Computing Object Risk Mapping by Sensitive Data and User Access
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Solution Overview
Problem
Existing data management systems lack effective methods for identifying and visualizing computing objects with sensitive information and associated risk levels, making it difficult to manage and mitigate potential data security risks.
Innovation Solution
A data management system (DMS) that identifies sensitive information in computing objects, calculates risk levels based on the quantity of sensitive information and user access, and provides intuitive visualizations in a user interface to facilitate risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a data management system stores and manages large volumes of data from multiple computing systems, then data management capabilities are improved, but the ability to identify and visualize high-risk objects with sensitive information deteriorates due to system complexity
Solution Approach 1:
The system extracts and isolates sensitive information from large volumes of data by implementing classification mechanisms that identify PII, classified information, and other sensitive data types. This extraction approach allows the system to separate high-risk objects from the broader data set, making them visible and manageable despite the overall data volume increase.
Solution Approach 2:
The system employs visual indicators and color-coded risk levels in the user interface to represent different sensitivity and risk categories. High-risk objects are visually distinguished through color changes and graphical representations, enabling administrators to quickly identify and prioritize security concerns without being overwhelmed by the total data quantity.
2Measurement precision
If the system calculates risk levels for all computing objects based on sensitive information quantity and user access, then risk assessment accuracy is improved, but processing time and system resources increase
Solution Approach 1:
The system performs preliminary classification of data during the backup and storage process, tagging sensitive information and calculating initial risk levels before the data is fully ingested. This advance preparation reduces the computational burden during risk assessment operations, maintaining accuracy while reducing processing time when risk evaluations are requested.
Solution Approach 2:
The system implements a tiered risk assessment approach where critical computing objects receive comprehensive, high-precision risk analysis, while less critical objects receive streamlined assessments. This partial action strategy focuses computational resources on high-priority targets, achieving sufficient risk measurement precision for the most important objects without proportionally increasing processing time across the entire system.
3Loss of information
If the user interface displays detailed information about all computing objects, then information completeness is improved, but ease of operation deteriorates due to interface complexity
Solution Approach 1:
The user interface is segmented into multiple views and levels of detail, allowing administrators to access comprehensive information when needed while defaulting to simplified summaries for routine operations. High-risk objects are presented in dedicated sections with prominent visual indicators, separating critical information from general data and improving navigability without sacrificing information availability.
Solution Approach 2:
The system adds visual dimensions to the interface through graphical representations, color coding, and spatial arrangement of computing objects based on risk levels. This transforms flat, text-heavy information into a multi-dimensional visual landscape where risk priority is immediately apparent, maintaining information completeness while dramatically improving ease of operation through intuitive visual navigation.
Data Source
AI summary
Methods, systems, and devices for data management are described. A data management system may determine risk levels for a plurality of computing objects. A risk level for a computing object is determined based on a respective quantity of elements comprising sensitive information within the computing object and a respective quantity of users with access to the computing object. The data management system may display the visualizations for a selected subset of computing objects in a user interface. The visualizations for the computing objects may be positioned within the user interface based on respective quantities of elements comprising sensitive information within the computing objects and further based on respective quantities of users with access to the computing objects. The user interface may be configured to receive a selection of a time period and reposition the visualizations of the computing objects within the user interface.