ML Risk Assessment for Dynamic Resource Access Control

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Solution Overview

Problem

Existing systems lack an efficient method to manage resource access permissions for users accessing both secured and unsecured computer systems, leading to potential risks of unauthorized access and misuse.

Innovation Solution

A system that uses a machine learning model to process resource access requests, determining a measure of risk and allowing access to unsecured resources if the risk is below a threshold, while utilizing secured resources for higher risk activities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If a user is allowed to access both secured and unsecured resources, then the user convenience and accessibility are improved, but the security risk and potential for unauthorized access increase

Engineering Contradiction:

Improveuser access convenienceVSAvoidsecurity risk

Core Design Contradiction:

Ease of operationVSObject-affected harmful factors

Solution Approach 1:

The system dynamically adjusts access permissions based on real-time risk assessment. The machine learning model continuously evaluates user behavior patterns, resource access history, and contextual information to dynamically grant or revoke access to secured resources, allowing users to access both secured and unsecured resources when safe while blocking access when risk is detected

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The machine learning risk assessment system acts as an intermediary between the user and the resource access control mechanism. It receives user access requests, evaluates them against established risk models, and mediates the access decision by allowing or denying access based on the risk assessment, thereby enabling convenient access while mitigating security risks

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If access control measures are implemented for all resources, then security against unauthorized access is improved, but the ease of operation and user convenience deteriorate

Engineering Contradiction:

Improveaccess control securityVSAvoiduser access convenience

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system applies different access control qualities to different resources based on their security characteristics. Unsecured resources are accessible to verified users without additional access control measures, while secured resources require risk assessment. This local differentiation allows the system to maintain security where needed while providing convenience where safe

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The machine learning model performs self-service risk assessment automatically in the background, analyzing user behavior patterns and access requests without requiring manual security reviews or user intervention. This automated self-service approach maintains strong security controls while preserving user convenience by handling security evaluations transparently and automatically

Inventive Principle:

Principle #25Self-service

3Reliability

If verified users are restricted to secured resources only, then the security posture is improved, but the quantity of accessible resources and user convenience deteriorate

Engineering Contradiction:

Improvesecurity postureVSAvoidaccessible resources

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system dynamically determines which resources a verified user can access based on real-time risk assessment. Rather than statically restricting verified users to secured resources only, the system dynamically evaluates each access request and allows access to unsecured resources when the risk assessment indicates safety, thereby expanding accessible resources while maintaining security

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250158820A1Systems and methods for managing resource access permissions

Publication Date: 2025.05.15 CAPITAL ONE SERVICES LLC

AI summary

Systems and methods for managing resource access permissions for users with secured and unsecured authentication tokens. In some aspects, the system may receive, from a user, a resource access request for a system. The resource access request may include a user identifier and a first activity for the resource access request. The system may retrieve information regarding authentication tokens associated with the user usable to process the resource access request. In response to determining whether the user is associated with the secured authentication token and the unsecured authentication token, the system may process, using a machine learning model, the resource access request to determine a measure of risk to the system that is associated with executing the resource access request for the user. In response to determining that the measure of risk is below a threshold, the system may process the resource access request using the unsecured authentication token.