However, the misconfiguration in the IaC template is now regarded as one of the critical factors responsible for cloud security breaches. Services impacted by these include storage, networking, identity management, and databases. This paper discusses TerraSecure – an advanced intelligent multilayer framework that is capable of identifying such misconfigurations. TerraSecure applies a hybrid approach which includes rule-based detection, machine learning, and AI-powered contextual analysis. This framework employs more than 50 security patterns extracted from actual breaches as well as best practices in cloud computing. A pre-trained XGBoost model, considering 50 security patterns, predicts the risk score with accuracy of 92.45% while ensuring the minimal false-positive ratio of 10.71%. As a result, vulnerable configurations, such as public storage access, overly broad permission scopes, unencrypted data, and unsafe network settings, can be identified. Moreover, an AI analysis component adds to the interpretability of this framework by delivering information about potential business impact, attack scenario (based on the real incident), and remediation steps. In addition, TerraSecure supports several output formats, among which there is SARIF to facilitate the integration with CI/CD pipelines and other tools (e.g., GitHub). The conducted experiments confirm the scalability, efficiency, and reliability of this framework in terms of security.
K et al. (Thu,) studied this question.