ML Identity Access Management for Dynamic Policy Adaptation
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Solution Overview
Problem
Current data security systems for companies rely on static security policies that require manual updates and lack a streamlined approach for user information storage, authentication, and access authorization, leading to increased workload for IT professionals and inefficiencies in threat mitigation.
Innovation Solution
A machine learning-based identity access management system using a backpropagation technique within an artificial neural network algorithm to classify transactions as normal or abnormal, allowing or mitigating access based on real-time data updates and dynamic policy adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static security policy is used for identity access management, then security operations can be carried out, but additional manual input from security personnel is required and the system cannot adapt dynamically to new threats
Solution Approach 1:
The system enables self-service through machine learning models that automatically analyze transaction data, classify transactions as normal or abnormal, and update security policies without requiring manual security personnel intervention. The model continuously learns from new data and autonomously adjusts security parameters.
Solution Approach 2:
The system implements feedback loops where transaction outcomes are continuously fed back to the machine learning model, which then updates its classification parameters and security policies. This closed-loop feedback mechanism enables dynamic adaptation as the model learns from actual system performance and emerging threat patterns.
2Productivity
If separate programs and databases are used for user information storage, authentication, and access authorization, then data security functions can be performed, but the workload for IT professionals increases and processes are not streamlined
Solution Approach 1:
The patent merges multiple separate security functions (user information storage, authentication, access authorization, and transaction classification) into a unified machine learning-based identity access management system. This consolidation integrates previously separate programs and databases into a single streamlined architecture that reduces complexity while improving processing efficiency.
Solution Approach 2:
The machine learning model serves multiple functions simultaneously: it stores user information, authenticates users, authorizes access, and classifies transactions. This multi-functional approach eliminates the need for separate specialized programs for each security operation, thereby reducing overall system complexity and improving productivity.
3Reliability
If traditional security systems are used, then basic authentication and authorization can be performed, but real-time threat mitigation and dynamic policy updates are not achieved
Solution Approach 1:
The system transitions from static security policies to dynamic, adaptive security parameters through machine learning. The model continuously adjusts security thresholds and classification criteria based on real-time transaction data analysis, enabling the system to respond dynamically to emerging threats while maintaining reliable authentication and authorization.
Data Source
AI summary
A computer readable medium, a system, and a method for providing data security through identity access management using a transaction classifier to classify transactions according to a set of transaction data associated with the transaction and mitigate abnormal transactions. The transaction classifier is trained using a set of training data and updated after each transaction. The identity access management may also include a mitigation policy that is used to determine a mitigation technique for each transaction.