AI Agent Data Categorization With Secure Consent Mediation
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Solution Overview
Problem
Existing data management systems lack a structured approach to categorizing and managing personal data, leading to data clutter, difficulty in accessing specific information, and a lack of personalized experiences due to inadequate data classification based on nature and origin, posing challenges in data privacy and security.
Innovation Solution
A system and method using artificial intelligence (AI) agents within a secure cloud-based enclave for categorizing personal data into distinct sets, enabling secure, personalized recommendations, and managing interactions between AI agents while preserving user privacy through a dynamic consent framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If personal data is stored without structured categorization, then data volume can be maintained, but data organization and accessibility deteriorate
Solution Approach 1:
The patent segments personal data into distinct categories (profile data, interaction data, preference data, behavioral data, contextual data) with specific storage locations and access protocols. This segmentation enables systematic organization while maintaining efficient retrieval mechanisms, resolving the contradiction between data organization complexity and accessibility ease.
2Adaptability or versatility
If AI agents interact freely to provide personalized services, then service personalization improves, but data privacy and security deteriorate
Solution Approach 1:
The patent introduces a secure enclave as an intermediary between AI agents and user data. The enclave authenticates agents, manages data access permissions, and processes requests without exposing raw data to untrusted agents. This mediator enables personalized services while mitigating privacy risks through controlled data sharing.
Solution Approach 2:
Different AI agents are granted different access permissions to different data categories based on their specific functions and trust levels. The system implements fine-grained access control where profile data may be accessible to one agent while behavioral data remains restricted, allowing personalized services tailored to each agent's authorized scope.
3Reliability
If data is stored without structured categorization, then storage simplicity is maintained, but data privacy and security management becomes difficult
Solution Approach 1:
The patent divides data into distinct categories with specific security requirements, storing each in dedicated locations within the secure enclave. This segmentation enables targeted security measures for sensitive data types while maintaining overall system reliability through systematic data organization.
Solution Approach 2:
The system performs preliminary authentication and authorization actions before allowing any data access. The secure enclave pre-establishes trust relationships and access policies, ensuring that only verified agents can access specific data categories, thereby enhancing security before data handling begins.
Data Source
AI summary
System and method for managing interaction of data between Artificial Intelligent (AI) agents within a secure cloud-based enclave are disclosed. The method comprises initiating, by a single AI agent, an interaction request with a shared AI agent. The shared AI agent triggers a negotiation and authorization process to the single AI agent based on the interaction request. The negotiation and authorization process determines whether the single AI agent is eligible to interact with the shared AI agent. The shared AI agent receives user data from the single AI agent when the single AI agent is eligible to interact with the shared AI agent. The shared AI agent categorizes the user data into data sets based on a type of the user data. The shared AI agent generates personalized recommendations based on the data sets.