AI in Computer Software Assurance (CSA)

Throughout our blog series on the evolution of software assurance in the life sciences industry, we’ve examined why life sciences companies are moving from CSV to CSA, how organizational change supports successful adoption, and how training, resourcing, and legacy-system triage can help build a sustainable approach.

This final installment addresses a question that typically follows: Can technology make validation faster and more cost-effective? We believe it can. For that reason, we recommend evaluating an AI-enabled validation tool as part of the transition to CSA rather than treating it as a separate, later initiative.

Here, we examine where the regulatory view of AI stands today, how quickly the underlying technology is changing, why we view AI as part of the CSA transition, and how organizations can put it to work without creating new compliance risks.

The Regulatory Landscape

Although the agencies differ in their specific requirements and timelines, their guidance reflects a consistent direction: AI should be governed according to its intended use, associated risk, and potential impact on product quality and patient safety.

FDA CSA guidance (final, September 2025). Already contemplates AI tools used within production and quality systems and applies the same risk-based logic to them as to any other software.
FDA draft guidance on AI to support regulatory decision-making for drugs and biologics (January 2025). Introduces a risk-based credibility framework built around the question the AI is answering (its context of use) and the consequence of a wrong answer.
FDA and EMA guiding principles for good AI practice (January 2026). Ten joint principles covering AI across the medicines lifecycle, and the clearest signal yet that the two agencies intend to align.
EU GMP Annex 22, Artificial Intelligence (draft, July 2025). The first GMP text written specifically for AI. It covers static, deterministic models used in critical applications and states that generative AI and large language models should not be used in critical GMP applications, while allowing them in non-critical uses with a qualified person accountable for the output. EMA held a stakeholder workshop in mid-2026 and is targeting final text in Q4 2026. Revised Annex 11 and Chapter 4 were released for consultation alongside it.
EU AI Act. Obligations for high-risk AI systems were moved to December 2027 under the Digital Omnibus agreement. Deferred, not cancelled.
ISPE GAMP Guide: Artificial Intelligence (July 2025). The industry’s practical framework for applying GAMP’s risk-based lifecycle to AI-enabled computerized systems.

Taken together, these publications reflect the same principles that underpin CSA: defining intended use, assessing risk, maintaining accountability through qualified personnel, and controlling the system throughout its lifecycle. Organizations that have already adopted CSA are therefore familiar with the risk-based language regulators are applying to AI. Organizations that continue to rely on traditional CSV practices may need to adapt both their validation approach and their AI governance framework.

Expect this list to keep changing. Annex 22 may be revised before it is final, and its position on generative AI is still being debated. The goal is a program that absorbs new guidance without being rebuilt each time: risk-based, with documented rationale and a person in the loop.

The Technology Is Moving Faster Than the Guidance

As quickly as regulation is moving, the technology is moving faster. Model capability now improves in months, not years. Tools that could only summarize a document two years ago can now draft a risk assessment against your own criteria, generate test cases from a requirement, and review executed evidence for gaps. Three shifts matter most for validation teams.

AI is being built into the systems you validate. eQMS, LIMS, MES and document management vendors are shipping AI features in routine releases. Each one is a new function with an intended use to define and a risk to assess, and it arrives on the vendor’s schedule, not yours.
Release cadence has changed the math. Cloud platforms update several times a year. Under CSV, every release meant an impact assessment and a round of regression scripts. Manual effort does not keep pace with that cadence, even under CSA.
AI-enabled validation tools are becoming more capable. Digital validation platforms can now support activities such as drafting documentation, maintaining traceability, and performing preliminary reviews. When implemented with appropriate controls and human oversight, these capabilities can help organizations apply CSA more consistently and at scale.

Selecting an AI-enabled tool is also a change-control decision. Model updates, new features, and configuration changes can affect system behavior. Organizations that manage these changes effectively treat the AI model and its configuration as controlled items: they document the version in use, assess changes, and perform appropriate re-verification when necessary.

Our Position: CSA Without AI Is Not a Recommended Approach

We consistently advise clients to consider an AI-enabled validation tool when transitioning to CSA. CSA changes the scope and focus of assurance activities, while AI can improve the speed and consistency with which those activities are performed. Implementing CSA without the supporting technology may therefore limit the operational benefits of the transition.

CSA removes low-value work; AI speeds up the work that remains. Intended use statements, risk assessments, rationale, traceability and evidence review still take time. AI drafts, cross-checks and flags them. People decide.

Consistency is the hardest part of CSA. Part 3 described the calibration problem: trained assessors reaching different ratings for the same system. A tool that applies your documented risk criteria the same way every time gives assessors a consistent starting point and makes outliers visible.

It is where the return actually shows up. The documentation savings from CSA are real, but modest if every step is still manual. The larger return comes from shorter cycle times per system and per release, fewer review loops, and staff time moved to the high-risk functions that deserve it.

AI can improve speed and quality. AI can review large volumes of test evidence without the variability that often accompanies repetitive manual review. It can identify missing traceability, inconsistent terminology, incomplete records, and evidence that does not correspond to the expected result. These are common documentation issues that can contribute to audit or inspection observations.

Doing both together is cheaper than retrofitting. Building new SOPs, templates and training around a manual process, then rebuilding them for a tool a year later, means paying for the transition twice.

