AI-Powered Infrastructure as Code (IaC) Security using Graph Neural Networks

This paper looks into the innovative ways that Graph Neural Networks (GNNs) are impacting security in Infrastructure as Code (IaC), especially regarding finding faults, automating security within the cloud and ensuring privacy. With a detailed look at Design, TAC-San and IPAG-HAGNN, these GNNs reach higher accuracy than most deep learning models. They make use of code semantics and graph concepts to search for advanced security loopholes in many types of code such as those written in smart contract designs or multiple languages. The paper also look at how GNNs are applied in dynamic cloud situations, proving that they provide reliable results in managing multiple cloud compliance, interacting through microservices and chaining services safely with success rates above 90%. Although GNNs are advantageous, the study finds that their deployment comes with important privacy and security problems such as injection of nodes and inferences on how a graph is embedded which should be addressed by future development of new defenses. The authors underline the efforts to make models more understandable and improve their features which helps IaC pipelines use them in real time. Based on the research, GNNs can greatly transform IaC security, so its urge people to adopt balanced strategies that prevent privacy issues and make the automation process explainable.