Data Processing System for Automated Risk Remediation
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Solution Overview
Problem
There is a growing need for improved systems and methods to manage personal data effectively, particularly to minimize the number of entities processing sensitive information and to respond to potential risks such as data breaches and changes in legal or industry standards, while ensuring compliance with privacy and security policies.
Innovation Solution
A computer-implemented data processing method that identifies potential risk triggers, assesses their relevance, and takes remedial actions by analyzing data assets affected, using data modeling techniques to update risk remediation data and adapt to changes, ensuring compliance with legal and industry standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data modeling techniques are used to identify and assess risk triggers, then risk management effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments risk management into distinct modules: risk trigger identification, risk assessment, data asset identification, and remediation. Each module handles a specific aspect of risk management, making the overall complex system manageable through functional decomposition. The data model itself is segmented into multiple data assets with specific attributes that can be independently analyzed.
Solution Approach 2:
A data model serves as an intermediary between raw data and risk analysis. The data model structure with defined data assets and attributes acts as a mediator that transforms complex data relationships into analyzable risk triggers, simplifying the risk assessment process while maintaining comprehensive coverage.
2Measurement precision
If comprehensive risk assessment is performed on all data assets, then risk detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining data model structures, data asset templates, and risk trigger categories before actual risk assessment begins. This pre-configuration enables faster processing during risk events while maintaining comprehensive assessment capabilities, as the analytical framework is already in place.
Solution Approach 2:
The system applies partial action by focusing risk assessment on specific data assets and attributes relevant to identified risk triggers, rather than uniformly assessing all data assets. This targeted approach maintains high detection accuracy for critical risks while reducing unnecessary processing of low-risk areas.
3Adaptability or versatility
If multiple entities process personal data, then data utility is improved, but security risk increases
Solution Approach 1:
The system implements feedback mechanisms where risk assessment results and remediation outcomes are continuously monitored and fed back into the data model. This enables dynamic adjustment of risk management strategies, allowing multiple entities to process data safely by learning from past risks and adapting security measures accordingly.
Solution Approach 2:
The system changes parameters by dynamically adjusting risk thresholds, data asset classifications, and remediation criteria based on assessed risks. This enables flexible data sharing among multiple entities while maintaining appropriate security controls, as parameters can be modified to reflect changing risk landscapes and data sensitivity requirements.
Data Source
AI summary
In various embodiments, a system may be configured to substantially automatically determine whether to take one or more actions in response to one or more identified risk triggers (e.g., data breaches, regulation change, etc.). The system may, for example: (1) compare the potential risk trigger to one or more previous risks triggers experienced by the particular entity at a previous time; (2) identify a similar previous risk trigger (e.g., one or more previous risk triggers related to a similar change in regulation, breach of data, type of issue identified, etc.); (3) determine the relevance of the current risk trigger based at least in part on a determined relevance of the previous risk trigger; and (4) determine whether to take one or more actions to the current risk trigger based at least in part on one or more determined actions to take in response to the previous, similar risk trigger.