Automated Security Compliance in Ci/Cd: A Natural Language Processing (NLP) Approach to Detecting Vulnerabilities in Infrastructure-As-Code (Terraform/Bicep)

Abstract

The advent of Infrastructure as Code (IaC) has transformed how organizations manage and provision their cloud resources but has also brought large security issues arising from the messed-up configuration that directly reaches the production systems. Conventional security scanning tools mainly depend on pattern-matching and rule-based mechanism and face problems when dealing with complex security issues and vulnerabilities hidden in IaC templates. In this context, this investigation has described using Natural Language Processing (NLP) approaches to improve automated security compliance assurance in Continuous Integration/Continuous Deployment (CI/CD) pipelines, with a particular emphasis on Terraform and Azure Bicep configurations. This study implemented these NLP models on 850 real-world IaC files from various enterprise projects in order to compare different NLP approaches such as transformer-based models, semantic analysis, and hybrid detection frameworks. This study consisted of inquiry research methods such as both quantitative performance assessment and qualitative evaluations concerning developer experience; involving 62 survey respondents from DevOps practices, the results revealed that security scanning greatly benefitted from the NLP process, yielding 89% precision for vulnerability detection, which is a 34% and 28% improvement over non-NLP methods in terms of precision and recall, respectively. Furthermore, the NLP approach raised the true positive rate by 61% and identified an additional 23% true security issues, excelling in the detection of contextual misconfigurations, if any, that rule-based system-based approaches had missed. Embedded within the implementation difficulties are such challenges as training requirements for model data and the computational overhead of CI/CD pipelines and answering the interpretations required by security teams. Real-world empirical evidence from this research validates the superior efficacy of NLP for IaC security and offers useful guidance to organizations interested in automating their security compliance.