Sequential Cipher Encryption for Secure Performance Test Data Removal
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Solution Overview
Problem
Existing systems face challenges in protecting and removing performance testing data from production environments, leading to security and functionality concerns, and existing solutions are inefficient and resource-intensive.
Innovation Solution
A system utilizing contextual machine learning to generate and encrypt testing data, forming a sequential cypher encryption chain to ensure secure and efficient removal of testing data from production environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If testing data is introduced to assess the application, then performance testing capability is improved, but security risk increases due to potential exposure of testing data
Solution Approach 1:
The system performs preliminary encryption of testing data before it is introduced into the production environment. The performance testing engine encrypts testing data using encryption keys generated by the machine learning model, ensuring that data is protected from the moment it enters the system, thus preventing security risks while maintaining testing capability
Solution Approach 2:
The patent introduces an intermediary encryption layer between the testing data and the production environment. The machine learning model generates encryption keys that act as intermediaries, transforming plain testing data into encrypted form that cannot be accessed or misused, thereby eliminating security risks while preserving the ability to conduct performance testing
2Reliability
If testing data is protected and removed from the application, then security is improved, but operational complexity increases
Solution Approach 1:
The system implements self-service through automated key management and data deletion. The machine learning model automatically generates encryption keys, and the performance testing engine automatically deletes testing data after use. This automation eliminates manual intervention, reducing operational complexity while maintaining high security standards
Solution Approach 2:
The patent implements a systematic process where testing data is encrypted, used for testing, and then automatically deleted. The encryption keys are generated, used for encryption, and subsequently discarded after the testing data is deleted. This structured approach to discarding and recovering resources simplifies operations while ensuring security
3Reliability
If sequential cypher encryption is implemented, then data security is improved, but processing time increases
Solution Approach 1:
The machine learning model generates encryption keys in advance before the actual encryption process. This preliminary key generation optimizes the subsequent encryption operations by having ready-to-use keys, reducing the overall processing time while maintaining the security benefits of sequential cypher encryption
Solution Approach 2:
The system dynamically adjusts encryption parameters and key lengths based on the specific testing requirements and data sensitivity. By optimizing encryption parameters rather than using fixed maximum-security settings, the system achieves adequate security with reduced processing overhead, balancing security and processing time
4Strength
If resource-intensive encryption processes are used, then encryption strength is improved, but resource consumption increases
Solution Approach 1:
The machine learning model optimizes encryption parameters such as key length and algorithm complexity based on the specific requirements of each testing scenario. This dynamic parameter adjustment ensures adequate encryption strength without consistently using maximum-resource-intensive settings, thereby reducing overall resource consumption while maintaining necessary security levels
Solution Approach 2:
The system applies encryption strength proportional to the actual security needs of the testing data rather than uniformly applying maximum encryption to all data. By assessing and applying only the necessary level of encryption for each dataset, the system avoids excessive resource consumption while maintaining adequate security
Data Source
AI summary
Systems, computer program products, and methods are described herein for sequential cypher encryption based on contextual machine learning powered performance testing engine for production environment. The present disclosure is configured to create a first set of testing data via a machine learning model (MLM) within a performance testing engine; encrypt the first set of testing data with a first encryption key; insert the encrypted first set into a lower environment within an application; test the lower environment with the encrypted first set; create a second set of testing data via the MLM; transfer the first encryption key to the second set of testing data; delete the first set of testing data within the lower environment using the first encryption key; encrypt the second set of testing data; and insert the encrypted second set of testing data into the lower environment.