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Annex 22 and AI Governance for GMP: Human Review, Model Monitoring, and Change Control

  • Date
    15 July 2026
  • 12.00 PM Eastern Time (US/Canada)
    04.00 PM GMT

Course is now LIVE. Click below to join the session.

This course helps organizations establish practical AI governance by defining human review responsibilities, monitoring model performance, and applying validation and change control practices that support compliant use of AI within GMP-regulated operations. This course is designed for organizations implementing, validating, supporting, reviewing, or governing AI-enabled systems that influence regulated processes or quality outcomes.

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US $290 per learner
  • This course is Included in Subscription Pack
Subscription include access to entire Learning Library
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  • Faculty
    Carolyn Troiano
  • Duration
    90 Minutes
  • Course ID
    TF2849
  • Live Q&A +
    Post-live Continued Learning
  • Presentation Handout
    & Templates
  • Assessment
    & Certification Included

 

Why take this course?

Artificial intelligence is becoming increasingly integrated into software applications used across regulated life science environments, creating opportunities for improved efficiency while introducing new governance challenges. AI, machine learning, and large language models can support software development, testing, validation, and operational activities, yet these technologies also introduce risks related to hallucinations, bias, performance drift, and limited transparency. As organizations accelerate adoption, maintaining quality, compliance, patient safety, and reliable decision-making requires controls that extend beyond traditional technology management practices.


This webinar examines the governance expectations surrounding AI-enabled systems within GMP environments, with particular focus on human review, model monitoring, and change control. Participants will evaluate how Annex 22 requirements, Computer System Validation (CSV), Computer Software Assurance (CSA), GAMP®5 Second Edition principles, data integrity expectations, and 21 CFR Part 11 requirements apply to AI-supported systems. The session emphasizes the role of qualified human reviewers as the critical safeguard responsible for assessing AI-generated outputs, applying judgment, and maintaining accountability. Attention is also given to inspection readiness, validation approaches, and practical oversight strategies for managing AI throughout its lifecycle.

Establish Meaningful Human Oversight of AI Systems

Participants will gain practical understanding of how qualified personnel should evaluate AI-generated outputs, recognize limitations such as hallucinations and bias, and apply critical thinking when making quality and compliance decisions. This capability becomes increasingly important as organizations expand AI use while remaining accountable for outcomes, documentation, and regulatory expectations.

Apply Governance Controls Across the AI Lifecycle

Effective AI oversight extends beyond implementation and requires ongoing monitoring, validation, and change management. Participants will learn how governance activities support inspection readiness, how evolving models introduce new risks, and how organizations can maintain confidence in AI-supported processes while preserving compliance and operational effectiveness.

Key Areas Covered

  • Human review responsibilities and expert oversight of AI-generated outputs
  • AI risks including hallucinations, bias, performance drift, and data quality concerns
  • Comparison of traditional Computer System Validation (CSV) and Computer Software Assurance (CSA) approaches
  • Transitioning validation activities toward CSA-aligned practices and GAMP®5 Second Edition principles
  • Annex 22 expectations for AI governance, model monitoring, human review, and change control
  • 21 CFR Part 11 requirements for systems utilizing AI technologies
  • Validation, maintenance, and lifecycle oversight of AI-enabled applications
  • Inspection readiness considerations for AI-supported GMP systems

Who Must Attend

  • Information Technology Departments
  • QC/QA Departments
  • Analytical Chemists
  • Compliance and Audit Managers
  • Laboratory Teams
  • Automation Analysts and Managers
  • Manufacturing Departments
  • Supply Chain Departments
  • Regulatory Affairs Departments
  • Clinical Data Teams
  • Computer System Validation Specialists
COURSE DIRECTOR

Carolyn Troiano

Carolyn Troiano brings more than 45 years of experience in computer system validation, FDA compliance, data integrity, and technology implementation within regulated industries. Her participation in the FDA-industry effort that developed guidance for electronic records and electronic signatures, combined with extensive CSV and compliance expertise, directly supports the AI governance, validation, and oversight topics addressed in this webinar.

