Cognoscible Computing Engine for Sensitive Data Detection at Scale
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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 the adoption of cloud services.
Innovation Solution
A multi-layered, multi-pathed cognoscible computing engine (CCE) that parses, detects, classifies, and tags sensitive data using a combination of artificial intelligence models, natural language processing, and semantic rules, supporting structured, semi-structured, and unstructured data formats, with modules for masking and redaction to ensure compliance with data privacy regulations.
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 detection 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 detection module employs an ensemble of artificial intelligence models that combine multiple algorithms and approaches. This composite approach integrates different AI techniques to achieve higher detection accuracy than any single model could provide alone, resolving the contradiction between accuracy and complexity.
2Productivity
If manual identification methods are used, then system complexity is lower, but productivity and speed of processing are insufficient
Solution Approach 1:
The system implements self-service automation where the multi-layered computing engine automatically performs detection, identification, confirmation, and tagging of sensitive data without human intervention. The engine autonomously processes data through multiple layers of analysis, achieving high productivity while managing automation complexity through structured modular architecture.
Solution Approach 2:
The data source identifier module performs preliminary classification and extraction of data attributes before the main detection process. This preliminary action prepares data in advance, enabling faster processing in subsequent modules and improving overall productivity without overwhelming the system with unprepared data.
3Adaptability or versatility
If comprehensive detection of all data formats is implemented, then coverage is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system implements a universal detection engine that can process multiple data formats including structured, semi-structured, and unstructured data. The multi-layered architecture with specialized modules provides multi-functionality, allowing the same system to adapt to various data types without requiring separate detection mechanisms for each format.
Solution Approach 2:
Each module in the detection pipeline is optimized for specific data characteristics and formats. The data source identifier handles format classification, while the detection module applies context-based classification tailored to different data types. This local optimization within each module reduces the overall difficulty of detecting diverse data formats.
4Measurement precision
If high-accuracy context-based classification is used, then detection precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The classification process is segmented across multiple modules, with each module performing a specific classification task. The data source identifier performs initial format-based classification, the detection module performs context-based classification, and the tagging module performs final categorization. This segmentation allows the system to achieve high overall accuracy while distributing computational load across multiple specialized components.
Solution Approach 2:
The data source identifier module performs preliminary data classification and attribute extraction before the main detection and classification processes. By preparing and categorizing data in advance, the system reduces the computational complexity of subsequent high-accuracy classification tasks, thereby reducing overall energy consumption while maintaining precision.
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.