Securing Serverless Architectures: A Comprehensive Guide to Protecting Modern Cloud Applications

Authors

  • Austin Roger Department of Computer Science, University of Harvard Author

Abstract

Serverless computing has revolutionized cloud application development by abstracting away infrastructure management, enabling developers to focus on code and functionality. However, this paradigm shift introduces unique security challenges that require careful consideration and implementation of robust security measures. This article provides a comprehensive guide to securing serverless applications, exploring the security implications of this architecture and offering best practices for mitigating risks in modern cloud environments. We delve into the key security risks associated with serverless computing, including insecure function configurations, vulnerable dependencies, excessive permissions, and inadequate logging and monitoring. The article examines the shared responsibility model in serverless environments, highlighting the roles and responsibilities of both cloud providers and developers in ensuring application security. We present best practices for securing serverless applications, such as implementing least privilege access controls, using strong authentication and authorization mechanisms, validating input data, protecting secrets and API keys, and employing runtime security monitoring. Furthermore, we discuss the importance of integrating security into the development lifecycle, emphasizing the need for automated security testing, vulnerability scanning, and incident response planning. By understanding the specific security challenges of serverless computing and adopting these best practices, organizations can effectively protect their serverless applications and maintain a secure cloud environment.

Downloads

Download data is not yet available.

Downloads

Published

2024-05-30

How to Cite

Securing Serverless Architectures: A Comprehensive Guide to Protecting Modern Cloud Applications. (2024). International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 15(1), 753-762. http://ijmlrcai.com/index.php/Journal/article/view/258