Multi-Layer CCE for Sensitive Data Detection and Tagging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems are inadequate in automatically identifying and protecting sensitive data with high accuracy and speed, particularly in unstructured formats, leading to data breaches and hindering cloud adoption due to inadequate data protection mechanisms.
Innovation Solution
A multi-layered, multi-pathed cognoscible computing engine (CCE) that systematically parses, detects, classifies, and tags sensitive data using artificial intelligence models, natural language processing, and semantic rules to ensure accurate identification and compliance with data protection standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional processes are used to identify sensitive data, then implementation is simpler, but accuracy and speed of detection are insufficient
Solution Approach 1:
The system segments the sensitive data identification process into five distinct modules: data source identifier, detection module, identification module, confirmation module, and data tagging and classification module. Each module performs a specific function in the detection pipeline, allowing for specialized processing that improves accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The system transitions from traditional single-layer detection to a multi-layered, multi-pathed architecture that processes data through multiple dimensions including structured, semi-structured, and unstructured data pathways. This dimensional expansion enables comprehensive detection across diverse data formats while maintaining systematic control.
2Productivity
If manual identification methods are used, then system complexity is lower, but productivity and processing speed are insufficient for growing data volumes
Solution Approach 1:
The system implements automated self-service capabilities where the multi-layered architecture automatically parses, detects, classifies, and tags sensitive data without manual intervention. The system serves itself by routing data through appropriate detection paths and generating compliance tags autonomously, dramatically improving processing speed and productivity.
Solution Approach 2:
The system performs preliminary classification and routing of data before detailed detection occurs. The data source identifier module pre-processes incoming data to identify its structure type, preparing it for optimized processing in subsequent modules. This preliminary action accelerates overall processing by preventing bottlenecks in later stages.
3Adaptability or versatility
If comprehensive detection of all data formats is implemented, then detection coverage is improved, but processing time increases
Solution Approach 1:
The system segments detection pathways according to data structure types (structured, semi-structured, unstructured), allowing data to be processed through the most appropriate path for its format. This segmentation enables comprehensive coverage of all data formats while minimizing processing time by avoiding unnecessary analysis steps for each data type.
Solution Approach 2:
The system dynamically routes data through different detection paths based on the identified data structure type. The multi-pathed architecture adapts processing intensity and methods according to the specific data format, providing versatile coverage while maintaining efficient processing speeds by applying appropriate detection depth for each data type.
4Productivity
If automated detection systems are implemented, then processing speed improves, but accuracy in identifying context-based sensitive data decreases
Solution Approach 1:
The system segments detection into specialized modules where the detection module uses AI/ML models for initial identification, the identification module applies semantic rules for context-based classification, and the confirmation module validates results. This segmentation allows each module to specialize in specific accuracy-critical tasks while maintaining high overall processing speed.
Solution Approach 2:
The system implements feedback loops where detection results are validated through semantic rule checking and context-based analysis before final classification. The confirmation module provides feedback verification to ensure accuracy, while the multi-layered architecture allows iterative refinement of detections, maintaining both speed and precision through controlled feedback cycles.
Data Source
AI summary
A computer-implemented apparatus, system, and method is disclosed for protecting sensitive data. A cognoscible computing engine is multi-layered and multi-pathed. It includes features for handling different data formats, including structured, semi-structured, and unstructured data. Features are included to support near real-time processing at scale with high accuracy. Applications include redacting or masking sensitive data to comply with data privacy and security standards.