Intelligent Data Protection System Using Multi-Layer Scanning
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Solution Overview
Problem
Existing systems lack an efficient and dynamic solution for identifying sensitive information, reclassifying it, managing access, and providing real-time protective measures across an entity-wide scale.
Innovation Solution
The system employs a three-layered discovery method, utilizing metadata scans, quick scans, and deep scans, combined with AI and ML, to identify sensitive data, assess protection needs, and generate recommendations for dynamic data management and protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a comprehensive data protection system is implemented across the entire entity, then data security and compliance are improved, but system complexity and implementation cost increase
Solution Approach 1:
The patent divides the data protection system into multiple independent scanning layers (metadata scan, quick scan, deep scan) that can operate separately. Each layer handles specific aspects of data analysis, allowing the system to manage complexity by processing data in staged segments rather than requiring a monolithic comprehensive approach.
Solution Approach 2:
The metadata scan layer performs preliminary identification of potentially sensitive data before more intensive analysis. This preliminary action filters and prioritizes data for deeper scanning, enabling comprehensive protection while reducing overall system complexity by not immediately analyzing every data element at full depth.
2Measurement precision
If multi-layer scanning methods are used to thoroughly identify sensitive data, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The scanning process is segmented into three distinct layers with different depths and purposes. The metadata scan provides quick identification, the quick scan offers intermediate analysis, and the deep scan provides thorough examination. This segmentation allows the system to achieve high detection accuracy for critical data while reducing processing time by not applying all scanning depths uniformly to all data.
Solution Approach 2:
The system applies partial scanning depth based on data characteristics and risk assessment. Not all data requires the same level of scanning intensity, allowing the system to achieve accurate detection for sensitive data while reducing overall processing time by applying less intensive scanning to less critical data.
3Productivity
If automated AI and machine learning models are deployed for data classification, then operational efficiency is improved, but model accuracy and adaptability to changing data patterns deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where scanning results and data protection outcomes are fed back into the machine learning models for continuous training and improvement. This feedback loop enables the automated models to maintain high accuracy by learning from actual data patterns and protection scenarios, preventing degradation over time.
Solution Approach 2:
The system performs preliminary manual or rule-based scanning and classification before deploying AI models for broader automation. This preliminary action establishes baseline accuracy and provides training data for the models, ensuring that automated processes start with high accuracy and can adapt to changing patterns through continuous learning.
Data Source
AI summary
Embodiments of the invention are directed to systems, methods, and computer program products for identifying sensitive, or non-publicly available, information, reclassifying identified sensitive information, and managing access to identified sensitive information in an intelligent and dynamic manner. In some embodiments, the systems and methods described herein utilize a pattern recognition engine designed to analyze and detect identifying characteristics of sensitive data or private data characteristics. The system may also employ an automated response and reporting capability to automatically re-classify sensitive data and apply appropriate protection measures in a multi-platform approach.