Every cloud vendor will tell you their platform is HIPAA-eligible, GxP-compatible, and FDA-ready. What they will not tell you is that "eligible" and "compliant" are very different things, and the compliance obligation sits with you — not with AWS, Azure, or GCP.
Here's what a cloud migration actually looks like in a regulated life sciences environment, and the decisions that matter most.
Vendor compliance ≠ your compliance
AWS has a HIPAA Business Associate Agreement. Azure has GxP guidelines. Google Cloud has a compliance documentation library. None of this means your use of their infrastructure is automatically compliant.
The cloud vendor is responsible for the security of the cloud (their data centers, hardware, hypervisors). You are responsible for security in the cloud — your data, your access controls, your configurations, your validation, your audit trails.
This is called the Shared Responsibility Model, and understanding where the vendor's obligation ends and yours begins is the most important thing to establish before any regulated workload goes to the cloud.
What needs to be validated in a cloud environment
The same systems that need validation on-premises need validation in the cloud. Moving your ELN from a local server to a hosted SaaS instance doesn't eliminate your validation obligation — it changes it. You now need to validate:
- The configuration of the cloud environment hosting the system
- The data migration from the old environment to the new one
- The performance of the system in its new environment
- Your backup, recovery, and business continuity procedures for the new environment
Data residency and sovereignty
For life sciences companies working with clinical trial data or patient-adjacent data, where your data physically resides matters. US clinical trial data subject to FDA regulations generally needs to stay in US data centers. EU clinical data may be subject to GDPR data residency requirements.
All three major cloud providers offer region-specific deployments. The key is configuring them explicitly — by default, many cloud services replicate data across regions for redundancy, which may violate your data residency requirements if not properly constrained.
The hybrid reality
Most life sciences companies end up with hybrid environments: some systems in the cloud, some on-premises. Common reasons include:
- Instruments that generate data locally and require local storage before transfer
- Legacy validated systems that can't be moved without a full revalidation effort
- Latency-sensitive applications that can't tolerate network round-trips to the cloud
Hybrid isn't inherently a problem, but it requires careful attention to data flow documentation — how data moves from on-premises systems to cloud systems, who has access at each point, and how audit trails are maintained across the boundary.
Azure vs. AWS vs. GCP for life sciences
The honest answer is that all three can work. The practical answer depends on what you're already using:
- Microsoft 365 shop? Azure integrates cleanly with M365 identity, Intune device management, and Teams — reducing the number of identity systems you have to manage and validate.
- Running workloads on specialized hardware or needing specific ML infrastructure? AWS has the widest selection of instance types and the most mature marketplace of life sciences-specific software.
- Research-heavy with significant genomics or bioinformatics compute? GCP has strong credentials in genomics (Terra, BigQuery for genomics) and tends to be cost-competitive for burst compute workloads.
Platform choice matters less than how you configure, document, and govern whatever you choose.
The migration timeline nobody shows you
Cloud vendor sales decks show a four-week migration. The realistic timeline for a life sciences company migrating regulated workloads looks more like this:
- Weeks 1-4: Environment assessment, architecture design, vendor agreement review (including BAA/DPA negotiation)
- Weeks 5-8: Validation planning, IQ/OQ documentation preparation, staging environment setup
- Weeks 9-14: Phased migration starting with non-regulated workloads, OQ testing, user training
- Weeks 15-20: Regulated system migration, PQ testing, sign-off from QA
- Ongoing: Periodic review of cloud configuration against validation documentation
Companies that skip steps in this process typically don't discover the problem until an audit — at which point they face a choice between a remediation project that retroactively documents what was done and a full revalidation. Neither is cheap.
Cloud is the right infrastructure strategy for most life sciences companies. The companies that do it well are the ones who treat it as a compliance initiative, not just an IT project.
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