CSA vs CSV Validation: Which Should Your Lab Follow?

Is your lab still using outdated and costly CSV validation methods? 

The FDA's 2022 guidance on Computer Software Assurance (CSA) isn't just another regulatory update—it represents a fundamental shift in how labs should approach software validation. 

For lab managers who've invested years building CSV processes, this transition raises critical questions: What changes are actually required? Will this create more work or less? And most importantly, how will this impact compliance status during your next audit?

In this guide, we’ll show you how CSV and CSA stack up side-by-side so you can understand the differences between the two.

CSA vs CSV: Key Takeaways

This is a lengthier discussion than most on our site, so here are a few key takeaways that you can leave this article with:

• CSA shifts validation focus from documentation to critical thinking and patient safety -
• CSA can reduce validation time by 30-50% for most lab systems
• High-risk functions still require comprehensive documentation
• FDA encourages this transition but hasn't made it mandatory (yet)
• Making the switch requires a methodical transition plan, not an overnight change

What is Computer System Validation?

Computer System Validation (CSV) is a documented process of ensuring that computerized systems perform as intended in a consistent and reproducible manner. 

This is important because labs in regulated industries like pharmaceuticals and medical devices need to meet specific requirements and quality standards to meet compliance standards. For example, a laboratory implementing a new LIMS would follow CSV procedures to verify that sample tracking, data analysis, and reporting functions work correctly and comply with regulations like FDA 21 CFR Part 11.

To validate its computerized systems, a lab must create and follow a computer system validation plan, which we’ll detail next.

What is a Computer System Validation Plan?

A computer system validation plan is a document that contains high-level planning for the validation of your systems and software. 

It’s easy to let your mind jump to “testing” when you hear the word “validation;” however, the need for a validation plan goes well beyond the tests run in your lab. To meet GxP standards, you will need a validation plan for all computerized systems in your lab, including computerized systems and software. 

Computerized systems cover more than just software, though. To create a successful validation plan, the following must all be validated: 

• Embedded Systems Equipment: PLC, Controllers, Control Panels equipment for the various processes.
• Software: Any software you purchase and use in your lab to interact with tests or data. This includes LIMSs, ELNs, and ERPs.
• Spreadsheets: Any spreadsheet application you use, such as Microsoft Excel or Google Sheets.
• Document Management Systems: Any software used for storing and tracking electronic or scanned documents.
• Operating systems and software: Any operating systems or office software installed on your machine to run business applications.

How to Perform CSV Tests

An effective validation plan will be made up of the following elements:

• Purpose and scope
• Roles and responsibilities
• Validation parameters and acceptance criteria
• Testing procedures
• Documentation requirements
• Training requirements
• Change control procedures
• Maintenance and revalidation schedules

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We’ll walk through each of these in more depth next.

Purpose and Scope

Your validation plan must start with a clear purpose and scope section that serves as the foundation for all validation activities. 

This section outlines exactly what is being validated, whether it's a new analytical method, a piece of equipment, or an entire laboratory system. It defines the boundaries of the validation effort, including which departments are affected and what quality attributes must be demonstrated. Most importantly, it connects the validation effort to specific regulatory requirements that the lab must meet.

Roles and Responsibilities

Next, your validation plan must contain well-defined roles and responsibilities. 

A validation team typically includes a project manager or validation lead who oversees the entire process, quality assurance personnel who ensure compliance with standards, and subject matter experts who provide technical expertise. Clear delineation of who has the authority to review and approve various stages of the validation process prevents bottlenecks and ensures accountability throughout the project.

Validation Parameters and Acceptance Criteria

These are the specific measurements and standards that prove the system works as intended. 

For your systems and software, that might include:

• User access control verification
• Data integrity checks
• System interface testing
• Backup and recovery testing

Each parameter must have clear, measurable acceptance criteria that align with the lab's quality requirements and regulatory standards. The criteria should be specific enough to enable objective pass/fail decisions but practical enough to be achievable under normal operating conditions.

Testing Procedures

Testing procedures form the practical framework of the validation process. 

These procedures detail exactly how each validation parameter will be tested, including sampling plans, control sample requirements, and data collection methods. When it comes to computerized systems, the top procedures to bear in mind are:

• Installation Qualification (IQ): Verifies proper system installation.
• Operational Qualification (OQ): Confirms system operates as designed.
• Performance Qualification (PQ): Ensures consistent performance under actual conditions.

Documentation Requirements

Documentation requirements in a validation plan cannot be overstated. 

