NLP Workspace Query Pipeline for Secure Multi-User Database Access
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Solution Overview
Problem
Collaborative workspace systems face challenges in managing diverse user access rights to databases, leading to data security risks and inefficiencies in data sharing across departments, as users often lack full access to all data due to security constraints and confidentiality issues.
Innovation Solution
A system that utilizes natural language processing (NLP) to convert user queries into SQL statements, validates access rights, and creates a pipeline of user queries, masking data inaccessible to each user, allowing secure collaboration while ensuring each user's access rights are respected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users are granted full access to all database data, then data accessibility and collaboration efficiency are improved, but data security risks and confidentiality violations increase
Solution Approach 1:
The system implements different data accessibility qualities for different users based on their access rights. Each user receives a personalized view of the data where only the portions they are authorized to access are visible and queryable, while other portions remain hidden. This local differentiation of data quality allows users to access data efficiently within their security constraints without compromising overall data security.
Solution Approach 2:
The NLP workspace system acts as an intermediary between users and the database. It receives natural language queries from users, translates them to SQL statements, validates access rights, and returns personalized results. This intermediary layer enables the system to maintain security by filtering and masking data before it reaches users, while still providing efficient collaborative access to authorized information.
2Reliability
If access rights are strictly enforced for each user, then data security is improved, but collaboration efficiency and data sharing speed decrease
Solution Approach 1:
The system performs preliminary validation of access rights and creation of temporary tables before processing user queries. By pre-establishing the security framework and temporary data structures, the system can quickly process collaboration requests without repeatedly validating access rights, thus maintaining both security and collaboration efficiency.
Solution Approach 2:
The system dynamically changes the data presentation parameters based on user access rights. Instead of providing a static view of all data, the system adapts the data parameters (visibility, accessibility, masking) for each user based on their authorization level, enabling efficient collaboration within security constraints.
3Adaptability or versatility
If the system processes multiple user queries simultaneously with different access rights, then system complexity increases, but data processing time and resource consumption increase
Solution Approach 1:
The system segments the database into temporary tables that are specific to each user or user group. By creating separate temporary tables for different users with different access rights, the system can process queries independently for each user without interfering with others, thus managing complexity while maintaining adaptability to different access control requirements.
4Reliability
If the system masks inaccessible data for each user, then data security is improved, but information completeness and data sharing quality decrease
Solution Approach 1:
The system applies local quality by providing each user with a personalized data view that includes only the information they are authorized to access. While this masks inaccessible data from users without proper permissions, it maintains information completeness for authorized users by providing them with full access to relevant data portions.
Data Source
AI summary
A method, computer system, and computer program product for generating a collaborative platform for collective database retrieval using natural language processing are provided. The embodiment may include receiving a query in natural language format from a user. The embodiment may also include converting the natural language query to a search query language statement using a natural language processing technique. The embodiment may further include validating the user access rights to queried data. The embodiment may also include rendering a response to the query submitted by the user in a personalized manner. The embodiment may further include creating a temporary table based on the response and the query. The embodiment may also include iterating a plurality of processes of validating and processing a plurality of queries of one or more different users using the created temporary table and based on access rights for each of the one or more different users.