AI adoption in pharma is moving beyond experimentation. As AI becomes embedded in regulatory workflows, organizations need to answer a different set of questions: Who can use it? For which tasks? What needs human review? How are decisions documented? And who is ultimately accountable?

These questions sit at the heart of AI governance in pharma.

Effective governance is not simply about controlling AI use. It provides the policies, responsibilities and evidence needed to use AI consistently within a regulated environment.

What does AI governance mean in pharma?

AI governance is the framework an organization uses to control how AI systems are selected, implemented, used, monitored and changed.

In regulatory affairs, this becomes particularly important because AI may interact with sensitive data or contribute to documents and workflows that ultimately support regulatory decision-making.

A governance framework establishes boundaries around that use. It defines what AI can be used for, who is authorized to use it, what level of human oversight is required and how activity is documented.

This is different from validating individual AI-generated outputs. Governance operates at a broader level: it defines the rules under which AI is allowed to operate.

Why AI governance goes beyond model performance

Accuracy matters, but an accurate output does not automatically mean an AI-enabled process is appropriately governed.

Organizations also need to understand how that output was produced and what happened around it.

For example:

• Who initiated the task?
• Which source information was used?
• Which AI system or workflow performed the task?
• Was the output modified?
• Who reviewed it?
• Who approved the final result?

This context becomes increasingly important as AI systems move from isolated tools to integrated components of regulatory workflows.

The core components of an AI governance framework

There is no single governance model that fits every pharmaceutical organization. However, several components are fundamental.

1. AI policies and acceptable use. Organizations should define approved AI tools and use cases, restrictions on sensitive or confidential information, prohibited uses and situations requiring additional review.
2. Roles and responsibilities. Ownership should be explicit. Regulatory, Quality, IT, Security and system owners may have different responsibilities, but each AI-enabled process should have clearly defined accountability.
3. Risk-based controls. Not every AI use case carries the same risk. Using AI to summarize internal information is different from using it to contribute to regulated documentation. Controls and review requirements should reflect that difference.
4. Access controls. Organizations should determine who can access AI systems, data and specific functionality according to their responsibilities.
5. Human oversight. Governance should establish when human review is required and who has authority to approve or reject AI-assisted work.
6. Audit trails and recordkeeping. Relevant actions should generate sufficient evidence to reconstruct what happened.
7. Change management. Changes to models, configurations, integrations or workflows may affect system behavior and should be assessed accordingly.
8. Incident and exception management. Organizations also need procedures for situations where AI behaves unexpectedly or established processes are not followed.

What should an AI policy cover?

An AI policy should translate governance principles into practical rules employees can follow.

At minimum, organizations should consider defining:

• Approved AI systems and use cases
• Prohibited or restricted uses
• Rules for confidential and regulated data
• Required levels of human review
• Access and authorization requirements
• Documentation and recordkeeping expectations
• Responsibilities for reviewing and approving AI-assisted work
• Procedures for incidents, exceptions and significant changes

The objective is not to create policy for its own sake. It is to remove ambiguity around how AI can be used in day-to-day operations.

Audit trails: creating evidence, not just logs

Logging activity and creating a meaningful audit trail are not necessarily the same thing.

For regulated workflows, organizations should consider whether available records allow them to reconstruct the relevant sequence of events.

A useful audit trail may connect:

User → source → AI action → output → modification → review → approval

This provides context around an AI-assisted process rather than simply recording that an action occurred.

Platforms such as Narrativa® Navigator can support this approach through capabilities including audit trails, role-based access, version management and human review workflows. These controls help organizations maintain visibility as AI becomes part of regulated processes.

Who is accountable for AI-generated regulatory content?

AI can perform increasingly sophisticated tasks, but accountability still needs to sit with clearly identified people and functions.

An AI system might generate, analyze, compare or review content. It cannot assume organizational responsibility for the final regulatory decision.

Governance should therefore distinguish between task execution and accountability.

For example, a system owner may be responsible for the technology, Quality for governance requirements, and regulatory or medical writing teams for reviewing and approving content within their respective processes.

The precise allocation will vary between organizations. What matters is that responsibility is defined rather than assumed.

Governance across the AI lifecycle

Governance should not begin when users start interacting with an AI system.

It should extend across the lifecycle:

Selection → implementation → controlled use → monitoring → change management → retirement

Organizations should consider governance requirements when evaluating an AI system, determine appropriate controls before deployment and continue monitoring the system as technology and workflows change.

This becomes particularly relevant with AI because systems, models and integrations can evolve more frequently than traditional software environments.

A practical AI governance checklist for regulatory teams

Governance area	Key question
Policy	Are approved and prohibited AI uses documented?
Ownership	Is there a defined owner for each AI-enabled process?
Risk	Are controls proportionate to the intended use?
Access	Can only authorized users access relevant systems and data?
Auditability	Can relevant actions and decisions be reconstructed?
Human oversight	Is human review defined at the appropriate stages?
Change control	Are significant changes assessed before implementation?
Accountability	Is responsibility for final decisions explicit?

From AI experimentation to governed AI operations

As AI becomes more deeply integrated into regulatory affairs, governance provides the structure needed to move beyond isolated experimentation.

Policies establish boundaries. Access controls determine who can do what. Audit trails provide evidence. Human oversight introduces defined review points. Clear ownership ensures accountability does not disappear simply because part of a process has been automated.

The objective is not to eliminate every risk associated with AI. It is to create a framework in which AI can be used consistently, transparently and accountably within regulated workflows.

Frequently asked questions about AI governance in pharma

What is AI governance in pharma?

AI governance in pharma is the framework of policies, responsibilities and controls that determines how AI systems are selected, implemented, used, monitored and changed. It helps organizations establish appropriate oversight and accountability when AI is used in regulated workflows.

Why is AI governance important in regulatory affairs?

AI governance helps regulatory teams define how AI can be used, who is responsible for its use, what requires human review and what evidence should be retained. This becomes increasingly important as AI moves from isolated experimentation into operational regulatory processes.

What should an AI governance framework include?

An AI governance framework should typically address acceptable use, roles and responsibilities, risk-based controls, access management, human oversight, audit trails, recordkeeping, change management and procedures for handling incidents and exceptions.

What is the role of audit trails in AI governance?

Audit trails provide evidence of relevant actions throughout an AI-assisted workflow. Depending on the process, they can help establish who performed an action, what sources were used, what the AI produced, what was changed and who reviewed or approved the final output.

Who is accountable for AI-generated regulatory content?

Accountability should remain with clearly defined people and functions within the organization. AI may generate, analyze or review content, but organizations should establish who is responsible for reviewing the output and making or approving final regulatory decisions.

Is AI governance the same as AI validation?

No. AI governance establishes the broader policies, responsibilities and controls for how AI is used within an organization. Validation focuses on demonstrating that a system or process performs as intended for its defined use. The two are related, but they address different aspects of responsible AI use.