Secure AWS Research Data Environment

Research teams need cloud storage that is easy to reach and hard to misuse. For this project, a venture-backed biotechnology company asked Prevvi to design its AWS environment so that internal researchers, external consultants, and high-throughput screening teams could each get exactly the access they needed, while production data stayed protected. The work sits at the intersection of our cloud services and life sciences IT practices.

The challenge

The company’s research increasingly ran through Amazon Web Services: large datasets in S3, compute on EC2, and a growing cast of people who needed access. That cast was the problem. Internal scientists, outside consultants, and an external sequencing vendor all had legitimate reasons to touch some of the data, and no reason to touch all of it.

Without deliberate architecture, cloud environments drift toward everyone having broad access, because broad access is the path of least resistance. The client wanted the opposite: a structure where access boundaries were designed up front and easy to maintain.

What we delivered

Prevvi designed and implemented the AWS access architecture end to end:

• S3 bucket architecture that separates production research data from consultant working areas, so a mistake in one place cannot reach the other
• IAM users, groups, roles, and policies giving each type of user a defined, auditable identity
• Read-only versus full-access separation, so people who only need to consume data physically cannot modify or delete it
• Consultant access controls scoped to specific buckets and prefixes
• EC2 access permissions aligned to the same least-privilege model
• A controlled vendor data exchange: vendor-specific S3 permissions, controlled upload and read paths, credential exchange procedures, and onboarding instructions for an external sequencing partner
• S3 lifecycle policies, including Intelligent Tiering and Deep Archive planning, so storage costs track how data is actually used
• User onboarding and access documentation, so granting the right access is a repeatable procedure instead of a judgment call

How we approached it

Least privilege as the default, not the exception

Every identity in the environment starts from zero and is granted only what its role requires. Scientists got the access their work demands. Consultants got scoped write access to their own working areas and read access only where needed. The sequencing vendor got a narrow, dedicated path for uploads and downloads, fully separated from internal permissions.

Segmentation you can explain in one sentence

Good security architecture is describable. Here, the rule is simple: production research data lives in its own buckets, consultant data lives in others, vendor exchange happens in a third area, and the IAM policies enforce those lines. When access questions come up later, the answer follows from the structure instead of requiring archaeology.

Plan the data lifecycle with the access model

Scientific datasets are large and long-lived, but not all of them are hot. Lifecycle policies move aging data into cheaper storage classes automatically, and archival rules for vendor-exchanged data keep the exchange area from becoming a junk drawer. Security and cost control came from the same design pass.

What is least-privilege access?

Least privilege is the security principle that every user, system, and application should have only the minimum access required to do its job, and nothing more. In practice on AWS, that means:

• Identities are specific. People and systems get their own IAM identities, so activity is attributable.
• Policies grant narrowly. Access is granted to specific buckets, prefixes, and actions, not with account-wide wildcards.
• Read and write are separated. Most data consumers only ever need read access. Granting them write access adds risk with no benefit.
• External parties are contained. Consultants and vendors operate inside dedicated areas that cannot see internal data.

The payoff is that a compromised credential or an honest mistake is limited to a small, known blast radius.

The outcome

Scientists and consultants got the access they needed without waiting on ad-hoc permission decisions, and production research data stayed protected behind least-privilege controls. The external sequencing vendor exchange became a secure, repeatable process rather than a one-off arrangement, and lifecycle policies kept cloud storage costs aligned with how the data is actually used. Because the model is documented, onboarding a new researcher or consultant follows a written procedure.

Key takeaways

• Design cloud access boundaries before the user list grows, not after.
• Make read-only the default grant; full access should be the documented exception.
• Give external vendors their own contained exchange area with their own credentials.
• Pair access architecture with lifecycle policies: the same design pass improves security and cost.