The old quality adage "if it isn't documented, it didn't happen" is particularly relevant to validation. A complete documentation package includes the validation master plan, standard operating procedures, test protocols and results, raw data records, equipment qualification documents, calibration records, training records, and deviation reports. The final validation report pulls all this documentation together to demonstrate that the validation was successful and complete.

Training Requirements

Training requirements ensure that personnel performing validation activities are competent and qualified. 

Your training program should include both initial and ongoing training, with clear methods for assessing competency. Documentation of training completion and verification of competency are essential parts of the validation record. This extends beyond just the validation period - ongoing training ensures that validated processes are performed correctly.

Change Control Procedures

Change control procedures protect the validated state by managing modifications systematically. 

Any changes to validated systems, whether planned improvements or emergency repairs, must be evaluated for their impact on the validation status. This includes a formal change request process, impact assessment, documentation updates, and procedures for implementing changes. Some changes may trigger partial or complete revalidation, making change control a critical part of maintaining validated status.

Maintenance and Revalidation schedules

This includes routine maintenance requirements, performance monitoring schedules, and criteria for when revalidation is necessary. Periodic reviews ensure that validation remains current and effective. Calibration schedules, software update procedures, and emergency maintenance protocols all play a role in maintaining the validated state of laboratory systems.

Learn more about what goes into a successful computer system validation plan in this guide.

While many labs follow Computer System Validation, the FDA has begun pushing for Computer Software Assurance, which we will detail next.

What is Computer Software Assurance?

As you can imagine, validating each computerized system can be a time-consuming process –  especially for modern labs that heavily utilize software in their processes.

Computer Software Assurance (CSA) is a more modern, risk-based approach to validating computerized systems that focuses on critical thinking and patient safety rather than extensive documentation. 

Introduced by the FDA as an evolution of CSV, CSA emphasizes testing what matters most based on risk assessment. For instance, under CSA, a laboratory implementing the same LIMS might concentrate validation efforts on high-risk functions that directly impact patient safety or data integrity while applying less rigorous testing to lower-risk features, resulting in a more efficient validation process.

How to Perform a Risk-Based Approach to CSA

Rather than create a validation plan that treats each system equally, a lab that wishes to follow CSA guidelines would work through the following phases:

• Risk assessment and categorization
• High-risk testing 
• Medium-risk testing 
• Low-risk testing
• Use varied testing methods
• Continuous risk evaluation

Risk Assessment and Categorization

The first step of CSA is to begin with risk assessment and categorization by thoroughly analyzing each function.

For example, a lab implementing a new LIMS would start by identifying sample accessioning as high-risk because errors could lead to misidentification of patient samples and potentially harmful treatment decisions. Their risk assessment documentation would explain this critical relationship between correct sample identification and patient outcomes, justifying the high-risk classification. This is important because the lab will allocate more time, testing, and resources to higher-risk activities rather than treat each aspect of the LIMS equally (we’ll share how to handle medium and low-risk functions later).

Perform High, Medium, and Low-Risk Testing

For high-risk functions, you must run and document the most tests to demonstrate quality. 

Using our hypothetical lab example that identified sample accessioning as high risk, the next steps they would take is to verify that barcodes are correctly generated and read, that unique identifiers are never duplicated, and that sample information remains intact throughout the workflow. They would then need to document this testing with formal test scripts, expected results, actual outcomes, and deviation records to provide evidence of proper function for this critical process. Focusing on high-risk activities first allows your lab to go deeper into tests that matter before moving on to medium-level risks. 

Medium and low-risk functions will require comparatively less testing and documentation. In some cases, documentation could be a simple screenshot of expected results with supporting notes.

Use Varied Testing Methods

As you can see, different risk levels will require different levels of testing. It’s important to take a varied approach to testing so that your methods are appropriate to each function’s risk level.

This could include:

• Automated scripts
• Detailed notes
• Screenshots

A varied approach to testing focuses resources where they matter most for data integrity.

Continuous Risk Evaluation

Finally, your lab should establish a plan for continuous risk evaluation of its systems. 

CSA is an ongoing process, not a one-and-done activity. Make sure to review your systems after updates to assess whether functions have changed in ways that alter their risk profiles. This includes feature changes or new features, as well as new workflows that your team builds. With each evaluation, run through the risk assessment and test-writing process accordingly.

