Data Lifecycle Discovery Platform for Privacy Compliance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for managing and protecting user data in online interactions are deficient in security, scope, and robustness, leading to inadequate protection against unauthorized access and misuse, and fail to comply with legal and contractual obligations.
Innovation Solution
A data lifecycle discovery platform (DLDP) that employs classification techniques, artificial intelligence, and machine learning to identify, track, and manage user data across various data stores, enforcing compliance with laws and agreements through a rules engine and providing a privacy health index, while managing user rights and access controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques are used to manage and protect user data, then implementation is simpler, but data security and privacy protection are insufficient
Solution Approach 1:
The patent segments data protection into multiple layers: data discovery, classification, rights determination, access control, and monitoring. Each layer handles specific aspects of data security, allowing the system to achieve comprehensive protection through modular components rather than a monolithic complex system.
Solution Approach 2:
The patent introduces intermediary components such as data protection officers, automated rights management systems, and access control intermediaries that mediate between data subjects and data processors. These intermediaries enable robust security without requiring direct complex interactions between all system components.
2Reliability
If comprehensive data tracking and management is implemented, then compliance with legal obligations is improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent implements preliminary actions by automatically discovering and classifying data before processing occurs, determining data subject rights in advance, and establishing access control policies beforehand. This proactive approach ensures compliance without requiring complex real-time decision-making during data processing operations.
Solution Approach 2:
The patent changes parameters such as data classification categories, rights management policies, and access control levels dynamically based on data type and context. This allows the system to adapt to different compliance requirements without requiring a completely different system architecture for each scenario.
3Reliability
If automated rights management and access control are enforced, then data privacy protection is enhanced, but processing time and operational complexity increase
Solution Approach 1:
The patent performs preliminary classification and rights determination during data ingestion and storage phases. By establishing access control policies and rights metadata beforehand, the system enables rapid data processing operations without repeated security checks, thus maintaining both privacy protection and processing efficiency.
Solution Approach 2:
The patent creates copies of data with associated rights metadata and access control information that can be quickly referenced during processing operations. This allows the system to enforce privacy protections through simple metadata checks rather than complex real-time analysis, preserving processing speed.
Data Source
AI summary
Techniques for data lifecycle discovery and management. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.