CSV vs CSA: What Changes, What Doesn’t, and How to Choose

For years, the regulated industry has often followed the maxim: “If it’s documented, it’s compliant.” But is the goal truly to validate paperwork, or to assure software quality, patient safety, and data integrity? When regulated life sciences teams feel overwhelmed by documentation, the critical differences between Computer Software Assurance (CSA) and traditional Computer Software Validation (CSV) finally come to the surface. If you work in QA, compliance, or software deployment, this guide can help you determine which path best suits your environment.

Key takeaways:

• Computer System Validation (CSV) remains valid and fully compliant, but can create heavy documentation burdens for modern systems.

• Computer software assurance (CSA) is the FDA’s preferred approach.

• Unscripted testing under CSA reduces the time for test-script creation and documentation by up to 80%.

• Choosing between CSV and CSA depends on system risk, update frequency, and the organization’s modernization maturity.

CSV vs CSA

Computer Software Validation (CSV)

Computer Software Validation refers to the traditional approach of establishing that software specifications conform to user needs and intended uses, through examination and objective evidence. This relies heavily on software testing throughout the software development life cycle. 

Computer Software Assurance (CSA)

Computer Software Assurance is a risk-based approach for establishing confidence that software is fit for its intended use. It specifically targets computers and automated data processing systems used as part of medical device production or the quality system. CSA utilizes a least-burdensome approach, meaning the validation effort is proportionate to the risk of compromised safety. 

Comparison

The relationship between these two concepts is defined by the FDA, which issued the Computer Software Assurance for Production and Quality System Software guidance on September 24, 2025, to supersede Section 6 of the older General Principles of Software Validation guidance. [1,2]

CSV: Limitations and challenges in modern digital environments

CSV was built for a different approach – one where software changed slowly, and updates happened once every few years. Technology evolved, but the CSV model stayed the same.

SaaS update cadence 

Traditional CSV mandates that when any change is made to the software, the validation status of the software needs to be re-established. This requires a validation analysis to determine the extent and impact of the change on the entire system, followed by appropriate regression testing. 

In a modern Software as a Service (SaaS) environment, updates are frequent. The requirement of a full re-validation and extensive scripted testing for every change becomes a burden. 

Multi-system digital ecosystems 

Modern environments often involve complex digital ecosystems where software features may be integrated and involve multiple systems. This includes cloud computing models like: 

• Infrastructure as a Service (IaaS) – provides virtualized computing resources so companies don’t need to manage physical hardware;

• Platform as a Service (PaaS) – offers a ready-to-use environment for building and deploying applications without handling the underlying infrastructure;

• SaaS – delivers fully developed software applications over the cloud that users can access instantly without installation or maintenance.

CSV applies to systems composed of software components from many sources, such as: 

• in-house developed software;

• off-the-shelf software;

• contract software. 

The strict regulatory requirements for validation documentation mean the device manufacturer must assess the adequacy of the developer’s activities. This is important, especially for the Off-the-Shelf (OTS) software. Then they have to determine what additional efforts are needed to establish validation for their intended use. 

This complexity results from the fact that necessary validation information or proprietary documentation (such as source code or vendor life-cycle documents) is frequently unavailable from commercial equipment vendors. 

Complex, distributed validation burdens 

CSV requires that validation processes and tasks remain the ultimate responsibility of the device manufacturer. They can be dispersed at different locations and conducted by different organizations; however, the responsibility falls on the manufacturer. For software components purchased OTS, the device manufacturer is responsible for ensuring the vendor’s methodologies are appropriate and sufficient. 

The manufacturer may need to conduct sufficient black-box testing alone when vendor information is unavailable. The black-box testing is a software testing method where you evaluate what the system does without knowing anything about how it works internally. This placed an extremely heavy problem on the device manufacturer to solve. It is known that CSV decreases the validation efforts by 30%. [3]

Increased strictness: Why qualification scripts grew under CSV

The inclusion of Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ) terminology in validation guidance promoted a rigid structure where validation tasks at the user site often involved extensive test cases with predefined expected results. 

• IQ verifies that the system is installed correctly according to the manufacturer’s specifications.

• OQ confirms that the system functions as intended under defined operating conditions.

• PQ proves that the system consistently performs as intended in the real production environment.

This led manufacturers to often perform unnecessary and highly redundant validation, particularly for lower-risk functions. As a result, there were inflated OQ/PQ scripts and excessive focus on the number of tests rather than targeted risk, often failing to address the true system performance issues.

The accumulation of compliance debt

The complexity and documentation requirements of CSV often resulted in manufacturers either delaying validation or performing incomplete validation for software systems. The sources acknowledge that failure to document and archive test cases can significantly increase the level of effort and expense of revalidating the software. 

This cumulative drag of delayed validation and incomplete documentation – the “compliance debt” – was a widespread issue stemming from the overly burdensome nature of CSV.

The accumulation of compliance debt

The complexity and documentation requirements of CSV often resulted in manufacturers either delaying validation or performing incomplete validation for software systems. The sources acknowledge that failure to document and archive test cases can significantly increase the level of effort and expense of revalidating the software. 

This cumulative drag of delayed validation and incomplete documentation – the “compliance debt” – was a widespread issue stemming from the overly burdensome nature of CSV.

How CSA responds to these CSV limitations

CSA vs CSV is a natural answer to the strict policy for verification. CSA answers the limitations of CSV by shifting validation from a paperwork exercise to a business enabler. Teams reclaim time, budgets, and expertise that were previously locked inside endless documentation loops.

