Data Model Adaptive Execution System for Privacy Risk Remediation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a growing need for improved systems and methods to manage personal data in compliance with privacy and security policies, as frequent breaches and unauthorized access to sensitive information have become more common, and individuals seek tools to minimize data processing by entities they do not actively engage with.
Innovation Solution
A computer-implemented data processing method that generates a visualization of data transfers and identifies potential risk triggers, using data modeling techniques to analyze data assets, determine relevant regulations, and take remedial actions, while allowing individuals to control their data through data subject access requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data modeling techniques are used to identify and assess data processing risks, then data security and compliance are improved, but system complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct components: data assets, data flows, processing activities, and risk triggers. This segmentation allows the system to manage complexity by analyzing and monitoring each component separately while maintaining overall security through their interconnections in the data model.
Solution Approach 2:
The patent introduces a data model as an intermediary layer that sits between raw data processing operations and risk assessment. This data model serves as a mediator that structures and organizes data processing information, enabling systematic risk identification and assessment without requiring direct complex analysis of all underlying data operations.
2Reliability
If comprehensive data transfer visualization is generated, then regulatory compliance is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by continuously maintaining and updating the data model in the background as data processing operations occur. This allows the comprehensive data transfer visualization and compliance information to be pre-computed and readily available when needed, rather than generating it from scratch during compliance audits or analysis.
Solution Approach 2:
The system implements self-service mechanisms where the data model automatically updates itself as new data processing activities are detected. The system autonomously identifies risk triggers, assesses their relevance, and maintains the visualization without requiring manual intervention or time-consuming analysis for each update.
3Adaptability or versatility
If data subject access requests are processed, then individual data control is improved, but operational overhead increases
Solution Approach 1:
The patent implements feedback mechanisms where the system automatically responds to data subject access requests by querying the existing data model. The data model provides real-time information about data assets, flows, and processing activities, enabling the system to fulfill access requests without manual operational intervention.
Solution Approach 2:
The system creates and maintains a copy of the data processing landscape in the form of the data model. This virtual copy allows data subjects to access and control their information through the model representation without requiring direct access to or manipulation of the actual complex data processing infrastructure.
Data Source
AI summary
In various embodiments, a Data Model Adaptive Execution System may be configured to take one or more suitable actions to remediate an identified risk in view of one or more regulations (e.g., one or more legal regulations, one or more binding corporate rules, etc.). For example, in order to ensure compliance with one or more standards related to the collection and/or storage of personal data, an entity may be required to modify one or more aspects of a way in which the entity collects, stores, and/or otherwise processes personal data (e.g., in response to a change in a legal or other requirement). In order to identify whether a particular change or other risk trigger requires remediation, the system may be configured to assess a relevance of the risk posed by the risk and identify one or more processing activities or data assets that may be affected by the risk.