Enterprise Document Classification With Adaptive Threat Labeling
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Solution Overview
Problem
There is a need for improved threat management systems that can dynamically adapt to changes in compute instances and new threats, effectively manage enterprise documents, and protect against a variety of cybersecurity threats across diverse network configurations.
Innovation Solution
A threat management system utilizing a Sensor, Events, Analytics, and Response (SEAR) approach, which includes a neural network or machine learning model for automatic document classification, policy management, security management, and event analysis to identify and mitigate threats across enterprise networks, endpoints, and cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual document classification is used, then classification accuracy can be ensured, but labor time and operational complexity increase significantly
Solution Approach 1:
The system enables documents to automatically classify themselves by extracting features and applying classification rules without human intervention. The classification engine autonomously processes documents, extracts relevant features, applies classification rules, and assigns classification labels, eliminating the need for manual classification while maintaining accuracy through multiple validation mechanisms.
Solution Approach 2:
The patent replaces manual mechanical classification processes with an automated electronic system. Instead of human reviewers manually examining and categorizing documents, the system uses feature extraction algorithms, classification rules, and automated decision-making logic to perform classification, dramatically reducing time while preserving accuracy through systematic processing.
2Reliability
If comprehensive threat detection is implemented across all enterprise networks, then security coverage is improved, but system complexity and computational resources increase
Solution Approach 1:
The system divides the enterprise network into multiple segments or zones, each with its own classification engine and rule set. Documents are classified within their specific network segments using localized rules, and classification results are aggregated at higher levels. This segmentation allows comprehensive coverage while managing complexity through modular, distributed processing.
Solution Approach 2:
The classification engine is designed as a universal system that can handle multiple document types, classification schemes, and network configurations through a single platform. The system uses standardized feature extraction and rule-based classification that can be applied across diverse enterprise networks, providing comprehensive security coverage without requiring separate systems for each network segment.
3Productivity
If automated classification rules are applied to all documents, then processing speed increases, but false classification rates may increase
Solution Approach 1:
The classification system dynamically adjusts its processing approach based on document characteristics and confidence levels. For high-confidence classifications, the system applies rules quickly with minimal validation. For borderline cases or documents with ambiguous features, the system automatically applies additional validation rules, seeks human review, or uses alternative classification methods, thereby maintaining speed for most documents while ensuring accuracy for uncertain cases.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results are validated against ground truth data, user corrections, and performance metrics. Classification rules are continuously refined based on feedback from false positives and negatives, allowing the system to maintain high processing speed while progressively improving accuracy through learned adjustments to rule parameters and thresholds.
Data Source
AI summary
A collection of documents or other files and the like within an enterprise network are labelled according to an enterprise document classification scheme, and then a recognition model such as a neural network or other machine learning model can be used to automatically label other files throughout the enterprise network. In this manner, documents and the like throughout an enterprise can be automatically identified and managed according to features such as confidentiality, sensitivity, security risk, business value, and so forth.