Introduction

Artificial intelligence (AI) is becoming increasingly important in pharmaceutical manufacturing, quality management, clinical research, laboratory operations, and regulatory processes. However, using AI in a regulated environment also introduces risks related to data integrity, system reliability, patient safety, and regulatory compliance.

An AI governance audit checklist helps pharmaceutical companies evaluate whether their AI systems are managed responsibly throughout their lifecycle. It provides a structured way to review governance policies, risk assessments, human oversight, change control, vendor management, and documentation.

In GxP environments, AI governance should also align with applicable quality management systems, computerised system validation or assurance practices, data integrity expectations, and risk-based controls. The appropriate controls depend on the intended use of the AI system and its potential impact on product quality, patient safety, and data reliability.

GxP Cellators provides specialized auditing support for organizations seeking to strengthen their AI governance practices, identify compliance gaps, and improve inspection readiness.

What Is an AI Governance Audit?

An AI governance audit is a systematic assessment of the policies, procedures, responsibilities, controls, and documentation used to manage AI systems. It examines whether an organization has appropriate oversight of how AI solutions are selected, developed, tested, deployed, monitored, updated, and retired.

For pharmaceutical companies, the audit should consider how AI interacts with regulated processes. For example, an AI tool used to organize routine administrative information may have a different risk profile from a system that supports laboratory result interpretation, manufacturing decisions, or product quality assessments.

The purpose is not simply to confirm that an AI system works technically. Instead, the audit evaluates whether the organization can demonstrate appropriate control, traceability, accountability, and ongoing oversight.

Why Is AI Governance Important for Pharmaceutical Companies?

Pharmaceutical companies must maintain reliable processes and trustworthy records. Introducing AI without clear governance can create gaps in accountability, documentation, and risk management.

A structured governance program helps organizations:

• Define accountability for AI-related decisions.
• Identify and prioritize risks associated with AI applications.
• Maintain appropriate human review of critical outputs.
• Control changes to models, software, data, and configurations.
• Evaluate third-party AI providers and service arrangements.
• Protect sensitive information and maintain data integrity.
• Establish monitoring and escalation procedures.
• Prepare evidence for internal audits and applicable regulatory inspections.

Importantly, AI governance does not replace validation, qualification, or established quality controls. Instead, it helps coordinate these activities within a documented, risk-based management framework.

AI Governance Audit Checklist for Pharmaceutical Companies

The following checklist provides seven essential areas to review during an AI governance audit. Organizations should adapt these questions to their AI applications, quality systems, and applicable regulatory obligations.

1. AI Governance Framework

A documented governance framework establishes how AI systems are evaluated, approved, managed, and monitored across the organization.

During the audit, review the following questions:

• Is there a documented AI governance policy or framework?
• Has senior management approved the framework?
• Does it define the scope of AI applications covered?
• Does it address AI systems used in GxP activities?
• Are approval and escalation processes clearly documented?
• Is the framework reviewed periodically?
• Are relevant policies updated when business processes or regulatory expectations change?

What auditors should verify: Review the approved policy, associated procedures, governance meeting records, and evidence of periodic review. Confirm that the framework applies to relevant AI systems rather than existing only as a high-level policy.

2. Roles and Responsibilities

Effective AI governance requires clear accountability. Quality assurance, regulatory affairs, IT, data science, operations, and business teams may all contribute to the oversight of an AI system.

The audit should determine whether:

• AI system owners are formally identified.
• Quality and compliance responsibilities are documented.
• Approval authority is clearly assigned.
• An AI oversight committee or equivalent governance body exists where appropriate.
• Committee membership reflects the required technical and quality expertise.
• Personnel receive suitable training.
• Responsibilities are communicated to internal teams and relevant external partners.

What auditors should verify: Examine responsibility matrices, job descriptions, committee charters, training records, and meeting minutes. Check whether personnel understand their responsibilities when an AI system produces unexpected or potentially harmful results.

3. AI System Inventory and Classification

A complete AI inventory helps an organization understand which systems it uses and where governance controls are required. Without an accurate inventory, unapproved or undocumented AI applications may be overlooked.

Review whether:

• A centralized inventory of relevant AI systems is maintained.
• Each system has a defined owner and intended purpose.
• The inventory identifies the system’s supplier or developer.
• GxP relevance has been assessed.
• Systems are classified according to risk and business impact.
• New AI applications are assessed before deployment.
• Model versions and significant configuration changes are traceable.
• Retired or replaced systems are documented appropriately.

What auditors should verify: Compare the official inventory with information from IT, quality, laboratory, manufacturing, and other relevant departments. Where appropriate, also review approved software lists and supplier records to identify systems missing from the governance process.

