Summary
Information Technology (IT) professionals are keenly aware of the security challenges facing applications, but workloads are every bit as important to consider in this domain. Workloads represent computational tasks, which encompass multiple programs or applications performing those tasks by utilizing computing, data, networking, and storage resources. Workloads evolve over their lifecycle through mission development, test, and production scenarios. “A workload is an expression of an ongoing effort of an application AND what is being requested of it … Applications tend to shape the characteristics of the workload itself by how it processes the data, or the software limits inherent to the solution.” [1] Workloads can be comprised of services across multiple clouds, with application programming interfaces (APIs) connecting to third parties and sensitive databases that require different levels of access. To navigate the complexities of managing workloads across computing environments and workflows, organizations are turning to advanced tools such as backend APIs, workload automation software, artificial intelligence (AI) predictive analytics, and cloud management platforms. [2] These tools enable organizations to achieve their mission of interconnectedness, scalability, and usability by interacting with and exchanging data.