Scripted vs. Unscripted Testing in CSA

A fundamental shift in CSA is the approach to testing methodology, which varies based on risk levels. CSA introduces two primary testing approaches:

Scripted Testing involves formal, documented test protocols with predefined steps, inputs, and expected results. This approach includes:

• Robust Scripted Testing: Comprehensive documentation of test cases with detailed steps, expected outcomes, and formal evidence collection
• Limited Scripted Testing: A streamlined version that maintains structure but with reduced documentation requirements

Unscripted Testing allows for more flexible, creative approaches to verification without rigid documentation. This includes:

• Ad-hoc Testing: On-the-spot testing based on tester knowledge and intuition
• Error-guessing: Testing based on experience with common failure modes
• Exploratory Testing: Simultaneous test design and execution that adapts as the tester learns about the system

The CSA approach encourages using unscripted testing for low-risk functions while reserving more rigorous scripted testing for high-risk functions. This is a significant departure from CSV, which typically required scripted testing for all functions regardless of risk level.

By matching the testing methodology to the risk level, labs can focus their validation efforts where they matter most, saving time and resources while maintaining compliance and quality.

Benefits of CSA Over CSV

CSA allows for a nuanced approach to verifying your systems that gives your lab flexibility and control over its testing. This nuance leads to a more efficient use of time and resources, along with these other benefits:

• More flexible: Greater flexibility in testing methodologies allows laboratories to select appropriate validation approaches based on risk. Instead of applying rigid, script-driven testing universally, teams can employ automated testing, ad hoc testing with screenshots, or leveraging vendor documentation where appropriate, making the validation process more adaptable.
• Reduced paperwork: Reduced documentation burden significantly decreases the paperwork associated with validation. Rather than generating extensive test scripts and results for every system function, CSA encourages documenting the critical thinking process and focusing detailed documentation only on high-risk areas, cutting unnecessary administrative overhead.
• Quicker implementation: Faster implementation timelines result from the streamlined approach. With less emphasis on exhaustive documentation and more focus on critical functions, organizations can validate and deploy systems more quickly, accelerating time-to-value without compromising quality or compliance.
• Agile: Better alignment with modern software development practices is another advantage. CSA accommodates agile development methodologies and continuous delivery models better than traditional CSV, making it more compatible with how software is actually developed and maintained today.

CSV vs CSA: Side-By-Side Comparison

We’ve covered quite a bit in this article, so to help drive home the essential points, here is a side-by-side comparison of CSV and CSA in tabular form:

CSV to CSA: A Practical Example

All of this is great in theory, but what about in practice?

Does CSA really deliver on the promise of saved time and a streamlined approach to validation?

Clarkston Consulting published a case study for a biotech client that transitioned from CSV to CSA, and the results speak for themselves. 

• Reduction of 69% in resource demand for the development of documents (42 documents down to 13 documents) 
• Reduction of 84% in the number of training courses required (25 instructor-led courses to 4 CBT courses) 
• Reduction of resources needed for training, as all instructor-led courses have been transitioned to on-demand CBT

The switch from CSV to CSA wasn’t just about following the latest guidance from the FDA. Clarkston Consulting demonstrates that CSA saves time, which means more time spent on science.

How to Transition From CSV to CSA

If you’re eager to change from a CSV approach to validation to CSA, this will require a gradual shift in approach rather than an overhaul of any processes. 

Here are the steps you can expect to take:

1. Review your current approach and documentation (2-4 weeks) - where are you pursuing documentation for documentation’s sake rather than documentation that genuinely contributes to software quality assurance?
2. Perform a GAP analysis (4-6 weeks) comparing your current CSV practices against CSA principles. 
3. Develop a risk assessment methodology (4-8 weeks) to categorize software functions based on their potential impact.
4. Implement CSA principles during the next revalidation cycle or when significant changes occur. 
5. Train your staff (2-3 weeks) on the new methodologies for CSA.
6. Update SOPs and validation templates (4-6 weeks) to reflect your new CSA approach. 

This transition should be collaborative, involving quality assurance, IT, and operational stakeholders to ensure the new approach maintains compliance while improving efficiency.

CSV and CSA Are One Piece of Lab Compliance. Download Our Lab Compliance Checklist For the Rest

The differences between CSV and CSA can be summarized in one word: nuance. 

CSA takes a risk-based approach that empowers your lab to evaluate its systems based on the risks their functions pose to your data and customers. 

Validating your systems (whether you validate each system equally or take a risk-based approach) is just one piece of a compliant and organized lab. For the rest, we recommend you download our lab compliance guide. 

In this checklist we break down steps your lab can take to help meet common regulations along with recommendations for software products to support your lab as well. Fill out the form below to get the free checklist and take a step toward better data and compliance.