Virtual Resource Matching With Dynamic Vulnerability Rules
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Solution Overview
Problem
Existing systems fail to account for dynamic user preferences and unique characteristics of virtual resources, leading to inefficiencies and vulnerabilities in the transmission of assets like cryptocurrencies and stocks, as they rely on static thresholds and surveys that do not adapt to changing user risk appetites or resource-specific indicia.
Innovation Solution
A system employing parameterized rules that assess vulnerability by extracting user and resource-specific parameters, using a combination of local and remote storage to minimize redundancy and leveraging machine learning for efficient risk evaluation, allowing temporary rejections or overrides based on user profiles and asset classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static thresholds and surveys are used to assess vulnerability, then the system is simple to implement, but it cannot adapt to dynamic user preferences and resource-specific characteristics
Solution Approach 1:
The system transitions from static thresholds to dynamic, parameterized rules that automatically adapt to changing user preferences and resource characteristics. The vulnerability assessment continuously updates based on user profile changes and resource-specific indicia, enabling real-time adaptation without manual reconfiguration.
Solution Approach 2:
The system uses parameterized rules with multiple configurable parameters (e.g., risk tolerance thresholds, validation strictness levels) that can be dynamically adjusted based on user profiles and resource types. This allows fine-grained adaptation to different scenarios while maintaining a unified assessment framework.
2Measurement precision
If extensive surveys are required to capture user risk appetite, then user preferences can be accurately assessed, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary actions by pre-defining parameterized rules and vulnerability indicators before actual transmission occurs. User risk appetite is captured through concise profile inputs rather than extensive surveys, and the system pre-processes this information into actionable assessment criteria that can be rapidly applied to multiple resources.
Solution Approach 2:
The system enables users to self-profile their risk appetite through intuitive interfaces, and automatically maintains and updates their profiles based on behavior patterns. This eliminates the need for continuous manual input while preserving accurate risk assessment, allowing users to serve their own profiling needs.
3Measurement precision
If individual tests are designed specific to each user and virtual resource, then assessment accuracy improves, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system creates a universal vulnerability assessment framework that can evaluate any virtual resource type (files, media, code, cryptocurrencies) using a common set of parameterized rules. This multi-functional approach maintains high assessment accuracy across different resource types while eliminating the need to design separate tests for each scenario.
Solution Approach 2:
The assessment process is segmented into modular, parameterized rules that can be independently configured for different resource types. Each rule handles a specific vulnerability indicator (e.g., file type validation, risk threshold checking), allowing the system to combine these modular segments into comprehensive assessments without redesigning the entire system for each resource type.
4Reliability
If multiple test suites with duplicate rules are maintained, then comprehensive coverage of vulnerability indicators is achieved, but storage resources are wasted
Solution Approach 1:
The system merges duplicate vulnerability rules into a single unified parameterized rule set that can be dynamically instantiated for different resource types. Instead of maintaining separate test suites for files, media, code, and cryptocurrencies, the system combines common vulnerability indicators into shared parameters that automatically adapt to each resource type, eliminating redundancy while preserving comprehensive coverage.
Data Source
AI summary
Systems and methods for assessing vulnerability of virtual resources are disclosed herein. A system may receive a request and parameterized rules from a database of parameterized rules for assessing potential vulnerability of user resources caused by a virtual resource transmission. The system may extract user vulnerability tolerance parameters indicative of user propensity for vulnerability and virtual resource vulnerability tolerance parameters indicative of vulnerability of the virtual resource. The system may input, into a parameterized rule, a user vulnerability tolerance parameter and a virtual resource vulnerability tolerance parameter to trigger execution of each parameterized rule. The system may then generate a match indicator indicating whether the virtual resource matches the user transmissions profile and, responsive to determining that the virtual resource does not match the user transmissions profile, trigger a temporary rejection on the transmission request requiring an override authorization.