Hybrid Bot Framework for Secure Enterprise Integration
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Solution Overview
Problem
Existing automated service integration solutions for enterprises face challenges with on-premise solutions being resource-intensive and cloud-based solutions struggling with security and confidentiality, particularly in maintaining personally identifiable information (PII).
Innovation Solution
A hybrid bot framework that combines on-premise components with cloud-hosted services, utilizing an extended finite state machine to manage contexts and transitions, and masking sensitive information to ensure security and confidentiality, while enabling multilingual interactions through various channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If on-premise solutions are implemented, then security and confidentiality of information are improved, but computing resources and cost increase
Solution Approach 1:
The system segments the automated service architecture into on-premise components (session manager, finite state machine, response generator) that handle sensitive data and context management, and cloud-based components that provide AI processing power. This segmentation allows security-critical functions to remain locally while leveraging cloud computing resources.
Solution Approach 2:
The on-premise session manager acts as an intermediary between cloud-based AI services and internal systems, managing sensitive information locally while enabling cloud processing. The finite state machine serves as another intermediary layer that handles context transitions without exposing sensitive data to cloud services.
2Quantity of substance
If cloud-based solutions are implemented, then computing resources are reduced, but security and confidentiality of information deteriorate
Solution Approach 1:
The system extracts sensitive information (personally identifiable information) from data sent to cloud services. The on-premise session manager removes or masks sensitive data before transmitting requests to cloud-based AI services, ensuring that only non-sensitive information is exposed to external services.
3Adaptability or versatility
If enterprise integration is performed, then service capability is improved, but integration complexity increases
Solution Approach 1:
The finite state machine implementation provides a universal framework that can be applied across different enterprise contexts and service types. The session manager serves multiple functions including context management, sensitive data handling, and service coordination, reducing the need for separate integration components for each service.
Data Source
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AI summary
Implementations are directed to receiving communication data from a device, the communication data including data input by a user of the device, determining a context based on an extended finite state machine that defines contexts and transitions between contexts, transmitting a service request to at least one cloud-hosted service, the service request being provided at least partially based on masking sensitive information included in the communication data, receiving a service response from the at least one cloud-hosted service, the service response including one or more of an intent, and an entity, determining at least one action that is to be performed by at least one back-end source system based on the service response, providing a response at least partially based on an action results received from the at least one back-end source system, and transmitting the result data to the device.