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Despite being beneficial in automated provisioning of computing infrastructure at scale, infrastructure as code (IaC) scripts are susceptible to containing secrets, such as hard-coded passwords. A derivation of practices related to secret management for IaC can help practitioners to secure their secrets, potentially aiding them to securely develop IaC scripts. The goal of the paper is to help practitioners in secure development of infrastructure as code (IaC) scripts by identifying practices for secret management in IaC. We conduct a grey literature review with 38 Internet artifacts to identify 12 practices. We identify practices that are applicable for all IaC languages, e.g., prioritized encryption, as well as language-specific practices, such as state separation for Terraform. Our findings can be beneficial for (i) practitioners who can apply the identified practices to secure secrets in IaC development, and (ii) researchers who can investigate how the secret management process can be improved to facilitate secure development of IaC scripts. 

With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholder’s, particularly, end user’s security, protecting the data from fraudulent efforts is one of the most pressing concerns. A set of malicious programming code, scripts, active content, or intrusive software that is designed to destroy intended computer systems and programs or mobile and web applications is referred to as malware. According to a study, naive users are unable to distinguish between malicious and benign applications. Thus, computer systems and mobile applications should be designed to detect malicious activities towards protecting the stakeholders. A number of algorithms are available to detect malware activities by utilizing novel concepts including Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we emphasize Artificial Intelligence (AI) based techniques for detecting and preventing malware activity. We present a detailed review of current malware detection technologies, their shortcomings, and ways to improve efficiency. Our study shows that adopting futuristic approaches for the development of malware detection applications shall provide significant advantages. The comprehension of this synthesis shall help researchers for further research on malware detection and prevention using AI.