4. AI Risk Assessment

Risk assessment is a central part of pharmaceutical AI governance. The level of control should reflect the system’s intended use, complexity, limitations, and potential impact on product quality, patient safety, and data integrity.

The audit checklist should include these questions:

• Has each relevant AI system undergone a documented risk assessment?
• Is the intended use clearly defined?
• Are potential failure modes and foreseeable misuse considered?
• Are risks to data integrity, privacy, and system reliability assessed?
• Is the potential impact on GxP decisions evaluated?
• Are high-risk applications subject to appropriate additional controls?
• Are mitigation measures documented and approved?
• Are risks reassessed following significant changes or incidents?

What auditors should verify: Review risk assessment reports, approval records, mitigation plans, and evidence that identified controls have been implemented. Confirm that risk classifications are supported by documented reasoning rather than assigned arbitrarily.

5. Change Control and AI Model Updates

AI systems may change through software releases, model updates, revised training data, configuration changes, or changes to their intended use. These changes can affect system performance and reliability.

The audit should establish whether:

• AI systems are covered by an appropriate change control process.
• Proposed changes are assessed before implementation.
• Model and software versions are recorded.
• Updates undergo suitable testing and approval.
• The potential impact on validated or qualified states is evaluated.
• Significant changes trigger risk reassessment where necessary.
• Rollback or recovery arrangements are defined when appropriate.
• Emergency changes are documented and reviewed.

What auditors should verify: Trace a sample change from its initial request through impact assessment, testing, approval, implementation, and post-implementation review. Check whether the process captures changes made by external AI providers as well as internal teams.

6. Human Oversight and AI Output Review

Human oversight is particularly important when AI outputs influence regulated decisions. Personnel should understand the system’s limitations and know when an output requires additional investigation.

During the audit, assess whether:

• Human review requirements are documented.
• Personnel can challenge or override AI recommendations where appropriate.
• Critical decisions receive the required level of review.
• Overrides and interventions are recorded when necessary.
• Users understand the system’s intended use and limitations.
• Procedures explain how to handle unexpected outputs.
• Escalation arrangements exist for potentially incorrect results.
• Appropriate training is provided to system users and reviewers.

What auditors should verify: Review relevant procedures, training records, sample decision records, and documented exceptions. Determine whether human oversight is meaningful or whether employees routinely accept AI outputs without appropriate evaluation.

7. AI Vendor and Supplier Management

Third-party AI tools can introduce additional risks because organizations may have limited visibility into model development, data handling, software updates, and supplier controls.

The audit should assess whether:

• AI suppliers undergo documented qualification or assessment.
• Supplier risks are evaluated according to intended use.
• Responsibilities between the organization and supplier are defined.
• Contracts or quality agreements address relevant quality obligations where appropriate.
• Suppliers provide sufficient technical and compliance documentation.
• Data security and confidentiality requirements are addressed.
• Supplier changes are communicated and assessed.
• Ongoing performance monitoring is established.
• Supplier audits are conducted when justified by risk and applicable requirements.

What auditors should verify: Examine supplier assessments, contracts, service descriptions, security documentation, change notifications, and available audit evidence. Confirm that the organization understands which controls remain its responsibility even when an AI service is outsourced.

Additional Areas to Include in an AI Governance Audit

Although the seven checklist areas provide a strong foundation, pharmaceutical organizations should also review supporting controls that affect AI reliability and compliance.

Data Integrity and Documentation

Review how AI-related data is collected, processed, stored, and retained. Where relevant, confirm that records are attributable, legible, contemporaneous, original or appropriately verified, and accurate. Also evaluate access controls, audit trails, record retention, and traceability of important decisions.

The applicable data integrity controls should reflect the system’s intended use and the relevant GxP requirements.

Performance Monitoring and Incident Management

AI governance should continue after deployment. Organizations need procedures for monitoring performance, identifying unexpected behavior, documenting incidents, and escalating significant concerns.

Check whether performance indicators, review frequencies, acceptance criteria, and escalation thresholds are defined where appropriate. Verify that incidents and recurring issues are investigated and that corrective and preventive actions (CAPA) are implemented when required.

Documentation and Audit Trail Review

AI governance decisions should be supported by suitable evidence. Depending on the application, this may include requirements, risk assessments, testing records, approvals, model versions, monitoring reports, and incident investigations.

Auditors should confirm that records are controlled, accessible to authorized personnel, and sufficiently detailed to reconstruct important decisions.

Common Findings During AI Governance Audits

Organizations may encounter several recurring weaknesses when introducing AI into regulated processes.

