Context-Based Sensitive Data Remediation With User Review
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data remediation methods, such as encryption and machine learning models, often result in network overhead, memory usage, and false positives, leading to inefficient and resource-intensive handling of sensitive data.
Innovation Solution
A system that generates and applies proposed remediations for sensitive data snippets based on user input, using context-based queries and machine learning models trained on sensitive data patterns, allowing for transparent and direct application of remediations to data records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used for data remediation, then automation of sensitive data handling is improved, but false positives increase leading to erroneous modifications
Solution Approach 1:
The system implements a feedback mechanism where users can review and correct false positives identified by machine learning models. Users receive notifications about detected sensitive data and can confirm or correct the model's identification, providing feedback that improves future automated remediation accuracy while maintaining reliability.
Solution Approach 2:
The patent introduces an intermediary layer between automated machine learning models and actual data modification. This intermediary allows users to review and validate automated decisions before they are executed, preventing erroneous modifications while preserving the benefits of automation.
2Reliability
If encryption and masking systems are applied to sensitive data, then data security is improved, but network overhead and memory usage increase
Solution Approach 1:
The system extracts only the necessary data elements for remediation from the original data records. Instead of processing entire encrypted or masked records, the system identifies and works with specific sensitive data snippets, reducing network overhead and memory requirements while maintaining security through targeted encryption and masking only where needed.
Solution Approach 2:
The patent segments the data remediation process into discrete steps: identification of sensitive data snippets, generation of remediation commands, and selective application. This segmentation allows the system to handle data in smaller portions rather than processing entire datasets at once, reducing resource consumption while maintaining security measures.
3Manufacturing precision
If comprehensive data remediation is applied to all sensitive data snippets, then data quality is improved, but computing resources are consumed excessively
Solution Approach 1:
The system applies partial remediation by focusing computational resources on the most critical and problematic data elements first. It prioritizes remediation based on the severity and complexity of each data snippet, applying comprehensive treatment only where necessary while using lighter processing for less critical data, thus improving data quality without excessive resource consumption.
Solution Approach 2:
The patent changes processing parameters dynamically based on the characteristics of each data snippet. The system adjusts the level of remediation applied by modifying parameters such as processing depth, computational intensity, and resource allocation according to the specific requirements of each data element, optimizing the balance between data quality improvement and computing resource usage.
Data Source
AI summary
In some implementations, a remediation system may receive, a tracking system, a set of tickets associated with at least one data record. The remediation system may search, in the at least one data record and using a set of contexts indicated in the set of tickets, for a set of sensitive data snippets. The remediation system may generate a set of proposed remediations corresponding to the set of sensitive data snippets. The remediation system ma6y output, for each sensitive data snippet in the set of sensitive data snippets, a corresponding proposed remediation in the set of proposed remediations. The remediation system may selectively apply the set of proposed remediations based on inputs from a user.