Access Rule Management via Janitor Computer Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Resource providers face inefficiencies due to the proliferation of unused or rarely used access rules in their decision management systems, leading to excessive storage usage, processor drain, and difficulty in determining which rules to maintain or remove, as they are reluctant to remove rules fearing negative impacts on their business and the complexity of determining their functionality.
Innovation Solution
A system and method utilizing a 'janitor computer' to track and analyze the triggering rates of access rules over time, allowing for the identification of unused or rarely used rules based on counter increments and triggering percentage analysis, enabling resource providers to determine whether rules should be removed, updated, or maintained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resource providers maintain all generated rules to prevent fraudulent access, then security coverage is improved, but system complexity and storage requirements increase excessively
Solution Approach 1:
The system performs preliminary tracking and analysis of rule usage patterns before making removal decisions. By continuously monitoring triggering rates and storing usage data, the system prepares information about which rules are actually needed, enabling informed decisions about rule maintenance without compromising security coverage.
Solution Approach 2:
The system implements feedback mechanisms by tracking rule triggering rates and usage patterns over time. This feedback information is used to evaluate whether rules should be maintained or removed, creating a closed-loop system that adapts rule management decisions based on actual usage data rather than static assumptions.
2Reliability
If resource providers continue to maintain all rules in the system, then rule coverage is preserved, but processor time and storage resources are consumed excessively
Solution Approach 1:
The system performs preliminary tracking and analysis of rule usage patterns before making removal decisions. By continuously monitoring triggering rates and storing usage data, the system prepares information about which rules are actually needed, enabling informed decisions about rule maintenance without compromising security coverage.
Solution Approach 2:
The system identifies and removes unused or rarely used rules from the active rule set, discarding rules that consume resources but provide no security value. The tracking and analysis mechanisms recover information about rule usage patterns to ensure only appropriate rules are removed, maintaining security coverage while reducing resource consumption.
3Measurement precision
If resource providers manually review each rule to determine applicability, then rule management precision is improved, but time consumption increases significantly
Solution Approach 1:
The system performs self-service by automatically tracking and analyzing rule usage patterns without requiring manual intervention. The janitor computer autonomously monitors triggering rates, stores usage data, and generates recommendations about which rules should be maintained or removed, eliminating the need for manual rule review while maintaining precision through data-driven decisions.
Solution Approach 2:
The system replaces manual mechanical review processes with automated electronic tracking and analysis mechanisms. The janitor computer uses software-based monitoring of rule triggering rates and usage patterns, substituting human manual review with automated digital processes that achieve equivalent or superior precision without the time consumption of manual inspection.
4Reliability
If resource providers add more rules to address new fraud types, then security coverage is improved, but the number of rules proliferates and becomes unwieldly
Solution Approach 1:
The system performs preliminary tracking and analysis of rule usage patterns before making removal decisions. By continuously monitoring triggering rates and storing usage data, the system prepares information about which rules are actually needed, enabling informed decisions about rule maintenance without compromising security coverage.
Solution Approach 2:
The system changes the parameter of rule management from static maintenance to dynamic optimization based on usage data. By monitoring triggering rates and usage patterns over time, the system adjusts the active rule set to maintain only necessary rules, optimizing the balance between security coverage and rule quantity through data-driven parameter changes.
Data Source
AI summary
Techniques managing access rules are provided. Access rules and their associated profiles are determined for evaluation. A triggering rate or a triggering percentage can be used to indicate efficacy of the rule. Recommendations can be provided based on a triggering percentage difference of the rule during a predetermined period of time. The recommendations can be provided in an interactive user interface.


