Automated Breach Impact Analysis System for Data Privacy
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Solution Overview
Problem
Conventional data breach management systems are inefficient, inaccurate, and lack automation, making it difficult for organizations to quickly and effectively respond to data breaches, leading to potential legal penalties, financial losses, and reputational damage.
Innovation Solution
An advanced Breach Impact Analysis (BIA) system that automates the assessment and response to data breaches by integrating data collection, identification, identity deduplication, residency inference, legal analysis, and automated response modules, utilizing advanced technologies like large language models and AI-driven communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to identify and analyze breached data, then systems can operate with simpler technology, but the speed and productivity of breach response deteriorates significantly
Solution Approach 1:
The system divides breach analysis into distinct modular components: data collection module, data identification module using NLP, identity deduplication module, residency inference module, and legal analysis module. Each module handles a specific aspect of the breach analysis process, improving overall productivity while managing complexity through functional segmentation.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Natural language processing algorithms substitute for manual data scanning, machine learning models replace human judgment in identity matching, and automated legal analysis tools supersede manual legal research, dramatically increasing breach response speed.
2Measurement precision
If advanced data processing techniques are implemented to improve identification accuracy, then measurement precision of personal information improves, but device complexity increases
Solution Approach 1:
The system introduces natural language processing as an intermediary layer between raw breached data and analysis results. The NLP technology acts as a mediator that automatically parses, understands, and structures unstructured personal information, achieving high identification accuracy without requiring complex custom-built processing systems for each data type.
Solution Approach 2:
The patent employs universal data processing techniques that handle multiple types of personal information through common methodologies. The same NLP framework processes names, addresses, dates, and other data types uniformly, while the identity deduplication system applies consistent algorithms across diverse datasets, reducing overall system complexity through standardized approaches.
3Reliability
If comprehensive data analysis is performed to identify all affected individuals and legal obligations, then completeness of breach assessment improves, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and normalizing data immediately upon breach detection. The data collection module continuously monitors and captures breached information in real-time, and the identification module prepares structured data formats in advance, so that when analysis is needed, the groundwork is already complete, reducing total assessment time while maintaining completeness.
Solution Approach 2:
The patent implements continuous automated analysis that operates without interruption throughout the breach response process. Rather than batch processing or periodic reviews, the system continuously collects data, identifies personal information, deduplicates identities, infers residency, and analyzes legal obligations in an unbroken workflow, ensuring complete assessment while minimizing analysis time through constant operation.
Data Source
AI summary
A system to analyse impact of data breaches on sensitive data is disclosed. The system includes a hardware processor and memory with program instructions for executing various modules. The data collection module retrieves and enriches impacted data from multiple repositories. The data identification module uses data loss prevention (DLP) and named entity recognition (NER) techniques, enhanced by large language models (LLMs), to accurately identify personal information. The identity deduplication module consolidates individual references using deterministic and probabilistic techniques, while the residency inference module applies machine learning and heuristic methods to determine residency based on various data sources. The analysis module assesses impacted data to identify relevant laws and estimate fines. The automation module streamlines response actions, including generating notifications and ensuring compliance. This system enhances breach response efficiency through integrated, automated analysis and actions.