Case study

Secure Analytics Platform for Government Agency

A government department required a scalable, secure AWS environment for data scientists to train and deploy fraud-detection models rapidly.

Industry
Government & Public Sector
Duration
24 months
Secure analytics platform for a government departmentData scientists reach a self-service query layer built on AWS Athena, which sits in its own access VPC. A transit gateway joins that VPC to an analytics VPC running EMR and SageMaker, and to a data VPC holding encrypted S3 storage and Lambda functions. An encryption boundary drawn around the whole cloud estate marks the client-side and server-side encryption applied across every service, and Terraform provisions the VPCs, subnets, gateways and encryption policy beneath it.Encryption boundaryData scientistsAccess VPCAthenaSelf-service queriesTransit GatewayRoutes between VPCsAnalytics VPCEMRSageMakerData VPCS3 (encrypted)LambdaClient-side and server-side encryptionacross every AWS serviceTerraformProvisions the VPCs, subnets, gateways and encryption policy
A simplified view of the platform. The encryption boundary is drawn as one figure because that is how it was specified: comprehensive, not per-service. Illustrative, not as-built documentation.

The challenge

Two requirements pulled against each other. The security and compliance bar was high, as it should be for public sector data at this sensitivity. But a platform so locked down that deploying a model takes weeks does not get used, and the fraud it was meant to detect continues.

The approach

Octasoft led a team of engineers architecting the first version of the platform.

Security was handled structurally, not procedurally. Multi-VPC and subnet configurations with transit gateways meant isolation was a property of the network design, not something depending on correct configuration each time. Encryption was comprehensive, both client-side and server-side across the AWS services in use, so meeting the regulatory requirement did not depend on remembering to enable it.

The self-service layer was built on AWS Athena, letting data scientists work without raising infrastructure tickets, inside boundaries the platform enforced.

The outcome

  • A secure, compliant platform meeting government security standards
  • Data scientists deploying models roughly ten times faster than the previous manual process
  • Reusable components subsequently adopted across several government programmes
  • Full end-to-end encryption satisfying the regulatory requirement

The component reuse across other programmes was the strongest signal. Infrastructure built for one department turned out to be worth adopting elsewhere, which does not happen when a platform is too specific to its first use.

Next step

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