Modern Automation Strategies for Biomedical Research Infrastructures: A Technical Framework

Abstract

The need for biomedical research institutions is to have digital infrastructures that can ensure security, reproducibility, and sustained regulatory compliance, and at the same time facilitate rapid scientific innovative processes. To deal with these problems in a setting where sensitive health information is processed, the National Institutes of Health and National Library of Medicine have established extensive automation models that are specifically targeted to combat these issues. The architecture combines Infrastructure-as-Code, container orchestration, and compliance-based workflow in accordance with the Health Insurance Portability and Accountability Act and Federal Information Security Management Act requirements. The frameworks of Machine Learning Operations have proven that it is possible to achieve a high degree of automation of infrastructure in a systematic way that leads to a considerable decrease in the cost of operations and, at the same time, ensures high-quality standards. Using continuous validation pipelines, system configuration drift was reduced substantially, and release frequency increased multiplefold without audit deviation. The deployment unified biomedical workflows across multiple institutes while ensuring data integrity through automated encryption rotation. Organizations implementing MLOps practices report substantial improvements in deployment reliability and model governance across diverse computing environments. This model demonstrates that automation, when embedded with regulatory intelligence, can enable scalability and security simultaneously. This framework introduces a public-trustfocused automation model that demonstrates automation, when embedded with regulatory intelligence, can enable scalability and security simultaneously. Scholarly significance arises from bridging automation theory and biomedical informatics practice, establishing a replicable framework for modernization in lifescience computing environments. The principles of design of the framework, such as infrastructure-ascode, ongoing compliance validation, and intrinsic regulatory intelligence, give a blueprint that can be adopted in various institutional settings and regulatory frameworks