AI-Based Container Security in DevSecOps: Integrating Predictive Analytics for Vulnerability Mitigation in CI/CD Pipelines
Keywords:
container security, DevSecOps, predictive analytics, AI-based solutionsAbstract
The rapid adoption of containerization technologies in modern software development has brought significant advantages in scalability, portability, and efficiency. However, these technologies also introduce new security challenges, particularly in continuous integration/continuous deployment (CI/CD) pipelines, where vulnerabilities can be quickly propagated if not effectively managed. This paper explores the role of artificial intelligence (AI)-based solutions in securing containers within DevSecOps frameworks, with a focus on integrating predictive analytics for proactive vulnerability mitigation. AI-driven models can analyze vast amounts of data from CI/CD pipelines, predict potential security risks, and automate vulnerability assessments to enhance container security. By examining the integration of predictive analytics into security practices, this research identifies key AI techniques, including machine learning (ML) and anomaly detection, and discusses their application in early vulnerability detection, risk management, and overall container security. The challenges of implementing AI-based solutions, such as the need for accurate data and computational resources, are also addressed, alongside best practices for their effective deployment in CI/CD pipelines. This research provides insights into the evolving role of AI in DevSecOps, offering a pathway to more secure and resilient containerized applications in the face of emerging threats.
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