CSV stands for Computer System Validation. The goal of CSV is to provide documented evidence that a computer system is fit for its intended use and complies with regulatory standards.
CSA stands for Computer Software Assurance and represents the FDA’s new guidance towards validation, encouraging risk assessment and critical thinking beyond simply focusing on functionality.
This new approach seeks to stay in line with new technologies, fast-paced software updates, cloud computing and storage and AI.
CSV and CSA can be considered two sides of the same coin, as these are both used in validating software used by life science companies, ensuring proper use according to compliance guidelines and established GxP validation best practices.
It can be said that CSA is the natural evolution of CSV, with the same main goal of validating software and computer systems. CSA, prioritizes a critical thinking-based approach that aims to replace mounds of useless printed screenshots and documentation with risk-based analysis, producing reduced - but far more meaningful - evidence that the validated system meets regulatory requirements for product quality, patient safety, data integrity, and security.
This “thinking-and-analysis first” approach rests in sharp contrast to “old school” CSV, which puts brute force documentation first and thinking last. CSA cuts to the chase and helps companies get down to the essential questions:
When companies plan, assess risk, and put critical thinking first, they are then prepared to develop appropriate validation plans and records. They can:
Although the documentation deliverables of CSA vs. CSV may be the same (validation plans, validation protocols, and test cases), CSA-based deliverables will be shorter, more focused, and more meaningful.
Consider how CSA and CSV differ regarding regulations, documentation and use of resources.
If any of these questions apply to you, the time has come to implement a CSA-optimized solution that streamlines your validation processes. Res_Q allows biotech and life-sciences organizations to stay in line with newer regulations and their updates while automating processes and reducing the paper-bound documentation that CSV typically entails.
AI will fundamentally change CSV and CSA, just as it is transforming many other work aspects. However, it's crucial to remember that AI should be a tool to assist humans in making processes faster and more efficient, not a replacement for them.
To guarantee compliance and success in CSV and CSA, any AI implementation must be accompanied by human QA. This ensures that the generated data aligns with previous results and regulatory requirements.
If you are interested in AI applications in GxP, please watch our webinar.
When comparing Computer System Validation vs. Computer Software Assurance, CSA directly addresses the most vital concerns regarding system integrity and safety through a risk-based approach.
In the end, CSA is about what is right for your business. The thinking and planning that goes into the project upfront allow you to allocate resources and test where it is needed most – mindful of quality and compliance, with the goal of keeping your GxP applications in a state of control.
Your team is aligned strategically and can work together to ensure that the software systems being validated are reliable, suited for intended use, and preserve data integrity.
Instead of wasting time on low-priority system validation tasks, you can ensure that there are no critical product quality issues and improve the user experience where it matters most. And that’s where the CSA vs. CSV difference becomes the most apparent.
Changing one letter – and changing your thinking – will help your company escape existing or mounting technical debt, saving valuable budget, resources, and time.