Document Management System Suspicious Entry Detection
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Solution Overview
Problem
Conventional document management systems are unable to effectively identify suspicious entries indicative of potentially malicious activity within documents, which can compromise entity security.
Innovation Solution
A document management system that classifies entries as 'suspicious' or 'non-suspicious' by using a security policy with defined attributes, employing a training module to generate security rules based on training data, and a security module to compare entry attributes with these rules, generating alerts and lists of suspicious entries for review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional document management systems are used to store and manage documents, then basic document storage and retrieval functions are provided, but the systems are unable to identify suspicious entries indicative of potentially malicious activity
Solution Approach 1:
The patent embeds a security module within the document management system, which in turn contains a training module and rule generation logic. This nested structure allows the system to gain advanced suspicious entry detection capabilities without requiring a completely separate external security system, thus improving reliability while controlling complexity through integrated design.
Solution Approach 2:
The training module uses training data to pre-generate security rules before actual document entries are analyzed. This preliminary action allows the system to establish detection criteria in advance, enabling it to identify suspicious entries without requiring complex real-time analysis algorithms during document processing, thereby improving security detection while maintaining reasonable system complexity.
2Measurement precision
If a security policy with multiple security rules is implemented to classify entries, then the system can accurately identify suspicious entries, but the complexity of the system increases due to rule generation and management
Solution Approach 1:
The system's training module automatically generates security rules from training data without requiring manual configuration by security experts. The rule generation logic autonomously creates and updates security rules based on patterns learned from training entries, enabling accurate suspicious entry classification while eliminating the need for complex manual rule management processes.
Solution Approach 2:
The system uses training data that includes labeled suspicious and non-suspicious entries to generate and refine security rules through an iterative feedback process. The training module continuously improves rule accuracy by learning from feedback in the training data, achieving high classification precision without requiring complex external rule management infrastructure.
3Reliability
If the system monitors and classifies all entries in documents, then suspicious activity can be detected, but the processing time and computational resources increase
Solution Approach 1:
The system applies security rules selectively to entries that match specific criteria or patterns identified during training, rather than uniformly processing every single entry with full security analysis. This partial action approach maintains effective security monitoring for suspicious patterns while reducing unnecessary processing time for normal entries, thus balancing reliability with processing efficiency.
Data Source
AI summary
A document management system manages documents of an entity. The document management system monitors for entries in a document that are suspicious. Entries in the document are classified by the document management system as a “suspicious entry” or a “non-suspicious entry.” In one embodiment, a suspicious entry is indicative of potentially suspicious activity at the entity.