Data Security Model Anomaly Detection via Interdependency Links
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Solution Overview
Problem
Conventional data security models in distributed computing environments lack a comprehensive, detailed, and dynamically modifiable data structure to account for interdependencies and identify anomalies in third-party products, such as missing data security controls.
Innovation Solution
A dynamically modifiable data security model with multiple data objects and links defining interdependency parameters, which allows for the identification of anomalies by comparing product data entries with applicable data objects and controls within the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive data security model with multiple data objects and interdependency links is implemented, then the ability to identify anomalies in third-party products is improved, but the device complexity increases
Solution Approach 1:
The data security model is segmented into multiple data objects (e.g., data element, data attribute, data type, data structure) with hierarchical levels. Each segment represents a specific aspect of data security, allowing complex interdependencies to be managed through structured decomposition while maintaining comprehensive anomaly detection capability.
Solution Approach 2:
The model introduces a hierarchical dimension with multiple levels (first level, second level, third level) to organize data security concepts. This dimensional structure enables systematic representation of complex relationships between data objects, facilitating thorough anomaly identification without overwhelming complexity in a single flat structure.
2Adaptability or versatility
If data security controls are dynamically modifiable to account for interdependencies, then the adaptability to different product implementations is improved, but the ease of operation decreases
Solution Approach 1:
The data security model employs dynamic characteristics where data objects can be added, modified, or removed based on specific product implementations. The interdependency links between data objects can be dynamically adjusted to reflect changing security requirements, enabling the model to adapt to diverse third-party product structures while maintaining operational manageability through structured updates.
3Reliability
If detailed data object interdependency parameters are defined, then the reliability of anomaly detection is improved, but the manufacturing precision requirements increase
Solution Approach 1:
Different levels of data objects have different levels of detail and precision requirements. Critical data objects with high interdependency have more detailed definitions and stricter precision requirements, while less critical objects have simplified definitions. This local differentiation enables reliable anomaly detection in critical areas without requiring uniform high precision across the entire model.
Data Source
AI summary
Systems, methods, and computer program products are provided herein for data security model based anomaly determinations. An example method includes receiving a product evaluation request that is associated with a first product dataset including product data entries and accessing a data security model. The data security model includes a plurality of data objects including one or more data entries where each data object defines an associated model level indicative of the hierarchical position of the data object within the data security model and one or more links between the data objects that define data object interdependency parameters. The example method includes determining data objects of the data security model applicable to the first product dataset and determining one or more anomalies associated with the first product dataset based on a comparison between the one or more product data entries and the applicable data objects of the data security model.