Enterprise Data File Review for Compliance Breach Notification
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Solution Overview
Problem
Current methods for identifying and managing compliance-related information in data breaches are inefficient and error-prone, often requiring manual review of unstructured data, which can lead to missed notifications and non-compliance with stringent regulatory deadlines due to the complexity and subjectivity of human review processes.
Innovation Solution
A method involving a combination of automated data analysis using machine learning and human oversight to identify and manage compliance-related information, including structured, unstructured, and semi-structured data types, with a focus on protected information elements, and generating a compliance-related database for timely notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of unstructured data is used, then human oversight and validation are achieved, but the process becomes inefficient and error-prone leading to missed notifications and non-compliance with regulatory deadlines
Solution Approach 1:
The system segments the data review process into distinct phases: automated machine learning analysis for initial identification of protected information, followed by targeted human review of specific high-risk categories. This segmentation allows the system to leverage automated processing for volume while reserving human expertise for complex judgment calls, thereby improving both efficiency and accuracy simultaneously.
Solution Approach 2:
The patent introduces an intermediary automated analysis layer between raw data and human reviewers. This intermediary system pre-processes unstructured data using machine learning models to identify potential protected information, reducing the burden on human reviewers and minimizing errors while maintaining human oversight for final validation.
2Productivity
If comprehensive automated analysis is implemented, then processing speed and coverage are improved, but the complexity of the system increases requiring sophisticated machine learning models and infrastructure
Solution Approach 1:
The system applies partial automation selectively to high-risk data categories rather than attempting to fully automate all review processes. Machine learning models are deployed to identify and prioritize specific types of protected information (e.g., PHI, PII, financial data) while less critical data receives standard processing, reducing overall system complexity while maintaining high productivity for compliance-critical functions.
Solution Approach 2:
The automated analysis system is designed with multi-functional machine learning models that can handle multiple types of protected information across different data formats and structures. This universal approach consolidates what would otherwise require multiple specialized systems, reducing device complexity while maintaining comprehensive coverage and processing speed.
3Measurement precision
If human reviewers validate all data files, then accuracy of protected information identification is improved, but the time required for review increases potentially missing regulatory deadlines
Solution Approach 1:
The system performs preliminary automated analysis of all data files before human review, pre-identifying protected information and categorizing files by risk level. This preliminary action allows human reviewers to focus their time-sensitive validation efforts only on files with detected protected information, significantly reducing total review time while maintaining high detection accuracy.
Solution Approach 2:
Human review resources are allocated non-uniformly across different data files based on their assessed risk level and the confidence score of automated detection. Files with high-confidence automated identification receive minimal or no human review, while files with low-confidence detection or high regulatory impact receive intensive human validation, optimizing the balance between accuracy and time efficiency.
Data Source
AI summary
Various examples are provided related to identification of protected information elements associated with unique entities in data files present in data file collections associated with enterprise IT networks. The unique entities can be associated with one or more entity identifications in one or more data files. Computer-generated identification of entity identifications and protected information elements can be conducted, in part, by at least some human review. Information generated accordingly to the disclosed methodology can be used to generate plans for a time and number of human reviewers needed to review data files. Information generated from the processes herein can be configured as user notifications, reports, dashboards, machine learning for subsequent data file analyses, and notifications of unique entities having protected information elements present in one or more data files.