Dynamic Authentication for Cloud Asset Access Control
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Solution Overview
Problem
Cloud-based computing systems face security vulnerabilities due to the large number of users accessing multiple devices and computing assets, which increases the risk of data breaches, especially as the number of users and applications grows.
Innovation Solution
An asset access learning system employing machine learning algorithms, such as neural networks, to analyze user access data, generate risk analytics, and dynamically control authentication processes and access privileges, enhancing security by providing real-time risk assessments and recommendations to users and system administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud-based computing systems provide broad access to multiple devices and computing assets for increased productivity, then accessibility and productivity improve, but security vulnerabilities and data breach risks increase
Solution Approach 1:
The patent implements dynamic authentication that adjusts security requirements in real-time based on user behavior patterns, device characteristics, and access context. The system transitions from static access control to dynamic risk-based authentication, where security measures adapt to current conditions rather than applying uniform rules to all access attempts.
Solution Approach 2:
The system incorporates continuous monitoring of user behavior and access patterns, using this feedback to adjust authentication requirements. Machine learning algorithms analyze historical and real-time data to identify anomalies and modify security responses, creating a closed-loop system that learns and adapts to emerging threats while maintaining productivity.
2Adaptability or versatility
If the number of users and applications grows to meet organizational needs, then service coverage and utility improve, but security threats and vulnerability exposure increase
Solution Approach 1:
The patent segments the authentication process into multiple factors and layers, including device authentication, user identity verification, behavioral analysis, and contextual risk assessment. This segmented approach allows the system to apply appropriate security measures to different aspects of access control independently, managing complexity while enhancing security for diverse user and application combinations.
Solution Approach 2:
The system changes security parameters dynamically based on the specific combination of user, device, application, and context. Rather than treating all access requests uniformly, the system adjusts authentication strength, required factors, and monitoring levels according to risk parameters derived from system growth and usage patterns.
Data Source
AI summary
Access to computing assets is controlled by dynamically selecting an authentication process for an access attempt to a computing asset. In an example embodiment, when an indication of an access attempt for a computing asset is received, a security level associated with the computing asset is determined. Based on the security level associated with the computing asset, an authentication process is selected from a plurality of authentication processes, and the selected authentication process is executed in relation to the access attempt for the computing asset. In further embodiments, the authentication process is further selected based on a comparison of an access characteristic associated with the access attempt for the computing asset and an access characteristic for a user associated with the access attempt.