AI is quickly becoming embedded in day-to-day work across organizations, and Quality teams are expected to keep that usage controlled, defensible, and audit-ready.
But making sure that AI use is compliant with the safety and regulatory standards required from life sciences organizations is a hefty task. Quality teams must quickly answer critical questions: Which AI tools are acceptable? What information can employees put into them? How should outputs be reviewed? What controls and documentation will stand up to scrutiny?
ZenQMS has already covered the first two steps in this journey: how to evaluate AI tools before adoption and how to validate AI tools. This post focuses on what comes next: the operating model.
Once an AI tool is approved (and validated when appropriate), QA leaders still need practical guardrails so teams can use AI compliantly across roles, workflows, and changing tool behavior.
Establish governance and require evaluation before use
A compliant AI program starts with intentional AI governance rather than ad hoc. In practice, that means teams need a documented method to determine whether a tool and its intended use are acceptable for work. Many organizations route this through an internal review process that includes Quality, IT, Security, and Privacy stakeholders.
This evaluation step is especially important for AI because risk is driven not only by the tool itself, but also by what users enter into it and how the tool stores or processes that information.
A clear governance model should:
- Define ownership
- List criteria for approval
- Make it easy for employees to verify whether a tool is approved
A risk-based approach keeps this manageable. Low-risk use cases can be evaluated quickly and approved with minimal overhead, while higher-impact scenarios require deeper scrutiny and more explicit controls.
The goal is consistency. Every AI tool used must have a known status, documented rationale, and defined boundaries for use.
Protect sensitive data with clear rules and technical controls
Data protection is the fastest way AI usage becomes a compliance concern.
QA leaders can reduce risk by defining explicit rules for what information can and cannot be entered into AI tools, aligned with existing security and privacy expectations.
Common restricted categories include:
- Patient data and ePHI
- Client or customer confidential information
- Non-public quality records
- Proprietary or trade secret material
These rules need to be written in plain language and reinforced through training. Many missteps come from routine copy/paste behavior or uncertainty around what “counts” as sensitive…which is why it’s good practice to back the policy with technical controls.
Depending on the tool, this can include configuring retention and deletion, restricting access through role-based permissions, disabling settings that allow content to be used for model improvement, and enabling administrative visibility for higher-risk use cases.
Retention deserves special attention. AI interactions can create a new class of stored information such as:
- Prompt history
- Uploaded files
- Generated drafts
- Searchable logs
When this content is retained indefinitely by default, exposure increases over time even when the original use case feels low risk. Align retention to internal policy – and enforce it through configuration where possible – to reduce long-term risk and support defensibility.
Define human review expectations for AI outputs in day-to-day work
AI outputs should be treated as draft material that requires review by qualified humans.
AI-generated content can include inaccuracies, omissions, and biases. Even when an output sounds plausible, it still needs a human check for correctness and appropriateness in context.
QA leaders can keep AI review practical by matching the level of review to the level of risk. For lower-impact uses, a quick check is usually enough: confirm the output is accurate, clear, and consistent with your internal terminology.
For higher-impact uses that could affect regulated documents or decisions, reviews should be more rigorous. That typically means checking the output against approved sources (like procedures or standards), confirming it stayed within the intended scope, and documenting the review when traceability is required.
Consistency also improves when teams use a short set of review prompts instead of adding heavy processes. A lightweight checklist can help reviewers confirm three basics:
- The output doesn’t include restricted information
- Key facts are correct (and supported by approved references when needed)
- The language aligns with current procedures
Finally, better inputs produce better outputs. Simple guidance on how to write prompts while excluding sensitive or restricted data reduces rework and improves reliability. It’s an easy training win that supports both compliance and everyday usability.
Scale SOPs, training, and oversight using a risk-based approach
Compliant AI use does not require a separate procedure for every tool feature or every low-risk use case. Documentation and training should scale with impact.
A solid baseline is an umbrella AI acceptable-use policy that applies across roles. From there, use role- or workflow-specific instructions when AI touches regulated work products, higher-impact decision support, or other activities that would draw auditor scrutiny. In those cases, integrate AI expectations into your existing procedures rather than creating standalone documents that drift out of date.
Even with strong governance and review expectations, AI usage can drift over time. Models update, tool behavior changes, and teams develop habits. A compliant operating model includes a clear way to surface concerns when teams notice repeated inaccuracies, biased outputs in relevant contexts, tool behavior that conflicts with intended use, or potential data handling issues.
Monitoring does not need to be complex. Define what to report, where to report it, and how it will be triaged. When trends are visible, Quality can decide whether the response is training reinforcement, updated work instructions, configuration changes, vendor engagement, or reassessment of the use case. This aligns with the core quality mindset: detect signals early, address root causes, and prevent recurrence.
Getting started with compliant AI use in life sciences quality management
For teams putting these ideas into practice, focus on a short list of controls that do the most work:
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Governance that defines approved tools and approved use cases
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Data handling rules supported by retention and configuration controls
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Human review expectations that scale with riskt
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Training plus monitoring to keep usage controlled over time.
When these elements are in place, AI can support quality teams without creating uncontrolled risk, and QA leaders can clearly demonstrate how AI use is managed within a regulated quality system.