Automated Compliance Analysis for Data Breach Events
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Solution Overview
Problem
Current methods for identifying and managing compliance-related information in data breach events are inefficient and error-prone, particularly due to the reliance on manual review of unstructured data, which can lead to missed notifications and non-compliance with stringent regulations like GDPR and CCPA, especially when dealing with complex and varied data types such as image files.
Innovation Solution
A method involving a combination of machine learning and human review to analyze structured, unstructured, and semi-structured data files, including image files, to identify protected information elements, and generate compliance-related databases for timely notifications, using a machine learning framework to automate the process and integrate human validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review methods are used to identify protected information in data breach events, then human judgment can validate results, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated machine learning system that uses natural language processing and image recognition algorithms to identify protected information elements in structured, unstructured, and semi-structured data files, dramatically reducing analysis time while maintaining high accuracy through automated validation mechanisms
Solution Approach 2:
The patent introduces a hybrid system that acts as an intermediary between fully automated machine learning analysis and human review, using the ML system to pre-process and flag potential protected information, then selectively engaging human reviewers only for ambiguous cases, thus optimizing both speed and accuracy
2Productivity
If automated machine learning methods are used to analyze data files, then processing speed increases, but accuracy may decrease without human validation
Solution Approach 1:
The patent implements feedback loops where the machine learning system continuously learns from human reviewer corrections and validations, refining its algorithms to improve detection accuracy over time while maintaining high processing speeds through automated iteration and model retraining on validated datasets
Solution Approach 2:
The patent applies partial automation where the machine learning system performs comprehensive initial analysis of all data files to identify potential protected information, then applies selective human review only to cases with lower confidence scores or ambiguous classifications, achieving both speed and accuracy
3Reliability
If comprehensive review of all data files is conducted, then compliance accuracy improves, but resource requirements increase
Solution Approach 1:
The patent applies local quality by directing comprehensive analysis resources specifically to data files and sections that contain or are likely to contain protected information elements, while using lighter processing for clearly non-sensitive files, thus achieving high compliance accuracy without proportionally increasing resource consumption across all files
Solution Approach 2:
The patent segments the data breach analysis process into distinct phases: initial automated scanning to identify potential protected information, detailed analysis of flagged sections, and validation steps, allowing resources to be concentrated on critical analysis tasks rather than uniformly processing all data files
4Measurement precision
If human reviewers manually analyze unstructured data including image files, then complex data types can be validated, but the process becomes highly complex and slower
Solution Approach 1:
The patent replaces manual human review of unstructured data and image files with specialized machine learning components including optical character recognition (OCR) for images, natural language processing for unstructured text, and automated classification algorithms that can validate complex data types with high precision while simplifying the overall process through automation
Data Source
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AI summary
Various examples are provided related to identification and management of compliance-related information associated with data breach events. In one example, a method includes receiving a first data file collection associated with a first data breach event; generating information associated with presence or absence of protected information elements of all or part of the first data file collection and incorporating data files including the protected information elements in a second data file collection; analyzing data files selected from the second data file collection; and incorporating the information associated with the analysis into machine learning information that may be used for subsequent analysis of data file collections.