DevSecOps Accelerator (Regulated Software)
MedAI_Flow_DevSecOps is a reference implementation / demonstrator that captures compliance-aligned pipeline thinking: quality gates, evidence automation, and release artefact discipline designed for regulated scrutiny.
What it is
A practical set of patterns for designing CI/CD in regulated environments: how to build, test, and release in a way that consistently generates reviewable evidence and maintains traceability.
Focus: repeatability, governance, artefact discipline, and risk-informed quality gates.
What it is not
Not a plug-and-play product. Not a generic CI/CD tool. Not a promise that you can “buy compliance”.
It supports consulting engagements by accelerating design decisions and providing a coherent starting reference for evidence-oriented delivery.
Typical adoption path
1) Assessment
Understand your QMS expectations, release risk profile, and current pipeline gaps.
2) Pipeline design
Define quality gates, evidence artefacts, and traceability integration points.
3) Evidence automation
Implement test evidence capture and release packaging patterns appropriate to your context.
4) Team enablement
Ensure teams can operate the model sustainably with lightweight governance.
5) Continuous improvement
Refine based on audits, delivery learning, and evolving risk controls.
Reference Architecture: MedAI_Flow_DevSecOps
Production-Grade Medical AI Blueprint
This GitHub repository showcases a sophisticated DevSecOps pipeline specifically designed for medical imaging AI software. Created by Transvolve, the project utilizes a cloud-native architecture featuring FastAPI, Azure Kubernetes Service, and Terraform to ensure scalable and secure deployments.
Technical Core
- 🔹 Infrastructure: Terraform & Azure AKS
- 🔹 Backend: FastAPI (Python)
- 🔹 CI/CD: GitHub Actions
- 🔹 Quality: Static Analysis & Security Scans
Regulatory Compliance
- ✅ HIPAA Protected Health Information
- ✅ ISO 13485 QMS Alignment
- ✅ FDA 21 CFR 11 Electronic Records
- ✅ Audit-Ready Traceability
A primary focus is maintaining rigorous regulatory compliance. The system performs continuous testing to validate clinical software, with recent updates introducing advanced database integration to enhance audit-readiness. Ultimately, it serves as a comprehensive blueprint for developing production-grade medical AI within a highly regulated environment.
How it reduces audit and delivery risk
Audit risk
Evidence is generated as part of delivery: controlled builds, test outputs, review discipline, and release readiness signals.
Delivery risk
Clear quality gates reduce late-stage surprises and make “release-ready” a transparent, repeatable state.