Quick answer
There is no general label of GxP-compliant AI. Compliance and validation apply to the implemented system, intended use, records, controls and operating procedures. Start by defining what the AI may advise, what it may never approve, which records are authoritative and how errors are detected. Build traceability, access, change control and human approval into the workflow, then generate evidence proportionate to risk.
Key takeaways
- Define intended use and prohibited use before choosing the model.
- Part 11 questions depend on whether electronic records and signatures are created, modified, maintained or transmitted.
- Source citations help review but do not prove that an answer is correct.
- A quality owner retains authority for deviations, CAPA and release decisions.
Do not call a model GxP compliant
A model is one component. The validated state belongs to the configured system and process: intended use, data, prompts or retrieval, interfaces, access, records, review, change control and procedures. A public model name does not establish fitness for a regulated use.
Define intended use and prohibited use
An intended-use statement should name the user, workflow, input, output and decision boundary. For example, an assistant may retrieve approved passages for an investigator but may not determine root cause, approve a CAPA or release a batch. Prohibited uses should be tested, not merely written in policy.
Map the authoritative record
Identify the source of truth for document status, version, permissions and controlled updates. A retrieval assistant can cite an obsolete or unauthorized record if metadata and access rules are incomplete. The reviewer must be able to open the source and understand why it was returned.
Apply risk-based assurance
The FDA's Computer Software Assurance guidance describes a risk-based approach for production and quality-system software. For an AI workflow, assurance evidence can include requirements, risk assessment, representative tests, expected and unexpected cases, access tests, audit evidence, change control, monitoring and approved procedures. The depth should match the risk of the intended use.
Understand the Part 11 boundary
FDA Part 11 guidance addresses electronic records and electronic signatures. Determine whether the AI application creates, modifies, maintains, archives, retrieves or transmits regulated electronic records, and which system remains authoritative. Logging every model token does not by itself create a compliant audit trail; the record, meaning, access and review process matter.
Evaluate retrieval and generation separately
Test whether the correct controlled evidence is retrieved before judging the final answer. Record performance by document type, version, permission and question class. Then evaluate whether the answer is supported by the retrieved passages, whether uncertainty is visible and whether escalation works.
Control change after approval
Models, prompts, retrieval rules, documents, interfaces and infrastructure can all change behaviour. Define which changes require assessment, regression testing, approval and rollback. A vendor's silent model update may be unacceptable for a validated boundary.
Preserve human authority
AI can organize evidence and draft language. Qualified people remain responsible for investigation conclusions, CAPA decisions, batch disposition, quality approval and regulatory commitments.
Decision table
| Option | Use when | Evidence needed | Watch for |
|---|---|---|---|
| Controlled retrieval assistant | Users need faster access to approved evidence. | Document status, permissions, retrieval tests and source links. | Obsolete records or answers that exceed the source. |
| Investigation drafting support | Qualified users need structured summaries or draft narratives. | Traceable inputs, review workflow and prohibited-use tests. | Draft language being accepted without evidence review. |
| Predictive quality signal | A bounded process decision has representative historical data. | Time-aligned data, held-out evaluation and action protocol. | Correlation being treated as root cause. |
| Autonomous quality decision | Only after a formal risk and validation case supports it. | System-level safety, regulatory and quality approval. | Replacing accountable quality authority with a model score. |
Frequently asked questions
Is a RAG assistant automatically GxP compliant because it cites sources?
No. Citations support review, but compliance and validation depend on the complete intended use, records, controls, testing, change process and operating procedures.
Can AI close a deviation or CAPA?
That decision requires the manufacturer's quality and regulatory assessment. NeoBram's default design keeps accountable quality personnel in approval and uses AI for retrieval, comparison, signal detection or drafting.
Can pharma data stay on-premises?
Yes, when the selected components and licences support it. The architecture must still cover identity, logs, backups, updates, model monitoring, document control and approved support access.
Limitations of this guide
- This guide is not regulatory, quality or legal advice.
- FDA guidance does not automatically determine requirements in India, the EU, the UK or other jurisdictions.
- Specific validation evidence depends on intended use, risk, records, interfaces and the manufacturer's quality system.
Primary sources used in this guide
- Part 11, Electronic Records; Electronic Signatures Scope and Application
U.S. Food and Drug Administration
Primary FDA guidance on the scope and application of Part 11.
- Computer Software Assurance for Production and Quality System Software
U.S. Food and Drug Administration
Primary FDA guidance for a risk-based assurance approach.
- AI Risk Management Framework
U.S. National Institute of Standards and Technology
Primary framework for governing, mapping, measuring and managing AI risk across the lifecycle.