Unidirectional Trust Decision Making for IT Conversation Agents
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Solution Overview
Problem
Natural language interfaces struggle to efficiently process IT requests in a secure and trustworthy manner, as they lack effective methods to assess and adjust trust levels between users and machines, leading to inefficiencies and increased risk in interactions.
Innovation Solution
A system that extracts parameters from natural language requests, determines the risk associated with the request, and adjusts the conversation pattern based on calculated trust levels by using speech recognition, concept expansion, and policy execution, allowing for dynamic trust calculation and behavioral modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If natural language interfaces process IT requests without trust assessment, then processing speed is improved, but security and reliability deteriorate
Solution Approach 1:
The system performs trust level calculation and risk assessment before executing the IT request. The conversation agent calculates a trust level for the user based on historical interaction data and user profiles, then uses this pre-assessed trust level to determine appropriate security measures and response strategies, eliminating the need for time-consuming iterative verification during request processing.
Solution Approach 2:
The system automatically calculates trust levels and adjusts conversation patterns without human intervention. The conversation agent autonomously monitors interaction quality, updates trust levels based on user behavior patterns, and modifies its communication strategy accordingly, enabling the system to self-regulate security and efficiency without requiring manual security reviews for each request.
2Measurement precision
If iterative communication is used between human and machine, then understanding accuracy is improved, but time consumption increases
Solution Approach 1:
The conversation agent dynamically adjusts its communication behavior based on the calculated trust level. For high-trust users, the agent uses more efficient, direct communication patterns with fewer iterative exchanges. For low-trust users, the agent employs more verbose, explanatory communication with additional verification steps. This dynamic adaptation optimizes the balance between understanding accuracy and time consumption for each user context.
3Reliability
If security measures are increased for all users, then reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system applies different security measures and conversation patterns to different users based on their individual trust levels. High-trust users experience streamlined, convenient interaction with minimal security friction, while low-trust users receive enhanced security monitoring and verification. This localized approach ensures that security is tailored to each user's demonstrated reliability rather than applying uniform restrictions to all users.
Data Source
AI summary
A method and system of processing an information technology (IT) electronic request is provided. The electronic request is received in natural language from a user. Parameters of the electronic request are extracted. A risk of the electronic request is determined. A policy based on the parameters and the risk of the electronic request is determined and executed. A level of trust between the user and the computer device is calculated based on the determined risk and an outcome of the execution of the policy. A conversation pattern of the computer device toward the user is adjusted based on the calculated level of trust.