If you would like to request a Proforma invoice to sign up for this course. please click here

Commonly Asked Questions About This Subject

The following questions address practical regulatory, compliance, validation, quality, operational, and inspection-related considerations commonly associated with this subject.

What is the most difficult release decision when critical test results are still pending but the patient treatment window is rapidly closing?

A release decision made under time pressure becomes difficult to defend when urgency replaces documented scientific justification. Patient scheduling concerns, clinical commitments, and product viability constraints can create significant pressure to move material forward before the full quality picture is available.


Inspectors typically focus less on the decision itself and more on how the decision was reached. Records often reveal informal discussions, undocumented assumptions, or verbal agreements that influenced disposition decisions. Those gaps become highly visible during review.


Evidence that carries weight includes predefined decision criteria, documented risk assessments, historical process knowledge, product-specific justification, management involvement, and clearly established conditions governing release. Reviewers want to see that the decision framework existed before the event occurred rather than being developed afterward to support a desired outcome.


The strongest release records demonstrate that patient considerations and product quality considerations were evaluated together through a controlled process rather than treated as competing priorities.

How do chain-of-identity failures typically occur even when labeling controls appear adequate?

An operational failure point often emerges during process transitions rather than during manufacturing activities themselves. Labels may be accurate, records may be complete, and electronic systems may function properly, yet identity errors still occur during handoffs between teams, facilities, laboratories, couriers, and clinical sites.


Investigations frequently reveal that personnel relied on assumptions regarding patient identifiers, scheduling information, shipping notifications, or manually transcribed data. The failure often originates in workflow design rather than labeling execution.


Inspection concerns increase when identity verification occurs only at isolated checkpoints instead of being embedded throughout the process. A well-controlled label attached to the wrong material still creates a serious failure.


Strong systems typically contain multiple independent verification opportunities across operational steps. Reviewers often place greater value on process design that prevents identity confusion than on procedures that merely detect it after the error has already occurred.

What makes a traceability system difficult to defend during an inspection even when complete records exist?

Documentation concerns arise when traceability depends heavily on reconstruction rather than immediate visibility. Investigators may eventually connect every activity, transfer, test result, shipment, and disposition record, yet require extensive effort to do so.


Inspectors often test traceability in reverse. They may begin with a patient administration event and request rapid demonstration of the associated manufacturing history, testing activities, material movements, deviations, and approvals. Delays during this exercise frequently expose underlying weaknesses.


Inspection friction increases when information resides across disconnected databases, spreadsheets, emails, paper records, and third-party systems that require manual reconciliation. Complete records alone do not demonstrate operational control.


Evidence of control is stronger when authorized personnel can efficiently establish material history, patient linkage, product status, and decision history without extensive investigation. Traceability that depends on institutional knowledge or specific individuals becomes increasingly difficult to defend as operations expand.

At what point does growth from clinical production to commercial operations begin creating new chain-of-identity risks?

A governance concern appears when existing controls continue functioning technically but no longer scale operationally. Processes that worked effectively for a limited number of patient treatments can become vulnerable when production volume, staffing levels, manufacturing locations, and external partners increase.


Reviewers often encounter systems originally designed around direct communication and experienced personnel. As activity grows, those informal controls gradually weaken. Personnel turnover, parallel manufacturing campaigns, multiple collection sites, expanded logistics networks, and increasing documentation volumes introduce complexity that was previously absent.


Inspection observations frequently involve organizations that expanded capacity while maintaining procedures, review structures, approval workflows, or oversight mechanisms intended for much smaller operations.


Evidence that supports scalability includes stress testing of processes, periodic reassessment of control effectiveness, management review of operational trends, simulation exercises, and documented evaluation of new failure modes introduced by growth. Successful expansion generally reflects deliberate redesign rather than simple replication of existing practices.

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