Automation Workflow Validation Using Encrypted Job Screening
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Solution Overview
Problem
Current automation workflow techniques result in insecure and difficult-to-maintain scripts that consume significant computing resources, require extensive privileges across multiple tools, and are prone to security breaches, making them inefficient and insecure.
Innovation Solution
A workflow system that securely designs and executes automation workflows by validating encryption keys and workflow portions using machine learning models, allowing only valid jobs to execute and processing denied jobs to predict a final result, thereby preventing security breaches and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional automation workflow scripts are executed without validation, then automation productivity is improved, but security reliability deteriorates due to prone security breaches
Solution Approach 1:
The system performs preliminary validation of encryption keys and workflow portions before executing automation jobs. The validation service checks cryptographic signatures and workflow definitions in advance, allowing only validated jobs to execute. This preliminary security check prevents security breaches while maintaining automation productivity, as invalid jobs are rejected before consuming execution resources.
2Adaptability or versatility
If all automation jobs are executed without validation, then automation versatility is improved, but computing resource consumption increases due to executing invalid jobs
Solution Approach 1:
The system extracts and validates only the necessary components (encryption keys and workflow portions) from each automation job before execution. The validation service separates the validation function from the execution function, checking cryptographic signatures and workflow definitions independently. This allows the system to reject invalid jobs without executing them, conserving computing resources while maintaining versatility in handling diverse automation requests.
3Reliability
If encryption key validation is performed for all jobs, then security reliability is improved, but device complexity increases due to validation infrastructure
Solution Approach 1:
The system introduces a validation service as an intermediary component between job submission and job execution. This mediator validates encryption keys and workflow portions by checking cryptographic signatures against stored public keys and verifying workflow definitions against schema definitions. The validation service simplifies the overall system architecture by centralizing security checks, making the validation infrastructure manageable while maintaining high security reliability.
4Manufacturing precision
If workflow portions are validated against schema definitions, then manufacturing precision is improved, but measurement precision requirements increase for validating encryption keys
Solution Approach 1:
The system performs preliminary validation of encryption keys by checking cryptographic signatures against stored public keys before validating workflow portions. The validation service first verifies the authenticity of the encryption key using cryptographic validation, then uses the validated key to validate the workflow definition against schema definitions. This sequential validation approach ensures both manufacturing precision of workflow definitions and measurement precision of encryption key validation without requiring simultaneous complex validation.
Data Source
AI summary
A device may receive workflow data identifying an automation request, and may request jobs for the workflow data. The device may receive encrypted jobs based on the request for the jobs, and may determine whether encryption keys for the encrypted jobs are valid. The device may determine whether workflow portions for the encrypted jobs are valid, and may determine whether to allow or deny each of the encrypted jobs based on whether the encryption keys and the workflow portions are valid. The device may execute the encrypted jobs determined to be allowed, to generate execution results, and may forgo execution of the encrypted jobs determined to be denied. The device may process the execution results and the encrypted jobs determined to be denied, with a machine learning model, to predict a final result for the automation request, and may perform actions based on the final result.