AI Data Access Proxy for Encrypted Traffic Security
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Solution Overview
Problem
Existing data security systems struggle to effectively protect sensitive data due to the increasing prevalence of end-to-end encrypted protocols and the difficulty in interpreting indirect traffic requests, leading to vulnerabilities in accessing data via malicious users.
Innovation Solution
Implementing an AI-powered data access proxy (Semantic Data Proxy, SDP) that inspects and transforms data requests based on user identity, permissions, and privacy rules, providing controlled access to private databases while preventing unauthorized access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional perimeter protection with firewalls is implemented, then network security is improved, but data protection effectiveness deteriorates due to end-to-end encrypted protocols making traffic inspection impossible
Solution Approach 1:
The patent introduces a database proxy as an intermediary component positioned between the database and users/applications. This proxy intercepts and inspects data requests before they reach the database, enabling security analysis of encrypted traffic without requiring decryption. The proxy acts as a mediator that maintains security controls while preserving the benefits of end-to-end encryption.
2Difficulty of detecting and measuring
If Web Application Firewall inspecting HTTP requests is implemented, then threat detection is improved, but data access security deteriorates because indirect traffic inspection is difficult to interpret and act upon
Solution Approach 1:
The patent extracts the security inspection function from the network layer and places it directly at the database access layer. Instead of indirectly inspecting HTTP requests, the database proxy directly intercepts and examines database queries and responses, extracting sensitive data elements for thorough security analysis. This direct extraction of data for inspection eliminates the interpretation difficulties of indirect traffic analysis.
3Reliability
If comprehensive data inspection and transformation is implemented, then data security is improved, but system complexity increases due to AI-powered contextual analysis and multiple transformation operations
Solution Approach 1:
The patent implements self-service mechanisms where the database proxy automatically performs contextual analysis, risk assessment, and data transformation without requiring manual intervention. The system uses AI-powered algorithms to autonomously evaluate data requests, determine appropriate transformation operations, and execute security policies. This automation reduces operational complexity while maintaining comprehensive security inspection.
4Adaptability or versatility
If AI-powered contextual analysis is implemented, then personalized data access control is improved, but processing time increases due to analyzing user identity, permissions, and activity history
Solution Approach 1:
The patent implements preliminary action by pre-establishing user profiles, permission sets, and activity baseline patterns before data access requests occur. The system pre-configures contextual frameworks and risk assessment criteria, allowing rapid evaluation of incoming requests against pre-analyzed user contexts. This preliminary preparation significantly reduces the processing time required for personalized access control decisions.
Data Source
AI summary
Exemplary systems include an AI-powered assistant that automatically analyzes available data to create a contextual framework based on the nature of the query, the persona, and the permissions of the user, allowing it to provide relevant and personalized responses to user queries. The system can also generate contextual personas based on job descriptions within an organization, respond to specific tasks associated with roles, and integrate with network appliances and flow data to embed AI-generated insights into logging streams and ensure compliance with defined policies.