Leveraging EKS and AWS ML Stack for Compliance-Ready AI in Healthcare
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I2P110Keywords:
Healthcare AI, AWS EKS, AWS ML Stack, Compliance, HIPAA, Data Security, Kubernetes, ML Ops, Scalability, Regulatory Standards, GDPR, Cloud-Native Infrastructure, Amazon SageMaker, Data Privacy, Secure Pipelines, Infrastructure as Code, Predictive Analytics, Healthcare Data, AI Deployment, Container Orchestration, Encryption, Audit Trails, Healthcare Compliance, Machine Learning Models, AWS SecurityAbstract
AI has been aiding healthcare in numerous ways, such as improving test accuracy, creating personalized treatments, utilizing predictive analytics, and simplifying processes. This further results in not only better patient outcomes but also in lower costs. On the other hand, the employment of AI in healthcare is not an easy task due to the existence of regulations such as HIPAA, GDPR, and other data protection laws that extend only to particular regions. These provide privacy principles for safe data management issues. An infrastructure informed by modern technology with solid security, flexibility, and automation is needed to guarantee that AI models comply with the regulations throughout the lifecycle. The AWS Machine Learning stack, along with Amazon Elastic Kubernetes Service, are really wonderful solutions for AI workloads in healthcare that require compliance. EKS greatly simplifies the management of containerized applications and therefore, healthcare companies enjoy higher control, availability, and integrated observability of their machine learning pipelines and inference services. It also integrates with AWS Identity and Access Management (IAM), AWS Key Management Service (KMS), and AWS Config in order to meet the HIPAA requirements while maintaining full audit trails and encryption. Amazon SageMaker, AWS Glue, and Amazon Comprehend Medical are some of the AWS ML services that accelerate the process of machine learning model creation, training, and deployment. In addition, they provide features of automated compliance such as encryption at rest, role-based access, and secure data translation. In this article, a technical approach is presented for building scalable AI systems that are compliant with the specified regulations using EKS and the AWS ML stack. It describes the most efficient ways to establish secure data pipelines, utilize AWS Cloud Formation for Infrastructure as Code (IaC) deployment, and guarantee that remote systems follow the rules. It also covers enhanced safety features such as automated vulnerability assessments, virtual private networks (VPNs) for network isolation, and multi-layer encryption for protecting healthcare data storage
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