Automated Asset Discovery and Risk Evaluation
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Solution Overview
Problem
Current data systems face challenges in discovering and managing assets, particularly unknown assets, due to their reliance on manual registration and maintenance, leading to inadequate asset inventory and increased vulnerability to security breaches.
Innovation Solution
The described techniques employ advanced machine learning and artificial intelligence to automatically discover, classify, and risk-rank assets and users across various environments, using human reinforcement learning and machine accumulative learning to identify both known and unknown assets, and deduce user activities, thereby enhancing data protection and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual registration and maintenance methods are used for asset management, then system simplicity is maintained, but asset discovery completeness deteriorates and security vulnerability increases
Solution Approach 1:
The patent replaces manual registration and maintenance mechanisms with automated machine learning-based asset discovery systems. The system automatically discovers, classifies, and inventories assets across IT, OT, and IoT environments without requiring manual intervention, thereby improving asset inventory accuracy while reducing reliance on complex manual processes
Solution Approach 2:
The system enables self-service asset discovery by automatically detecting and inventorying assets across multiple environments. The machine learning models autonomously identify known and unknown assets, classify data confidentiality levels, and calculate risk scores without human intervention, allowing the system to maintain and update asset inventories independently
2Ease of operation
If manual asset registration is used, then implementation ease is maintained, but security vulnerability increases due to unknown assets
Solution Approach 1:
The patent replaces manual asset registration with automated machine learning-based discovery systems that actively scan and identify assets across IT, OT, and IoT environments. This substitution eliminates security vulnerabilities associated with unknown assets by ensuring complete asset visibility without requiring complex manual registration processes
Solution Approach 2:
The system performs preliminary asset discovery and classification before security risks can manifest. By proactively identifying and inventorying assets including unknown ones, the system prevents security vulnerabilities from developing, allowing security measures to be implemented in advance rather than reactively
3Reliability
If automated machine learning-based asset discovery is implemented, then asset discovery completeness improves, but system complexity increases
Solution Approach 1:
The patent segments the asset discovery system into specialized machine learning models for different environments (IT, OT, IoT) and different functions (asset discovery, data classification, risk scoring). This segmentation allows each component to be optimized independently while working together to achieve complete asset discovery across diverse environments without overwhelming system complexity
Solution Approach 2:
The system implements universal machine learning models that can operate across multiple technology environments (IT, OT, IoT) and perform multiple functions (asset discovery, data classification, risk calculation). This multi-functionality reduces overall system complexity by using a unified approach rather than separate specialized systems for each function and environment
4Measurement precision
If comprehensive data collection from remote locations is performed, then asset discovery accuracy improves, but data processing time increases
Solution Approach 1:
The patent extracts and retrieves data from remote locations and applies machine learning models to discover assets, classify data, and calculate risks. By selectively extracting only the necessary data elements needed for accurate asset discovery and classification, the system achieves high measurement precision while minimizing unnecessary data processing time
Data Source
AI summary
Methods, systems, and devices for asset discovery, user discovery, data classification, risk evaluation, and data/device security are described. The method includes retrieving data stored at one or more remote locations, summarizing the retrieved data at the one or more remote locations, transferring the summarized data from the one or more remote locations to the at least one computing device, processing the transferred data by the at least one computing device, discovering assets in technology environments, classifying data that resides on each asset of the discovered assets into a respective confidentiality group of multiple confidentiality groups, calculating one or more risk scores for the discovered assets or users of the discovered assets, or both, and performing a security action to protect data that resides on an asset of the discovered assets.