Dynamic File Access Control via User Behavior Risk Scoring
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Solution Overview
Problem
Current file access control systems face challenges with either perpetual access or temporary access policies, which are inflexible and require constant management, as they do not account for real-time user behavior, leading to potential security vulnerabilities and administrative burdens.
Innovation Solution
A machine-learning driven approach that encapsulates files with cryptographic wrappers and calculates a risk score based on historical and current user behavior, allowing for fine-grained and dynamic access control by determining whether to grant or deny access in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If policy-driven file access control is implemented, then access can be granted for a finite period or revoked when employees leave, but the system requires constant configuration and management by administrators as circumstances change
Solution Approach 1:
The system automatically adjusts file access control policies based on observed user behavior patterns without requiring manual administrator intervention. The machine learning model continuously monitors user activities and autonomously modifies access permissions, eliminating the need for constant policy configuration and management by administrators.
Solution Approach 2:
The system implements continuous feedback loops where user behavior is monitored, analyzed, and used to dynamically adjust access control decisions. The machine learning model processes ongoing user activity data and feeds back into policy adjustments, creating a self-adapting system that responds to changing circumstances automatically.
2Reliability
If policy-driven file access control is implemented, then access can be revoked when employees leave, but the overhead and potential errors of adding, updating, and deleting policies increases as the number of employees grows
Solution Approach 1:
The machine learning-driven system automatically manages access control policies without requiring administrator time for adding, updating, or deleting policies. The system self-adjusts permissions based on user roles and behavior patterns, eliminating the time-consuming manual policy management that scales poorly with organizational growth.
Solution Approach 2:
The system dynamically changes access control parameters based on user behavior metrics and organizational context. Instead of static policies that require manual updates, the system continuously adjusts access parameters automatically, reducing administrative time overhead while maintaining security.
3Adaptability or versatility
If machine-learning driven access control is implemented, then fine-grained and dynamic access control can be applied, but the system requires on-access validation of user rights and risk score calculation
Solution Approach 1:
The system performs preliminary risk assessment and user validation before granting file access. By calculating risk scores and validating user rights in advance through automated machine learning models, the system enables fine-grained dynamic access control without requiring complex real-time validation mechanisms during actual file operations.
Data Source
AI summary
Systems and methods for a machine-learning driven fine-grained file access control approach are provided. According to one embodiment, a server associated with an enterprise network can obtain and store information regarding historical user behavior of users of the enterprise network by observing file access requests initiated by the users. The server receives a file access request initiated by a user, which relates to a file stored within the enterprise network in encrypted form. In response to receipt of the file access request, the server determines a risk score for the user based on multiple factors, including information regarding historical user behavior, the file access request and observed data determined based on the file access request so that based on the risk score, access to the file is permitted by returning a decryption key for the file or denied by withholding the decryption key.