• Missing governance procedures: AI tools are adopted without a clear approval and oversight framework.
• Incomplete inventories: Departments use AI applications that are not recorded centrally.
• Weak risk assessments: Assessments do not adequately consider the intended use or impact on GxP activities.
• Uncontrolled updates: Model or software changes are introduced without appropriate impact assessment.
• Insufficient human oversight: Personnel rely on AI outputs without following defined review procedures.
• Limited supplier oversight: Organizations lack adequate information about third-party controls or change management.
• Poor documentation: Decisions, exceptions, approvals, and corrective actions cannot be reconstructed reliably.
• Inadequate monitoring: There is no clear process for detecting performance issues after deployment.

These findings should be evaluated in context. Their significance depends on the application, applicable requirements, and potential impact. Organizations should document each finding, assess its risk, assign responsibility, and establish appropriate corrective actions.

How GxP Cellators Supports AI Governance Audits

GxP Cellators supports pharmaceutical and life sciences organizations with auditing and compliance services designed to help identify weaknesses and strengthen governance practices.

Its AI governance audit support can include:

• AI governance framework assessments.
• Reviews of roles, responsibilities, and oversight arrangements.
• AI system inventory and classification reviews.
• AI risk assessment reviews.
• Change control and model update reviews.
• Human oversight process assessments.
• AI vendor and supplier audit support.
• Mock AI audits and inspection-readiness assessments.
• Documentation and data integrity reviews.
• CAPA verification and follow-up.

The scope of each engagement should be tailored to the organization’s AI applications, regulatory environment, and identified risks.

Why Choose GxP Cellators?

GxP Cellators focuses on practical audit support for regulated organizations. Its approach emphasizes identifying control weaknesses, reviewing available evidence, and providing actionable recommendations.

Organizations can discuss their requirements, including relevant FDA, EMA, Health Canada, MHRA, or WHO expectations, depending on their operations and applicable regulatory obligations.

The goal is to help companies understand their current governance position, prioritize remediation activities, and improve oversight of AI systems used in regulated processes.

To discuss your requirements, visit the GxP Cellators Contact Page.

Frequently Asked Questions

1. What should be included in an AI governance audit checklist?

An AI governance audit checklist should cover the governance framework, roles and responsibilities, AI system inventory, risk assessment, change control, human oversight, vendor management, data integrity, documentation, and ongoing performance monitoring. The checklist should be tailored to the intended use and risk of each AI system.

2. Why is AI governance important for pharmaceutical companies?

AI governance helps pharmaceutical companies maintain oversight of AI applications and manage risks involving product quality, patient safety, data integrity, and regulatory compliance. It establishes clear responsibilities, documented controls, and processes for monitoring and managing AI systems throughout their lifecycle.

3. Who should be part of an AI oversight committee?

Depending on the organization’s size and AI applications, an oversight committee may include representatives from quality assurance, regulatory affairs, IT, data science, information security, operations, and relevant business functions. Members should have suitable expertise, clearly defined responsibilities, and appropriate decision-making authority.

4. How often should an AI governance audit be conducted?

The audit frequency should be based on risk, applicable requirements, changes to the AI environment, and the organization’s quality system. Higher-risk applications may require more frequent review. Significant incidents, supplier changes, model updates, or changes in intended use may also trigger additional assessments.

5. What is the difference between AI governance and AI validation?

AI governance defines the policies, responsibilities, oversight, and controls used to manage AI systems. AI validation or an appropriate assurance approach evaluates whether a system meets its intended requirements and is suitable for its intended use. Both activities can be important in regulated environments, but they serve different purposes.

6. How does GxP Cellators support AI governance audits?

GxP Cellators offers AI governance audit support that may include framework assessments, risk assessment reviews, inventory reviews, change control assessments, vendor audit support, mock audits, and CAPA follow-up. The engagement scope can be tailored to the organization’s requirements.

7. How can a pharmaceutical company prepare for an AI governance audit?

Start by identifying relevant AI systems and their intended uses. Gather governance policies, risk assessments, system inventories, change records, supplier documentation, training records, and monitoring evidence. Assign responsible personnel, review open CAPAs, and address significant gaps before the audit.

Contact GxP Cellators for AI Governance Audit Support

A structured AI governance audit checklist can help pharmaceutical companies identify weaknesses before they develop into larger quality or compliance concerns. By reviewing governance responsibilities, system risks, change control, human oversight, and supplier management, organizations can establish a more controlled approach to AI adoption.

GxP Cellators provides specialized auditing support to help organizations assess AI governance practices, identify potential gaps, and plan appropriate improvements.

Contact GxP Cellators: https://www.gxpcellators.com/contact/