Intelligent DevSecOps Pipeline for Adaptive Software Deployment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional automation methods, such as preprogrammed logic or decision trees, are inadequate for managing the complexity of modern software deployments and development pipelines, which require consideration of numerous factors and permutations to ensure secure and high-availability software deployments.
Innovation Solution
An intelligent DevSecOps pipeline with built-in intelligence tools that integrate artificial intelligence and machine learning to evaluate risk profiles and automatically determine appropriate actions, enabling continuous feedback, continuous learning, and continuous compliance, and incorporating a risk-based adaptive pipeline tool and Scan Confidence Tool (SCT) for enhanced security and quality enforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional automation methods with preprogrammed logic or decision trees are used, then the system is simple to implement, but it is inadequate for managing the complexity of modern software deployments and development pipelines
Solution Approach 1:
The patent implements continuous feedback loops where monitoring tools collect data about software deployment status, security conditions, and pipeline health. This feedback is processed by the automation system to dynamically adjust decisions and actions, enabling the system to adapt to complex scenarios without requiring manual reprogramming of logic trees.
Solution Approach 2:
The automation system performs self-service through autonomous decision-making capabilities. Instead of relying on preprogrammed logic, the system independently evaluates multiple factors, determines appropriate actions, and executes them without human intervention, thereby managing deployment complexity automatically.
2Reliability
If comprehensive monitoring of numerous data factors and permutations is implemented, then security and robustness are improved, but reaction time increases due to the complexity of analysis
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and analyzing data factors before critical issues arise. Security checks, vulnerability scans, and compliance validations are executed in advance during the development and deployment pipeline, allowing potential problems to be identified and resolved before they impact production systems.
Solution Approach 2:
The patent replaces manual or mechanical analysis processes with automated computational systems. Machine learning models and algorithms automatically process numerous data factors and permutations, substituting human analysis time with rapid computational evaluation, thereby maintaining comprehensive monitoring while reducing reaction time.
3Productivity
If manual management of software deployments is used, then flexibility in decision-making is maintained, but productivity and consistency decrease in large-scale scenarios
Solution Approach 1:
The automation system implements dynamic decision-making capabilities that can adapt to different scenarios. Rather than rigid automated rules, the system dynamically evaluates current conditions, adjusts its behavior accordingly, and makes context-appropriate decisions, thereby maintaining flexibility while achieving high productivity in large-scale deployments.
Solution Approach 2:
The patent creates a universal automation platform that handles multiple functions across the entire software development lifecycle. This multi-functional system manages security checks, compliance validations, deployment orchestration, and incident response across numerous applications and pipelines simultaneously, achieving consistent results at scale that would be impossible through manual management.
Data Source
AI summary
Disclosed herein are system, computer-implemented method, and computer program product (computer-readable storage medium) embodiments for implementing an intelligent DevSecOps workflow. An embodiment includes receiving, by at least one processor, a risk profile associated with a software deployment, and an update related to the software deployment; and evaluating, by the at least one processor, at least one parameter associated with the update, to produce an evaluation result. Additionally, the at least one processor may determine a set of actions in response to the update, based at least in part on the evaluation result, an application dataset corresponding to the software deployment, and a group of specified criteria on which the risk profile is based; or perform at least one action of the set of actions in response to the update, according to some example use cases.