Compliance & Assurance
CSV for GDP: a pragmatic path to validated systems
A practical approach to CSV and GDP validation that keeps evidence audit-ready without slowing delivery.
TL;DR
CSV and GDP validation can be practical when it focuses on intended use, traceability, and data integrity. Treat validation evidence as a living asset that supports operations, not just audits.
When you need this
- New or changed systems that handle regulated data.
- Audit readiness efforts that rely on manual evidence gathering.
- Teams unsure how to align CSV activities with GDP expectations.
Key concepts
Intended use: the documented purpose of the system and its quality impact.
Traceability: the link between requirements, risks, tests, and evidence.
Data integrity: confidence that data is complete, accurate, and protected through its lifecycle.
Common mistakes
- Documenting everything instead of what is critical to intended use.
- Separating validation evidence from operational controls and reviews.
- Allowing traceability gaps to appear between requirements and tests.
Practical checklist
- Confirm intended use and validation boundaries.
- Maintain a risk assessment tied to data integrity.
- Create a traceability matrix that stays current.
- Capture test evidence with clear acceptance criteria.
- Plan periodic review so evidence remains audit-ready.
Related services
Ready to validate without friction?
Let’s align on your validation scope and build a pragmatic evidence set.