Arocom IT Solutions Pvt. Ltd.

Healthcare AI Platform Migration to AWS Cloud

Client

A specialty risk transfer and care coordination company serving insurers, government entities, self-insured plan sponsors, and other managed care organizations in the healthcare industry.

Challenge

The client operated their P2R healthcare platform entirely in a private cloud environment with strict security requirements including encrypted data storage, no direct internet access, and HIPAA and SOC compliance controls. They needed to assess and analyze the feasibility of integrating generative AI capabilities while maintaining healthcare compliance standards. The challenge involved understanding current data storage and processing architectures, analyzing requirements for securely transferring huge gigabytes of protected health information (PHI) from private cloud to AWS for training a 70B parameter Large Language Model (LLM), and developing comprehensive recommendations for both temporary training infrastructure and permanent inference capabilities while ensuring continuous HIPAA and SOC compliance.

Key Results

  • Delivered comprehensive migration assessment with detailed recommendations, reducing project risk through thorough analysis of security and compliance requirements
  • Provided cost-optimized infrastructure recommendations with 3 GPU instance options, enabling informed decision-making for cost savings on AI training workloads
  • Developed complete architectural blueprint with HIPAA-compliant data transfer mechanisms, establishing clear implementation roadmap for secure AI integration

Solution

  • Discovery and Requirements Analysis: Conducted comprehensive stakeholder interviews and technical assessments to understand current data storage mechanisms, AI workload requirements, and security constraints. Analyzed existing private cloud architecture including SQL Server 2017 databases, encrypted file storage, and DMZ configurations.
  • Architecture Design and Recommendations: Developed detailed AWS multi-account strategy recommendations with Control Tower implementation, organizational units for security and healthcare workloads, and Service Control Policies for HIPAA compliance. Designed secure connectivity architecture using Site-to-Site VPN and Transit Gateway for protected data transfer.
  • Data Migration Strategy Development: Created comprehensive recommendations for secure data transfer using AWS Database Migration Service (DMS) for SQL Server replication and AWS DataSync for file migration. Documented encryption requirements, VPC endpoint configurations, and compliance controls for PHI protection.
  • AI Infrastructure Planning: Analyzed and recommended optimal GPU instance configurations (p4d.24xlarge, p4de.24xlarge, p5.48xlarge) for temporary 3-month LLM training with detailed cost comparisons. Designed permanent inference environment options (inf2.48xlarge, g5.48xlarge, g5.24xlarge) to support cost optimization.
  • Security and Compliance Framework: Documented comprehensive security recommendations including AWS Business Associate Agreement (BAA) execution, AWS Config conformance packs for HIPAA controls, and CloudWatch monitoring strategies. Provided detailed compliance validation procedures and audit logging requirements.
  • Implementation Roadmap: Created 7-week implementation timeline with clear milestones, team responsibilities, and delivery phases. Documented operational procedures, backup strategies, and recovery mechanisms for ongoing management.

Technologies Used

  • AWS Control Tower & Organizations
  • AWS Site-to-Site VPN & Transit Gateway
  • Amazon EC2 (GPU instances: p4d, p4de, p5 series)
  • Amazon RDS SQL Server
  • Amazon S3 with encryption
  • AWS Database Migration Service (DMS)
  • AWS DataSync & Storage Gateway
  • Amazon API Gateway
  • AWS CloudWatch & CloudTrail
  • AWS Config & AWS Backup
  • AWS Key Management Service (KMS)
  • Large Language Models (70B parameter)
  • QLoRA fine-tuning
  • Microsoft IIS & SQL Server 2017

Summary
We conducted a comprehensive assessment for a healthcare risk management company, analyzing current private cloud architecture and developing detailed recommendations for securely migrating huge gigabytes of protected health information to AWS for Large Language Model training and inference. The assessment delivered a complete architectural blueprint with HIPAA-compliant data transfer strategies, cost-optimized GPU infrastructure recommendations, and a 7-week implementation roadmap that established clear pathways for AI integration while maintaining strict healthcare compliance standards.