Model-Based Confidential Data Activity Detection System
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Solution Overview
Problem
Traditional methods for detecting unwanted activity associated with confidential data are ineffective in monitoring user behaviors such as reading, note-taking, and other interactions, leading to potential data leakage.
Innovation Solution
A model-based system that identifies behavioral information associated with the use of confidential data, creates a model based on these parameters, and detects potentially unwanted activity by comparing it to predefined models or threshold scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional techniques are used to detect unwanted activity associated with confidential data, then data leakage can be prevented based on detection of unwanted activity, but user behaviors such as reading, note-taking, and other interactions are not detected
Solution Approach 1:
The patent transforms the detection approach by changing the parameters monitored from traditional security events to user behavior parameters. It identifies behavioral information based on predetermined parameters including user interactions with confidential data such as reading, note-taking, printing, emailing, and other actions. This parameter transformation enables the system to detect previously undetectable behaviors while maintaining detection accuracy.
2Adaptability or versatility
If a model-based system is implemented to monitor user behaviors, then potentially unwanted activity can be detected, but system complexity increases
Solution Approach 1:
The patent segments the behavior monitoring system into distinct functional modules: a behavioral information identification module that captures user interactions, a model creation module that processes behavioral data, and a detection module that compares behavior against models. This segmentation reduces system complexity by organizing functions into independent, manageable components that can operate autonomously.
Solution Approach 2:
The patent introduces behavioral models as intermediary structures that mediate between raw behavioral data and detection decisions. These models are created from collected behavioral information and serve as reference patterns for identifying potentially unwanted activity. The intermediary models simplify the detection process by providing a structured comparison framework rather than direct analysis of raw behavior data.
3Ease of operation
If traditional detection methods are used, then the system remains simple to operate, but it fails to detect unwanted activity based on user behaviors
Solution Approach 1:
The patent implements self-service functionality where the system automatically collects behavioral information, creates behavioral models, and performs detection without requiring manual configuration or intervention. The system autonomously identifies user behaviors, generates detection models from collected data, and compares new behaviors against these models to detect potentially unwanted activity, maintaining ease of operation while improving detection reliability.
Data Source
AI summary
A model-based system, method, and computer program product are provided for detecting at least potentially unwanted activity associated with confidential data. In use, behavior information associated with use of confidential data is identified, based on predetermined parameters. Additionally, a model is created utilizing the behavioral information. Furthermore, at least potentially unwanted activity associated with the confidential data is detected utilizing the model.