Cloud Access Rights Model Using ML Clustering
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Solution Overview
Problem
Managing access control in cloud computing environments is challenging due to complexity, fragmentation of security protocols, difficulty in obtaining user details, and incorrect access levels, which can lead to data breaches and non-compliance with industry standards.
Innovation Solution
A security monitoring platform utilizing a combination of unsupervised, supervised, and reinforcement learning techniques to model user access rights across multiple cloud applications, cluster historical data, train an access rights data model, and update it based on feedback to ensure accurate access levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional access control methods are used in cloud computing environments, then implementation is straightforward with single protocols, but security management becomes complex and fragmented across multiple applications
Solution Approach 1:
The patent combines multiple security protocols and access control systems into a unified machine learning model that manages access rights across cloud applications. The system integrates historical access data from multiple sources and applies consolidated security policies, reducing management complexity while maintaining consistency across fragmented environments.
Solution Approach 2:
The access rights data model serves multiple functions: it clusters historical data, predicts correct access levels, generates security policies, and provides feedback mechanisms. This universal model handles diverse access control scenarios across different cloud applications, replacing multiple specialized protocols with a single multi-functional system.
2Measurement precision
If manual access rights management is implemented, then detailed user control is achievable, but resource consumption increases and accuracy decreases
Solution Approach 1:
The system performs preliminary clustering of historical access data using unsupervised learning to organize information before making access decisions. By pre-processing and structuring data in advance, the model reduces computational overhead during real-time access evaluations, achieving both accuracy and efficiency.
Solution Approach 2:
The machine learning model applies partial automation by focusing computational resources on evaluating specific access patterns and user behaviors rather than processing all access requests uniformly. This selective approach improves accuracy for critical decisions while conserving computational resources.
3Ease of operation
If access rights are freely assigned without verification, then user convenience is improved, but security risks increase due to incorrect access levels
Solution Approach 1:
The system implements continuous feedback loops where the reinforcement learning component monitors access decisions and their outcomes. Correct access level assignments are rewarded, while incorrect assignments trigger corrections and learning updates. This feedback mechanism maintains security by learning from past decisions while preserving user convenience through automated, accurate access management.
Solution Approach 2:
The machine learning model autonomously evaluates access requests, determines correct access levels, and enforces security policies without requiring manual security team intervention. The system self-corrects incorrect access assignments and continuously improves its decision-making, providing both convenience and security through automated self-service.
4Measurement precision
If comprehensive historical data is collected for access analysis, then model accuracy is improved, but data processing time and complexity increase
Solution Approach 1:
The system segments historical access data into distinct clusters using unsupervised learning, organizing large volumes of data into manageable groups based on similarity. This segmentation reduces processing complexity by handling clustered data patterns rather than individual records, maintaining prediction accuracy while reducing processing time.
Solution Approach 2:
The machine learning model transforms raw historical data into optimized feature representations and probability scores that are more efficient to process. By changing data parameters from raw logs to structured clusters and probability distributions, the system improves prediction accuracy while reducing the computational burden of processing comprehensive historical records.
Data Source
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AI summary
A security monitoring platform may use an unsupervised machine learning technique to cluster historical data related to user access rights associated with multiple cloud applications based on various features that relate to user permissions and attributes within the multiple cloud applications. The security monitoring platform may use a supervised machine learning technique to train an access rights data model based on the clustered historical data and perform one or more actions that relate to current access rights assigned to at least one user within one or more of the multiple cloud applications based on a score representing a probability that an access level assigned to the at least one user within the one or more of the multiple cloud applications is correct. The security monitoring platform may apply a reinforcement learning technique to update the access rights data model based on feedback related to the one or more actions.