AI Security Control Generation from Test Data
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Solution Overview
Problem
Existing systems lack an automated method to deploy security controls in production environments when changes occur, requiring manual review and reconfiguration of security settings.
Innovation Solution
A system and method that utilize historical data from test management systems and artificial intelligence to automatically generate and apply new security controls in production environments when configuration changes are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review and reconfiguration of security settings is performed when production configuration changes, then security controls can be accurately adjusted, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary action by pre-training an AI model on historical testing data that maps configuration parameters to security controls. When a new production configuration is detected, the pre-trained model can immediately generate appropriate security controls without requiring manual review, thus reducing deployment time while maintaining accuracy through the model's training on previously verified secure configurations
Solution Approach 2:
The system copies security controls from historical testing configurations to production environments. The AI model learns from historical data where configurations and their corresponding security controls were verified during testing. When a new production configuration is detected, the model copies and adapts appropriate security controls from similar historical configurations, enabling rapid deployment while maintaining security standards
2Manufacturing precision
If security controls are manually configured for each production change, then control precision can be maintained, but productivity decreases due to repetitive manual work
Solution Approach 1:
The system implements self-service by enabling the AI model to automatically generate and apply security controls without human intervention. The model processes production configuration changes autonomously, queries historical data, and deploys appropriate security controls independently, thereby maintaining precision through learned patterns while significantly improving deployment speed and productivity
3Extent of automation
If historical testing data is used to train AI models for security control generation, then automation capability is improved, but system complexity increases due to data processing and model training requirements
Solution Approach 1:
The system achieves universality by building a single AI model that handles multiple functions: processing historical testing data, mapping configuration parameters to security controls, and deploying controls to production environments. This multi-functional approach consolidates what would otherwise require separate systems for data processing, model training, and control deployment, thereby reducing overall system complexity while maintaining high automation capability
Data Source
AI summary
The present disclosure relates to a system and method of automatically updating the set of security controls in the production environment using AI based on historical data generated in the test management system TMS during the system's testing in the testing environment including information about its elements, their properties, testing environment, its characteristics, and security controls with their settings. Once the AI has sufficient historical data from a testing environment, every time a change is detected to the system in the production environment, its elements, their properties, or at least one characteristic of the production environment, the AI system makes a recommendation to update the set of security controls in the production environment.