Working model
What belongs in this system
A production cloud boundary is strongest when account structure, identity, data policy, delivery, logging, and recovery reinforce one another. These guides focus on provider-native patterns while keeping the design portable across AWS, Google Cloud, and Azure.
Separate environments structurally
Accounts, projects, or subscriptions give production a clearer blast radius than naming conventions inside one shared boundary.
Promote immutable artifacts
Build once, verify the artifact, and promote it with short-lived roles and explicit production approvals.
Test the operating model
Central logs and backups only become controls when teams can query the evidence, restore systems, and act under realistic conditions.
Recommended path
Start with the boundary, then go deeper
Use the first guide to establish the architecture and vocabulary for the topic.
Move into implementation details for the telemetry, data, cloud, or integration surface you own.
Turn the guidance into reviewable decisions, tests, and operating evidence for your team.
Search intent
Questions this collection answers
- How should development, staging, and production be separated in AWS, GCP, or Azure?
- Which delivery, logging, backup, and recovery controls need independent verification?
- Where should secrets, data, deployment roles, and audit evidence live?
09 field notes
Read the collection
- Responding to Suspected PHI Exposure
A practical engineering playbook for containment, evidence, access revocation, scope assessment, vendor escalation, recovery, and safer follow-up.
- Secrets and Encryption-Key Management for Healthcare Applications
A practical operating model for storing secrets, separating keys, rotating credentials, controlling access, and recovering encrypted healthcare workloads.
- How I Moved Mackex from WordPress to a Fast Next.js Site
A practical case study of rebuilding Mackex Services from a WordPress theme into a fast Next.js App Router site backed by WordPress, WPGraphQL, ISR, Vercel, and SEO cleanup.
- How to Structure AWS, GCP, and Azure for Development, Staging, and Production
A practical guide to separating development, staging, and production across AWS accounts, GCP projects, and Azure subscriptions, including identity, networks, data, delivery, backups, and governance.
- Secure CI/CD for Healthcare Apps Across AWS, GCP, and Azure
A practical healthcare CI/CD model using workload identity, protected releases, verified provenance, isolated runners, safe migrations, and audit evidence.
- Centralized Cloud Audit Logging Across AWS, GCP, and Azure
A practical multi-cloud design for complete audit coverage, protected retention, investigation, alerting, cost control, and verification.
- Backup and Disaster Recovery for HIPAA Workloads
A practical guide to backup and disaster recovery for HIPAA workloads, including recovery objectives, immutable storage, provider controls, BAAs, PHI, and restoration testing.
- Using Healthcare Production Data in Development and Staging
A practical workflow for healthcare test data covering synthetic fixtures, de-identification limits, governed subsets, controlled debugging, access expiry, refreshes, backups, and verified deletion.
- Using Amazon Macie for HIPAA Workloads: Finding PHI in Amazon S3
A practical architecture guide to using Amazon Macie to discover possible PHI in Amazon S3 while accounting for AWS BAA requirements, findings, remediation, and coverage limits.
How I can help
Turn the guidance into a production plan
Architecture review
Map the system boundary, data flow, cloud services, deployment path, and operational risk.
Vendor and BAA stack review
Separate contract coverage from product configuration, enabled features, retention, and subprocessors.
PHI data-flow review
Identify where sensitive data can appear in storage, logs, analytics, AI tools, email, support, and exports.
Production readiness
Turn decisions into controls, tests, runbooks, monitoring, access reviews, and release evidence.
Healthcare cloud migration · Cloud architecture services
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