Secure and Automated AWS Infrastructure for Forensic AI
Industry: Healthcare, Government, Research, Artificial Intelligence
Platform: Amazon Web Services (AWS)
Client: National Center for Forensic Medicine / AI4VI
Services: Cloud Infrastructure Automation, Infrastructure as Code, Security, Cloud Operations, AI Infrastructure, Governance
About the Customer
The National Center for Forensic Medicine (NCFM), operating under the Israeli Ministry of Health, serves as Israel’s central authority for forensic identification and related medical examinations.
Following the events of October 7, 2023, the center faced unprecedented challenges in identifying victims through forensic methods, including comparisons of ante-mortem and postmortem medical imaging.
To advance forensic identification capabilities, Prof. Ilan Tsarfaty of Tel Aviv University initiated AI4VI (Artificial Intelligence for Victim Identification), an innovative research initiative aimed at accelerating forensic identification through artificial intelligence.
The project requires highly secure and scalable technology infrastructure to support AI research and the processing of sensitive forensic and medical imaging data.
Customer Challenge
The AI4VI project required a secure, scalable, and standardized AWS environment to support research involving sensitive medical imaging and forensic information.
Initially, the AWS infrastructure had been configured manually, creating operational challenges around configuration consistency, infrastructure deployment, and ongoing environment management.
As research activities evolved, manually provisioning and modifying AWS resources became increasingly inefficient and increased the possibility of configuration inconsistencies between environments.
The sensitive nature of forensic and medical imaging data also required strict security controls, including appropriate access permissions, encryption, data segregation, and adherence to AWS Israel Region data residency requirements.
The project needed an infrastructure operating model capable of supporting changing research requirements while maintaining consistent configurations and minimizing manual administrative tasks.
Managing such an environment required specialized AWS infrastructure, security, and automation expertise, allowing researchers to focus on developing AI identification capabilities rather than repeatedly configuring cloud resources.
The AI4VI team therefore worked with Peax Cloud to standardize and automate the AWS environment, simplify its operation, and establish a secure foundation for continued AI research.
Partner Solution
Peax Cloud Cloud partnered with the National Center for Forensic Medicine and Prof. Tsarfaty’s research team to implement a standardized, Terraform-based AWS infrastructure automation framework.
The solution includes:
- Infrastructure as Code: Peax Cloud transformed the manually configured AWS environment into a Terraform-managed infrastructure, providing repeatable resource provisioning and consistent configurations.
- Standardized Infrastructure Management: Reusable infrastructure configurations were established to simplify the deployment and modification of AWS resources as research requirements evolve.
- Security and Access Control: IAM roles, encryption, and data segregation policies were implemented to help protect sensitive forensic and medical imaging information.
- Data Residency: The infrastructure was designed to maintain forensic data within AWS Israel Region boundaries.
- Scalable AI Infrastructure: Secure compute and storage resources were provisioned to support AI model development, medical imaging analysis, and future research workloads.
- Operational Automation: Terraform automation reduces manual provisioning effort and provides a structured approach to managing cloud infrastructure changes.
Peax Cloud provides AWS infrastructure and automation expertise to support the management and evolution of the environment.
The Terraform-based approach enables infrastructure changes to be implemented consistently, reducing manual configuration activities and simplifying the deployment of resources for new research requirements.
Standardized infrastructure definitions also support consistent security configurations and make the AWS environment easier to maintain as the project expands.
This operating approach helps reduce the infrastructure management burden on research teams while providing a secure, scalable foundation for advanced forensic AI development.
Results and Benefits
Reduced Operational Complexity: Terraform automation replaced manually configured infrastructure with a standardized approach, reducing the effort required to provision and modify AWS resources.
Improved Infrastructure Consistency: Infrastructure as Code enables repeatable deployments, reducing configuration differences and the potential for manual errors.
Enhanced Security and Governance: IAM controls, encryption, and data segregation provide a stronger foundation for protecting sensitive forensic information.
Faster Research Environment Provisioning: Automated infrastructure deployment allows researchers to establish or modify AWS environments more efficiently, supporting faster experimentation and development cycles.
Improved Operational Manageability: Standardized infrastructure configurations simplify ongoing infrastructure changes and provide greater consistency as research requirements evolve.
Support for AI Innovation: The AWS architecture provides scalable infrastructure for developing advanced forensic identification capabilities, including future integration of additional medical imaging and forensic analysis methods.
Greater Focus on Research: By reducing infrastructure configuration and management complexity, the project allows research teams to devote more effort to AI model development and forensic identification innovation.
The solution provides AI4VI with a secure, reproducible, and efficiently manageable AWS foundation capable of supporting its continued research and future expansion.