Webinar How to Use AI in Drug Development Without Creating Compliance Risk
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
WEBINAR

Using AI in Drug Development Without Creating Compliance Risk

A practical framework for faster work, lower exposure, and audit-ready adoption

April 21, 2026
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

This webinar follows a risk-first path from use case to pilot

  1. 1
    Where AI fits safely in drug development workflows
  2. 2
    How compliance risk rises with GxP impact and sensitive data
  3. 3
    Which use cases are worth piloting first, and which are not
  4. 4
    What failure modes create avoidable privacy, IP, and audit issues
  5. 5
    Which guardrails, evidence, and pilot steps survive review
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Table 1Lower-risk and higher-risk AI task patterns in drug development
Task patternTypical useRelative risk
Drafting supportFirst-pass summaries, email draftsLower
Search and retrievalSOP lookup, template findingLower
Quality checksMissing fields, inconsistency flagsLower
Interpretive analysisSignal meaning, root-cause claimsHigher
Regulated decisionsEligibility, release, case assessmentHighest

Risk level depends on data sensitivity, GxP impact, tool controls, and human oversight.

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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Figure 1Bounded AI support with human accountability
flowchart TD
 A[Source documents and approved data] --> B[AI assists with draft, summary, search, or check]
 B --> C[Human reviews against source]
 C --> D{Accurate and appropriate?}
 D -->|Yes| E[Use in working draft or next step]
 D -->|No| F[Revise, reject, or escalate]
 E --> G[Document tool, review, and version]
 F --> G
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

GxP impact is the first risk question, not the last

Start by asking whether the task can affect regulated records, product quality, patient safety, or submission content. If the answer is yes, the bar for controls, review, and evidence goes up fast.

  • Non-GxP support tasks usually carry lower inherent compliance risk
  • GxP-adjacent drafting can be workable when humans verify against source
  • Autonomous decisions in regulated workflows are the wrong place to start
  • The same model can be low-risk in one task and high-risk in another
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Table 2Task type, data, and oversight shift the risk profile
Task exampleGxP impactData exposurePractical stance
SOP search over approved libraryLowInternal, controlledGood early pilot
CSR summary first draftMediumConfidential study dataUse with source review
Site notes in public chatbotHighPII and confidentialDo not allow
Safety narrative pre-sortMedium-HighSensitive case dataPilot with reviewers
Final decision on case codingHighSensitive, regulatedKeep human-owned

Illustrative examples based on common drug development workflows.

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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Figure 2A simple screen for regulated AI use
flowchart TD
 A[Define the task] --> B{Touches GxP record or decision?}
 B -->|No| C{Sensitive data involved?}
 B -->|Yes| D{Human can verify against source?}
 C -->|No| E[Lower-risk pilot candidate]
 C -->|Yes| F[Use approved tool and data controls]
 D -->|Yes| G[Add review, logging, and sign-off]
 D -->|No| H[Do not deploy this use case]
 F --> G
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

A realistic paraphrase of what QA and privacy leads often say: "The incident usually starts with good intent. Someone wants speed, pastes real data into the wrong tool, and now we have a preventable problem."

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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Human review is a control only when it is designed, not assumed

"Human in the loop" can mean anything from careful source verification to a quick glance at fluent text. Review reduces risk only when the reviewer knows what to check, has access to source material, and remains accountable.

  • Require reviewers to compare outputs to source, not just read for tone
  • Define what the AI may draft, and what humans must decide
  • Log prompts, versions, and reviewer sign-off for material outputs
  • Treat vendor opacity as a reason to strengthen internal checks
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Medical writing is a strong first pilot when AI drafts, humans decide

Medical writing has high document load and clear source material, which makes output review practical. The safe pattern is first-pass drafting or summarization, then writer and subject matter review before anything enters the official document set.

  • Use AI for outlines, section summaries, and consistency checks
  • Keep humans accountable for claims, tone, and final wording
  • Review every output against source tables and approved references
  • Block autonomous interpretation of efficacy or safety findings
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Table 3Early AI pilots in drug development, compared by reviewability and risk
Use caseWhy it fits firstMain control
CSR summary draftingSource tables enable line-by-line reviewMedical writing review
TMF document taggingMetadata can be checked quicklyQC sample review
PV narrative pre-sortHumans keep case assessmentQualified reviewer sign-off
Regulatory intelligence scanSources are public and citableCitation verification
SOP question answeringApproved corpus limits inventionRetrieval over controlled content

Illustrative examples, the right pilot still depends on data sensitivity and GxP impact.

