediator that allows individual systems to operate autonomously while maintaining centralized security control. It intercepts and validates interactions, ensuring compliance with organizational security standards without directly managing the internal operations of each AI system.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for managing generative artificial intelligence (AI) queries from client systems and responses to the queries from generative AI systems. A request management system may provide an adaptable framework for receiving, managing, monitoring, and/or controlling generative AI queries received from client systems seeking generative AI content. The request management system may authenticate client systems and generative AI systems. The request management system may also administer security and observabilities protocols to the queries and responses. The request management system may identify a ruleset that includes one or more conditions that indicate whether the queries and responses may be forwarded to the generative AI systems and client systems, respectively. This may provide enterprise computing control over interactions between client devices and generative AI systems.