Automated Personal Data Removal System for Privacy Compliance
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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 lead to unauthorized access and misuse, 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 identifies and removes personal data not associated with privacy campaigns by accessing data assets, generating a catalog of privacy campaigns, scanning for unassociated data, and presenting removal indications to individuals, allowing for the automatic removal of such data from the assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data management processes are used to track personal data, then flexibility and adaptability are maintained, but productivity and efficiency deteriorate due to increased time consumption and human error
Solution Approach 1:
The system enables automatic self-service functionality where the data processing system autonomously identifies, catalogs, and removes personal data without requiring manual intervention. The system automatically scans data assets, matches personal data against privacy campaign criteria, and executes removal actions based on predefined policies, thereby dramatically improving productivity while managing complexity through automation.
Solution Approach 2:
The system performs preliminary actions by pre-configuring privacy campaign criteria, data asset inventories, and removal policies before actual data processing occurs. This preliminary setup enables the system to automatically execute data identification and removal operations without requiring real-time manual configuration, thus enhancing efficiency while containing system complexity through upfront preparation.
2Measurement precision
If comprehensive data scanning and analysis are performed to identify all personal data, then measurement precision and detection accuracy improve, but loss of time and processing duration increase
Solution Approach 1:
The system applies partial action by focusing data scanning efforts on specific data assets and personal data types that are most relevant to active privacy campaigns. Rather than uniformly scanning all possible data, the system strategically targets high-priority areas based on pre-configured criteria, achieving high identification accuracy for critical data while reducing overall processing time through selective scanning.
Solution Approach 2:
The system maintains continuous operation by performing data identification and removal operations in an ongoing manner rather than through periodic batch processing. The automatic removal mechanism continuously monitors and processes personal data as it is identified, ensuring high measurement precision is maintained over time without requiring repeated full-scanning cycles, thus reducing cumulative time loss.
3Productivity
If automatic removal of personal data is implemented, then productivity and compliance efficiency improve, but reliability and data integrity risks increase due to potential erroneous deletions
Solution Approach 1:
The system incorporates feedback mechanisms where the automatic removal process continuously monitors the outcomes of data deletion operations. The system tracks removal actions, verifies successful execution, and uses this feedback to adjust and refine its identification and removal criteria over time. This feedback loop enhances reliability by detecting and correcting potential errors while maintaining high productivity through automated operation.
Solution Approach 2:
The system applies beforehand cushioning by implementing pre-removal verification steps and backup mechanisms before actual data deletion occurs. The automatic removal process includes preliminary checks to confirm data matches removal criteria, maintains logs of intended deletions, and prepares recovery capabilities. These protective measures cushion against potential erroneous deletions, ensuring reliability while preserving the efficiency benefits of automation.
Data Source
AI summary
A Data Processing Risk Remediation System may be configured to: (1) access risk remediation data for an entity that identifies suitable action(s) to remediate a risk in response to identifying one or more data assets of the entity that may be affected by potential risk trigger(s); (2) receive an indication of an update to the one or more data assets; (3) identify one or more updated risk triggers for the entity; (4) analyze the one or more potential updated risk triggers to determine a relevance of a risk posed to the entity by the one or more updated risk triggers; (5) use one or more data modeling techniques to identify one or more data assets associated with the entity that may be affected by the risk; and (6) update the risk remediation data to include the one or more actions to remediate the risk.