Dynamic Access Permission Management via Predictive Misuse Analysis
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Solution Overview
Problem
Existing token-based authentication and consent processes in computing environments lack real-time mechanisms to determine the likelihood of misuse or breach by third-party systems accessing confidential data, failing to dynamically adapt access permissions accordingly.
Innovation Solution
A computing system employs adaptively trained predictive models to analyze interactions between third-party systems and programmatic interfaces, generating predicted outcome data on the likelihood of misuse and modifying access permissions, with permissioning data recorded on a distributed ledger to ensure secure and compliant data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If token-based authentication and consent processes are used, then access permission management is established, but real-time detection of misuse likelihood is not available
Solution Approach 1:
The system continuously monitors third-party system interactions and uses this feedback to update predictive models in real-time, enabling dynamic assessment of misuse likelihood based on actual behavior patterns rather than static permissions
Solution Approach 2:
The patent replaces traditional mechanical token-based authentication with an AI/ML-based predictive modeling system that analyzes interaction patterns, behavioral metrics, and contextual data to assess misuse risk dynamically
2Device complexity
If static access permissions are granted, then system simplicity is maintained, but dynamic adaptation to misuse risk is not achieved
Solution Approach 1:
The system transitions from static access permissions to dynamic, real-time permission adjustments based on predicted misuse likelihood, allowing access rights to change automatically as risk assessments are updated
Solution Approach 2:
The patent changes the state of access permissions from fixed binary (granted/denied) to variable parameters that can be adjusted based on risk scores, enabling fine-grained control over access levels dynamically
3Reliability
If real-time monitoring of third-party systems is implemented, then security is enhanced, but computational resources are consumed
Solution Approach 1:
The system monitors only specific critical interaction parameters and uses selective sampling of data points rather than continuous full-state monitoring, reducing computational load while maintaining effective security oversight
Solution Approach 2:
The patent optimizes resource consumption by dynamically adjusting the granularity and frequency of monitoring based on risk levels, concentrating computational resources on high-risk interactions while using minimal resources for low-risk operations
Data Source
AI summary
The disclosed exemplary embodiments include computer-implemented systems, apparatuses, and processes that dynamically manage consent, permissioning, and trust between computing systems that maintain confidential data and unrelated third-party applications. By way of example, an apparatus may obtain interaction data that identifies an interaction between an application program executed at a first computing system and a programmatic interface of a second computing system. Based on the interaction data, the apparatus may generate outcome data characterizing a probability that the requested access to the data element is inconsistent with an access permission granted to the executed application program, and may modify the access permission in accordance with the outcome data. The apparatus may also perform that generate permissioning data indicative of the modified access permission and that store the permissioning data within a locally accessible or cloud-based repository.