Health Insurer Runs Secure Cloud ML on Anonymized Data; 2-Day Deployments

Context

A large health insurer and healthcare provider sought to establish a strategic cloud platform to host existing and new workloads while enabling rapid delivery of cloud-native AI and machine learning capabilities. The objective was to provide a consistent, repeatable, and secure path for product teams to deploy services into sandbox, development, UAT and production environments. Requirements included infrastructure as code (IaC), automated CI/CD pipelines, compliance as code and an inherently secure architecture that supported experimentation with anonymised healthcare data without exposing sensitive information.

Challenges

Previous cloud initiatives relied on manual deployment processes that produced inconsistent architectures, configuration drift and slow, error-prone environment builds. This inconsistency inhibited governance, slowed time to insight and increased operational risk. Compounding the problem, the engineering organisation lacked experience in modern IaC practices, DevSecOps toolchains and the specific regulatory and data-handling constraints of healthcare and insurance domains. Product teams needed a platform that reduced cognitive load, enforced policy automatically and enabled safe, repeatable deployments for AI and ML workloads.

Implementation

The solution was a strategic Azure cloud platform designed against the Microsoft Well-Architected Framework. Core design decisions focused on security, operational excellence, reliability and cost optimization while providing accelerators — opinionated, reusable wrappers around native Azure services including AI and Machine Learning components. Terraform was used to implement modular IaC that product teams could consume to provision resources into secure spokes, ensuring network segmentation, role-based access and separation of duties.

Automated CI/CD pipelines were built end-to-end to support sandbox through production promotion paths. Compliance-as-code was integrated into pipeline gates so policy, security controls and automated checks ran before any deployment progressed. The platform architecture included anonymisation patterns and data handling controls that allowed machine learning algorithms to run against de-identified datasets while preserving auditability and traceability for regulatory compliance.

To address the skills gap, the platform operating model included a customer success function that delivered a white-glove service to product teams. This function performed hands-on onboarding, created reference templates, ran workshops and provided bespoke coaching so teams could adopt Terraform modules, pipeline patterns and secure deployment practices. Our Fractional Head of AI led the technical alignment between platform accelerators and data science workflows, ensuring ML tooling was consumable and that repeatable patterns existed for model training, validation and deployment.

The implementation emphasized reusability and governance: accelerator modules encoded baseline security settings, monitoring and logging, and linked into centralized identity and secrets management. Teams could instantiate a compliant environment via Terraform and trigger fully automated pipelines that applied compliance checks, deployed platform services and registered observability hooks without manual configuration.

Results

The platform enabled the organisation to run cloud-native machine learning algorithms against anonymised healthcare and insurance data in a secure, scalable environment. Automated compliance checks and policy gates reduced human error and improved audit readiness. Product teams reported faster iterations and clearer guardrails for experimentation.

A direct operational gain was the reduction in new environment build time to two days through fully automated pipelines and reusable Terraform accelerators — a dramatic improvement over prior manual timelines. Standardised architectures and the accelerator catalogue produced consistent, secure deployments across spokes, improving reliability and simplifying support. The customer success-led upskilling closed critical skills gaps in IaC and DevSecOps, increasing internal capability to manage and extend the platform.

Overall, the strategic Azure platform delivered repeatable, secure pathways for AI and ML innovation in a highly regulated sector, accelerating time to value while embedding governance and best practices into everyday deployment workflows.