What makes this shift even more powerful is how naturally CSA fits the modern tech stack. Cloud platforms and SaaS applications no longer trigger panic or mountains of rework. 

CSA lets organizations lean on trusted vendor evidence and concentrate their own effort only on the pieces that are customized or critical. This makes cloud adoption smoother and far more budget-friendly. Automation, intelligent analytics, bots, and AI tools become practical, low-friction additions to production and quality environments. CSA encourages the use of tools to automate testing and assurance activities, maximizing efficiency.

CSV vs CSA in real use cases 

CSV vs CSA – risk profiles and when each makes sense

Choosing between CSV and CSA depends on the level of risk and the type of system, as well as how often it changes. 

When CSV is still appropriate

CSV remains the right choice for systems involving high patient-impact and strict regulatory dependencies. It is especially important if there is a direct patient risk, should the system fail, meaning it is providing more safety.

When CSA is the better route

CSA is the smarter choice for modern, configurable, cloud-driven, and frequently updated systems. It is excelling when SaaS platforms release updates every few weeks.

What FDA actually expects today 

The FDA’s current regulatory posture is characterized by a flexible approach to software assurance that shifts the compliance burden from exhaustive documentation to a focused one.

• CSV is still compliant – the core requirement remains that software must be validated for its intended use, and the FDA has not withdrawn its foundational document. CSV methodology can still be used, provided it satisfies applicable requirements.

• CSA aligns with the FDA’s preferred, least-burdensome approach; this philosophy of minimizing unnecessary burden aligns with the FDA’s broader policy. 

• Crucially, both CSV and CSA must satisfy the core regulatory mandate of 21 CFR Part 11 concerning electronic records and electronic signatures for ensuring compliance.

• FDA inspectors expect rational justification, not piles of evidence – the goal of CSA is for the manufacturer to establish and capture sufficient objective evidence that the software function was assessed and performs as intended. The assurance record needs only to retain sufficient details of the assurance activity.

• FDA now supports flexibility in testing methods – the level of assurance rigor must be commensurate with the process risk. For software features that are not high process risk, manufacturers may consider using unscripted testing methods such as scenario testing, error guessing, or exploratory testing. Even for high process risk applications, manufacturers may choose a hybrid approach.

Transition from CSV to CSA

Shifting to Computer Software Assurance is not replacing test methods. It is a whole new viewpoint of assurance. 

The transition starts with updating policies and Standard Operating Procedures (SOPs) to move teams away from documentation-heavy practices and toward a value-driven approach. This includes redefining how risks are assessed and how supplier evidence is used.

To make this shift work, Quality, IT, and business teams must align on when CSA applies and how frequently updates change validation expectations. Training is essential so teams understand why CSA reduces burden without reducing control.

The detailed steps for updating policies, templates, and governance will be covered in the CSA Implementation Guide.

How moving from CSV to CSA impacts stakeholders

The shift to the new framework represents a fundamental change in the stakeholders’ workflow. The role of QA Reviewers shifts from strictly verifying piles of documentation to performing risk-based rationale review. QA’s focus moves toward confirming the justification and risk management plan rather than auditing test script volume.

IT System Owners, particularly those managing modern or cloud-based infrastructure, gain greater flexibility and efficiency in maintaining software used in production or the quality system. 

Regulatory teams have the ultimate responsibility for ensuring software is validated. They benefit from the alignment of CSA with the FDA’s preferred least burdensome approach. 

Engineering teams experience a redirection of effort away from highly formal documentation and towards targeted testing based on risk. They gain flexibility in selecting the most effective assurance activities. 

Vendors and integrators of software play a more critical, formalized role in the validation lifecycle of the device manufacturer. 

Manufacturers are actively encouraged to leverage the validation work already performed by developers as a starting point for their own assurance. This means vendors who provide comprehensive documentation regarding their software development life cycle and cybersecurity are more desirable partners.

Choosing the right path: A decision framework

Conclusion

CSV isn’t going away anytime soon; it remains a valid and reliable approach for ensuring compliance. According to the FDA, CSV vs CSA is not a war between new and old systems; they are just two approaches to the same problem. Ultimately, the choice between the two should be guided by your system’s risk profile. 

If you need a reliable partner for introducing CSA or CSV in your work, do not hesitate to contact us at BGO Software

References:

[1] U.S. Food and Drug Administration, Center for Devices and Radiological Health, & Center for Biologics Evaluation and Research. (2025, September 24). Computer Software Assurance for Production and Quality System Software: Guidance for industry and Food and Drug Administration staff. U.S. Department of Health and Human Services.

[2] U.S. Department of Health and Human Services, Food and Drug Administration, Center for Devices and Radiological Health, & Center for Biologics Evaluation and Research. (2002, January 11). General principles of software validation; Final guidance for industry and FDA staff [Guidance Document].

[3] Sharma, A. (n.d.). eBook CSA-FINAL. Scribd. https://www.scribd.com/document/738051394/eBook-CSA-FINAL

[4] Thirumalai, P. P. (2023). Computer software assurance – An interpretation and future [Unpublished manuscript]. Center of Excellence in Regulatory Science and Innovation, Johns Hopkins University. https://publichealth.jhu.edu/sites/default/files/2023-07/pavithra-thirumalai.pdf

Dobrin Kolarov

Healthcare business analyst with expertise in marketing and business development, and holds an MPharm degree. He specialises in creating and executing communication strategies that make digital health solutions and pharmaceutical technologies clear, accessible, and resonation for their audiences.