Data Subject Access Request Processing System
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Solution Overview
Problem
Organizations face challenges in efficiently managing and complying with privacy and security policies regarding personal data, particularly in responding to data subject access requests and minimizing data processing entities, due to the complexity of data storage across multiple locations and the need for improved tools to protect individual data rights.
Innovation Solution
A computer-implemented method and system that processes data subject access requests by identifying and generating reports on personal data storage locations, visualizing data transfers, and assessing risks associated with data transfers, using data models and machine learning techniques to ensure compliance with regulations and individual rights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to identify and track personal data across multiple storage locations, then data subject access requests can be processed, but the time and resources required increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically identifying and mapping all storage locations of personal data before a data subject access request is received. Data catalogs are pre-generated and maintained, so when a request arrives, the system can quickly retrieve and process the requested information without manual searching, thus reducing processing time while ensuring compliance
Solution Approach 2:
The system creates and maintains digital copies of data location information in structured catalogs and data models. These copies allow rapid querying and processing of data subject access requests without needing to physically search through multiple storage systems, significantly reducing the time required to fulfill requests while maintaining accurate tracking of all personal data locations
2Object-affected harmful factors
If comprehensive data tracking across all entities is implemented to ensure data subject rights, then individual data protection is improved, but the complexity of the data management system increases
Solution Approach 1:
The system segments the data management functionality into distinct modular components: data catalogs for storing location information, data models for representing data structures, automated identification modules for tracking personal data, and reporting modules for generating access request responses. This segmentation allows comprehensive data tracking while managing complexity through modular, independently maintainable components with well-defined interfaces
Solution Approach 2:
The system introduces intermediary data structures including data catalogs that store mapping information between personal data and storage locations, and data models that represent the organizational structure of data. These intermediaries simplify the complexity by providing standardized interfaces between the diverse storage systems and the data subject access request processing functionality, enabling comprehensive tracking without directly coupling all system components
3Productivity
If automated systems are used to identify storage locations and generate reports, then processing efficiency improves, but the initial setup and implementation complexity increases
Solution Approach 1:
The system implements universal data catalogs and standardized data models that can serve multiple functions: tracking personal data for access requests, monitoring data transfers, generating compliance reports, and supporting various data protection activities. This multi-functionality improves processing efficiency while reducing implementation complexity by avoiding the need to build separate systems for each data protection task
Data Source
AI summary
In various embodiments, an organization may be required to comply with one or more legal or industry requirements related to the storage of personal data (e.g., which may, for example, include personally identifiable information) even when responding to and fulfilling Data Subject Access Requests. In particular, when responding to a DSAR, the system may compile one or more pieces of personal data for provision to a data subject. The system may store this compilation of personal data at least temporarily in order to provide access to the data to the data subject. As such, the system may be configured to implement one or more data retention rules in order to ensure compliance with any legal or industry requirements related to the temporary storage of the collected data while still fulfilling any requirements related to providing the data to data subjects that request it, deleting the data upon request, etc.