Automated Access Control Policy Generation via Historical Data Analysis
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Solution Overview
Problem
Existing security solutions require manual configuration of access control rules for computer resources, which is time-consuming and often leads to incomplete or ineffective configurations, increasing the vulnerability of systems to attacks.
Innovation Solution
An automated system generates access control rules based on historical user access data, separating it into training and validation sets to determine valid rules that limit access to only necessary users, using machine learning and statistical analysis to improve security without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of access control rules is performed, then security policies can be customized for each user and resource, but the process becomes time-consuming and administratively burdensome
Solution Approach 1:
The system automatically generates access control rules by analyzing historical access data and user behavior patterns without requiring manual administrator intervention. The automated rule generation engine processes access requests, identifies patterns, and creates security policies autonomously, eliminating the time-consuming manual configuration process while maintaining effective security controls
Solution Approach 2:
The system pre-processes historical access data to establish baseline user behavior patterns and access relationships before security threats occur. By analyzing past access patterns in advance, the system prepares access control rules that are ready to be applied immediately, reducing the time required for security configuration while ensuring reliable protection
2Adaptability or versatility
If manual analysis of files and system logs is performed to create tailored policies, then customized access control rules can be generated, but the process requires significant time and resources
Solution Approach 1:
The system replaces manual mechanical analysis of files and system logs with an automated computational engine that uses machine learning algorithms to analyze access patterns. The automated engine processes large volumes of access data rapidly, identifying relationships and generating customized access control rules without requiring administrator time or resources for manual analysis
Solution Approach 2:
The system creates standardized templates for access control rules based on analyzed access patterns, which can be automatically replicated and applied across multiple users and resources. Once access control patterns are identified through analysis, the system generates template rules that can be efficiently copied and adapted, maintaining policy customization while dramatically improving creation efficiency
3Reliability
If access control rules are manually maintained, then security policies can be updated, but the process requires accessing each machine separately and involves significant administrative overhead
Solution Approach 1:
The system creates a centralized access control management platform that can remotely update and maintain security policies across multiple machines simultaneously. The automated rule generation and maintenance system provides universal functionality to manage access control across diverse systems through a single interface, eliminating the need to access each machine separately and reducing management complexity while maintaining reliable security
Solution Approach 2:
The system introduces an automated access control rule generation engine as an intermediary between security administrators and multiple target machines. This intermediary component handles the complex task of analyzing access patterns, generating rules, and distributing updates across all machines, simplifying the management process for administrators while ensuring consistent security maintenance across the entire system
4Object-affected harmful factors
If comprehensive access control rules are implemented, then the attack surface is reduced, but the configuration process becomes more complex and error-prone
Solution Approach 1:
The system automatically generates comprehensive access control rules by analyzing historical access data and identifying necessary security policies without requiring manual configuration. The automated engine processes access patterns and creates rules that comprehensively address security needs while avoiding configuration errors, reducing the attack surface without increasing configuration complexity
Solution Approach 2:
The system continuously monitors access patterns and validates generated access control rules against actual usage data. By implementing feedback loops that analyze whether generated rules are effectively reducing the attack surface while maintaining necessary access, the system automatically refines and adjusts configurations, ensuring comprehensive security coverage without manual intervention or increased complexity
Data Source
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AI summary
Methods, systems, and media are shown for generating access control rules for computer resources involving collecting historical access data for user accesses to a computer resource and separating the historical access data into a training data set and a validation data set. An access control rule is generated for the computer resource based on the properties of the user accesses to the computer resource in the training data set. The rule is validated against the validation data set to determine whether the rule produces a denial rate level is below a threshold when the rule is applied to the validation data set. If the rule is valid, then it is provided to an administrative interface so that an administrator can select the rule for application to incoming user requests.