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AI in Computerized System Validation (CSV) for Life Sciences
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 6h 23m | Size: 3.98 GB
Master AI-powered CSV using GAMP 5, CSA, validation documentation, testing, compliance, and practical AI prompts
What you'll learn
Apply AI across every phase of the Computerized System Validation (CSV) lifecycle while maintaining GxP compliance.
Create high-quality CSV documents using ChatGPT, Microsoft Copilot, Claude, and Gemini with regulatory-aware prompts.
Identify AI hallucinations, validation risks, and compliance pitfalls before they impact regulated documentation.
Build inspection-ready, human-reviewed AI workflows that comply with FDA 21 CFR Part 11, EU Annex 11, ALCOA+, CSA, and GAMP 5.
Generate AI-assisted validation plans, URS, FS, DS, IQ, OQ, PQ, traceability matrices, risk assessments, CAPA, and validation reports.
Use AI effectively for Agile CSV, Cloud/SaaS validation, Computer Software Assurance (CSA), and digital transformation projects.
Develop AI governance practices for regulated pharmaceutical computerized systems.
Confidently explain and defend AI-assisted validation decisions during regulatory inspections and audits.
Requirements
Basic understanding of Computerized System Validation (CSV) concepts.
Familiarity with GAMP 5, validation lifecycle, and validation documentation.
Experience with pharmaceutical or life sciences computerized systems is recommended.
Knowledge of FDA 21 CFR Part 11 and EU GMP Annex 11 is helpful but not mandatory.
A computer with internet access to use ChatGPT, Microsoft Copilot, Claude, or Gemini during practical demonstrations.
No prior AI experience is required-we will teach AI prompting from the fundamentals for CSV professionals.
Description
Master AI-Powered Computerized System Validation (CSV) for the Pharmaceutical & Life Sciences Industry
Artificial Intelligence is rapidly changing how validation professionals work. Instead of spending countless hours writing validation documents, designing test cases, reviewing evidence, or preparing compliance documentation, modern AI tools can dramatically accelerate these activities-when used correctly and within regulatory expectations.
This comprehensive course is designed specifically for professionals working in Computerized System Validation (CSV), IT Quality Assurance (ITQA), Computer Software Assurance (CSA), Quality, Automation, Engineering, Manufacturing, Laboratory Systems, and Digital Transformation within regulated pharmaceutical and life sciences organizations.
Unlike generic AI courses, this program focuses entirely on how AI can be safely, effectively, and compliantly integrated throughout the complete validation lifecycle while maintaining regulatory compliance with FDA 21 CFR Part 11, EU GMP Annex 11, GAMP® 5 Second Edition, ALCOA+, Data Integrity principles, and emerging Good Machine Learning Practices (GMLP).
Throughout the course, you'll follow the complete Software Development Life Cycle (SDLC) and Validation Life Cycle, learning exactly where AI adds value, where human review remains mandatory, and how to avoid common compliance risks.
You'll learn how to use AI to generate and improve
- Validation Plans (VP)
- User Requirements Specifications (URS)
- Functional Specifications (FS)
- Design Specifications (DS)
- Configuration Specifications
- Risk Assessments
- Traceability Matrices (RTM)
- IQ, OQ, PQ and UAT Protocols
- Test Scripts
- Test Evidence Reviews
- Defect Reports
- Deviation Reports
- CAPA Documentation
- Validation Summary Reports
- Authorization for Use (AFU)
- Periodic Review Reports
- Change Control Documentation
- Retirement & Decommissioning Documentation
This course goes far beyond document generation.
You will learn how AI assists in
- Risk-based validation planning
- Test design optimization
- Evidence verification
- Requirement traceability
- Regression testing
- Data migration validation
- Security testing
- Audit Trail testing
- Electronic Signature verification
- Backup & Disaster Recovery validation
- Periodic Review trend analysis
- Defect classification
- CAPA root-cause analysis
- AI governance in regulated environments
- Cloud & SaaS validation
- Agile CSV
- Computer Software Assurance (CSA)
Every module includes practical demonstrations, real-world scenarios, AI prompts, compliance discussions, regulatory expectations, and best practices that you can immediately apply in your projects.
Unlike theoretical AI courses, every prompt shown in this program is written specifically for pharmaceutical validation activities and demonstrates how experienced validation professionals can increase productivity while ensuring compliance.
The course concludes with a complete end-to-end capstone project where you'll validate a cloud-based laboratory system using AI-assisted documentation, testing, risk assessment, compliance verification, and final release activities.
By the end of this course, you'll have your own reusable AI prompt library, practical workflows, and validation playbook that can significantly reduce manual effort while maintaining regulatory expectations.
Whether you're an experienced validation engineer or just beginning your CSV journey, this course provides practical knowledge that can immediately improve your daily work.
Who this course is for
CSV Engineers and Validation Professionals working in pharmaceutical and life sciences industries.
Computer Software Assurance (CSA) professionals adopting AI-assisted validation.
QA, IT QA, Compliance, and Quality Assurance professionals responsible for GxP computerized systems.
Validation Consultants working with LIMS, MES, ERP, SAP, QMS, LMS, SCADA, PLC, or laboratory systems.
Digital Transformation, Automation, and DevOps professionals supporting regulated pharmaceutical systems.
Project Managers, System Owners, and Business Analysts involved in GxP system implementation.
Professionals preparing for AI-driven validation practices and future regulatory expectations.
Experienced CSV professionals who want practical, real-world AI workflows instead of generic AI tutorials.
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