Persona-Boundary Vectorized Data Access for Secure LLM Queries

The patent segments data from structured databases into vectorized documents organized by persona boundaries (user roles). Each vectorized document contains data accessible to a specific persona, enabling fine-grained access control while maintaining large-scale data storage. This segmentation resolves the contradiction by allowing comprehensive data storage without compromising security through unrestricted access.

The patent introduces an intermediary layer between structured databases and LLMs: the vectorized document store. This intermediary transforms structured data into persona-based vectorized formats that LLMs can process while automatically enforcing access control policies. The intermediary enables both large data storage and secure access control by mediating between raw data and AI processing.

The patent segments data access by creating persona-specific vectorized documents. When an LLM receives a query, it first determines the user's persona and retrieves only the corresponding vectorized documents. This segmentation prevents the LLM from accessing or generating responses containing data outside the user's authorized scope, eliminating unintended information disclosure while maintaining versatile query capabilities.

The patent implements feedback mechanisms where the system continuously monitors LLM queries against persona boundaries and access control policies. If a query attempts to access data outside authorized personas, the system intercepts and blocks the request. This feedback loop ensures that LLM versatility is maintained within secure boundaries, preventing harmful information disclosure.

The patent creates copies of structured database data in vectorized document format, organized by persona boundaries. Instead of modifying the original structured database or implementing complex access control layers throughout the entire system, the patent copies data into a separate vectorized store that is pre-organized for secure access. This copying approach simplifies the system by isolating security logic from data storage.

The patent performs preliminary action by pre-processing and transforming structured database data into persona-based vectorized documents before they are accessed by LLMs. This preliminary transformation organizes data according to access control requirements in advance, eliminating the need for complex real-time access control logic during query processing. The preliminary action simplifies the overall system by shifting complexity to a preparatory stage.

The patent replaces the mechanical/structured database organization system with a semantic/vector-based organization system. Instead of relying on rigid tables and schemas that LLMs struggle to interpret, the patent transforms data into vectorized documents with semantic meanings that LLMs naturally understand. This substitution maintains effective data organization while dramatically improving AI model understanding capability.

The patent changes the fundamental parameters of data representation from structured formats (tables, rows, columns) to vectorized semantic representations. This parameter transformation converts data into a format that is both easily organized and naturally understandable by LLMs. The vectorized format preserves data organization benefits while eliminating the comprehension barriers of structured databases for AI models.