AI Identity Governance for Access Entitlement Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large organizations face challenges in managing user access entitlements in complex, distributed networked computing environments, leading to inefficiencies in compliance efforts and increased security risks due to inadequate monitoring and evaluation of access landscapes.
Innovation Solution
The implementation of artificial intelligence-based identity governance systems that utilize machine learning models, such as isolation forest models, to identify common or unique access items by analyzing identity management data and generating predictive scores, thereby improving identity governance and reducing computational burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual methods are used to manage user access entitlements in large organizations, then compliance monitoring can be applied uniformly across all users and applications, but this results in wasted time, labor, and resources due to the complexity of managing thousands of users and hundreds of applications
Solution Approach 1:
The patent transforms the management approach by changing parameters from uniform manual review to AI-driven automated analysis. The system uses machine learning models to dynamically prioritize access items based on risk parameters, commonality metrics, and anomaly detection, automatically adjusting the depth and focus of compliance monitoring without uniform manual intervention across all users and applications.
Solution Approach 2:
The patent replaces manual mechanical processes of access entitlement management with an AI-based automated system. The machine learning model automatically analyzes identity management data, generates predictive scores for commonality and uniqueness, and prioritizes access items for compliance review, substituting human labor with intelligent automated processing capable of handling thousands of users and hundreds of applications simultaneously.
2Reliability
If comprehensive compliance monitoring is applied to all access items, then no access risk is overlooked, but organizations cannot establish baseline measurements or quantify improvements over time
Solution Approach 1:
The patent implements feedback mechanisms where the AI system continuously monitors access entitlements, generates predictive scores, and establishes baseline measurements of access risk. The system tracks changes over time, compares current state against historical baselines, and provides feedback on compliance improvements, enabling organizations to quantify security posture evolution and demonstrate effective risk reduction.
Solution Approach 2:
The patent performs preliminary analysis of all access items to establish baseline measurements before compliance enforcement. The machine learning model pre-calculates predictive scores for commonality and uniqueness, identifies high-risk access patterns in advance, and creates a baseline security profile that enables subsequent measurement of compliance improvements and risk reduction effectiveness.
3Productivity
If AI-based analysis is used to identify common or unique access items, then compliance efforts can be focused on areas of greatest risk, but this requires sophisticated machine learning models and identity management data infrastructure
Solution Approach 1:
The patent creates a universal AI-based identity governance system that performs multiple functions: analyzing identity management data, generating predictive scores for access item commonality and uniqueness, prioritizing compliance review items, establishing baselines, and measuring improvements. This multi-functional system consolidates what would otherwise require separate tools and processes into a single integrated platform, managing complexity through consolidation rather than proliferation of separate systems.
Data Source
AI summary
Methods and systems for identity governance that provide for the identification of common or unique access items (e.g., identity management artifacts that may grant access). Certain embodiments may leverage representative data structures that represent an enterprise's identity management data to determine common or unique identity management access items represented in those data structures. In other embodiments, a machine learning model may be trained based on identity management data and utilize predictive scores to determine common or unique identity management access items.