Where AI Fits in the CSA Lifecycle

AI does not replace the CSA process. Instead, it can support specific activities within that process, while qualified personnel remain responsible for risk decisions, acceptance of evidence, and final approval.

CSA Step	How AI Can support the Process	What Your People Own
Define intended use	Drafts intended-use statements from requirements, vendor documentation and process maps; flags gaps and ambiguous functions	Confirming the intended use is accurate and complete
Assess risk	Proposes a risk rating for each function against your documented criteria, with its reasoning shown	Agreeing or overriding the rating and signing the rationale
Plan Assurance	Recommends scripted, unscripted or vendor-leveraged assurance by risk; drafts test cases from requirements	Approving the plan and the level of rigor
Execute	Guides execution in a digital platform, captures evidence automatically, runs automated regression on each release	Performing and witnessing high-risk testing; judging unexpected results
Review and record	Checks evidence against expected results, traceability and documentation rules; summarizes deviations	Final review and approval
Maintain	Reads vendor release notes, maps changes to validated functions, drafts the impact assessment; supports periodic review	Change-control decisions

Digital Platforms, Unscripted Testing and Vendor Evidence

AI works best on a foundation that is already digital. Three other parts of the technology picture matter as much as the AI itself.

Digital validation platforms. Paperless authoring, execution, review and approval, with traceability and Part 11 controls built in. Without one, AI produces drafts that people copy into Word and print. With one, the whole record stays connected, searchable and inspection-ready.
Unscripted and exploratory testing tools. CSA explicitly allows unscripted and ad hoc testing for lower-risk functions. Tools that record the session, capture screens and log results automatically turn exploratory testing into defensible evidence without writing scripts first.
Vendor evidence. Supplier qualification packages, audit reports and vendor test results can be leveraged under CSA rather than repeated. AI can read those packages and show where the vendor’s testing covers your intended use and where it does not.

Measuring the ROI of CSA and AI

The return from CSA and AI is real, but it has to be measured, not assumed. Part 3 recommended tracking effort explicitly in the first year; that baseline is what proves the case.

Track:

Effort. Hours per system and per release, compared with the CSV baseline.
Speed. Elapsed time from change request to release approval.
Quality. Review cycles per deliverable and documentation errors found in review.
Focus. Share of assurance effort spent on high-risk functions.
Outcome. Audit and inspection observations on CSA-assured systems.

Expect the implementation pattern described in Part 3: effort will initially increase during tool selection, configuration, and pilot execution before declining below the CSV baseline. The right tool can shorten the transition period and reduce the ongoing level of effort, but it will not eliminate the need for implementation and adoption activities.

Assuring the AI Tool Itself

An AI tool used in validation is itself software used in the quality system, and CSA applies to it. That is not a reason to avoid it; it is a reason to set it up correctly.

Define its intended use narrowly. A tool that drafts documents for human review carries a very different risk from one that approves anything. Keep a qualified person accountable for every output, consistent with Annex 22 and the FDA and EMA principles.
Control versions and configuration. Know which model version the tool uses, and treat model updates as changes to assess.
Put prompts, templates and risk criteria under document control. They shape the output as much as the model does.
Protect your data. Confirm where data is processed and retained, and that confidential and GxP data is not used to train public models.
Keep an AI inventory. List every AI-enabled tool and every AI feature in your validated systems, with its intended use and risk rating. It is fast becoming one of the first things an auditor asks to see.

A Practical Starting Point

Pick one pilot. Choose a system from the Part 3 triage, ideally one with a vendor upgrade coming.
Select the tool using clear criteria, including its suitability for GxP use, Part 11 controls, transparency of AI-generated outputs, the vendor’s quality-system maturity, and its ability to integrate with the systems already in use.
Assure the tool under CSA. Give it a narrow intended use, a risk assessment and a documented rationale.
Run the pilot side by side and measure. Compare hours, elapsed time and review findings against your current approach.
Update and scale. Build the tool into SOPs, templates and role-based training, then extend it in the order the triage sets.

Closing the Series

Across four parts, the message has been consistent. CSA is FDA’s stated expectation, not a draft concept (Part 1). It succeeds or fails on culture (Part 2), and it needs real training, dedicated resources and a disciplined legacy triage (Part 3). Technology is what turns that effort into lasting speed, consistency and return.

The regulatory framework for AI will continue to evolve, and the underlying technology will advance even more rapidly. Organizations will be better positioned to manage those changes when they have a risk-based CSA program, clear governance, qualified human oversight, and technology that supports controlled, repeatable validation practices.

How RCM Life Sciences Can Help

RCM Life Sciences helps organizations move from traditional CSV to a more efficient, risk-based CSA model without losing control of compliance, quality, or data integrity. Our team brings practical experience across quality, regulatory, computer system validation, eQMS, project management, and digital transformation initiatives, helping clients assess current validation practices, redesign SOPs and templates, define risk-based assurance strategies, train cross-functional teams, and support inspection-ready implementation.

RCM supports organizations at every stage of CSA adoption, including evaluating current practices, updating legacy CSV procedures, implementing regulated systems, and reducing outdated validation documentation. Our team helps develop right-sized, sustainable approaches aligned with FDA expectations and industry best practices while maintaining patient safety and product quality.

Contact us to discuss selecting and implementing AI-enabled validation tools as part of your CSA transition, assuring AI-enabled systems and features, building an AI inventory and governance framework, or measuring the return on your validation program.