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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Figure 3Document-intensive pilot with human accountability
flowchart TD
 A[Select bounded task] --> B[Use approved tool and data]
 B --> C[Generate draft or triage suggestion]
 C --> D[Check against source material]
 D --> E{Issues found?}
 E -->|Yes| F[Revise or discard output]
 E -->|No| G[Human approves next use]
 F --> D
 G --> H[Log decision and version]
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Clinical operations and TMF support work when the model helps organize, not decide

Operations teams drown in document handling, tracker updates, and repeated status questions. AI can reduce search and admin time, but it should not invent site actions, change essential document status on its own, or bypass TMF quality checks.

  • Draft follow-up emails and meeting notes from approved inputs
  • Suggest TMF tags, filing locations, and missing-document flags
  • Answer staff questions using approved SOPs and templates
  • Require staff to confirm metadata before records are finalized
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Realistic paraphrase: "Start where the work is repetitive, document-heavy, and easy to verify against a source. That is where AI can save time without asking QA to suspend disbelief." This is why PV triage support and regulatory intelligence reviews often beat more ambitious decision use cases.

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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Fluent outputs can be wrong in exactly the way busy teams miss

Hallucinations are not just bizarre mistakes. In regulated work, the more dangerous failure is a plausible sentence, citation, or summary that sounds right and slips through because it matches expectations.

  • Treat every AI draft as unverified until checked to source
  • Assume confidence in wording is not evidence of correctness
  • Watch for invented references, shifted numbers, and lost nuance
  • High-pressure timelines make polished errors easier to accept
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Table 4Common AI failure modes and practical controls
Failure modeWhat it looks likeMinimum control
Hallucinated contentInvented fact or citationCheck against source record
Prompt leakageSensitive text in wrong toolClassify data before use
Automation biasReviewer rubber-stamps outputRequire active verification
Missing audit trailNo version, prompt, reviewerLog prompts and approvals
Vendor opacityUnknown retention or training useReview contract and settings

Adapt controls to intended use, data sensitivity, and GxP impact.

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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Figure 4How a simple shortcut becomes a compliance incident
flowchart TD
 A[Time pressure hits] --> B[User picks fastest AI tool]
 B --> C[Sensitive data pasted into prompt]
 C --> D[Model returns fluent draft]
 D --> E[Reviewer skim-approves]
 E --> F[Output enters regulated workflow]
 F --> G[Error or exposure discovered later]
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

One skipped review step can undo hours of productivity gain

Automation bias is the habit of trusting a system because it is fast, polished, or usually helpful. In drug development, that turns human review into theater unless reviewers must verify facts, context, and source alignment.

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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Missing records turn a manageable AI use into an audit problem

Even when the output is acceptable, weak documentation creates a second failure. If you cannot show what tool was used, what data went in, who reviewed the result, and which version was approved, you cannot defend the process.

  • Capture tool name, version, date, and key settings
  • Retain prompt, source material, output, and reviewer identity
  • Track what changed between draft and approved version
  • Update SOPs when AI changes a defined workflow step
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Approved tools and clear access rules prevent avoidable incidents

Start with the tool, not the prompt. Teams need a short approved list, named owners, and access rules that match the work, otherwise convenience quietly becomes policy.

  • Publish approved AI tools by use case, owner, and support status
  • Restrict public chatbots for any work tied to regulated or sensitive data
  • Use role-based access, least privilege, and managed authentication
  • Define who can pilot, who can buy, and who can approve exceptions
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Table 5Data classes and allowed AI use
Data classPublic AIEnterprise AIRequired control
Public, non-confidentialAllowedAllowedBasic review
Internal, low sensitivityAvoidAllowedNamed business purpose
Confidential businessNoConditionalAccess control, logging
Personal data, PHI, PIINoRare, controlledDPA, approval, minimization
GxP record contentNoControlled onlyValidation, review, audit trail

Local policy should define examples for each data class and approval path.

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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Figure 5Simple pre-use decision path
flowchart TD
 A[Need help with a task] --> B{Approved tool available?}
 B -- No --> C[Stop and request review]
 B -- Yes --> D{Input data classified?}
 D -- No --> E[Classify data first]
 D -- Yes --> F{Sensitive or GxP content?}
 F -- Yes --> G[Use controlled workflow and sign-off]
 F -- No --> H[Use tool with human review and logging]
 G --> I[Document output and decisions]
 H --> I[Document output and decisions]
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Review checklists and sign-off keep humans accountable

A good review step is more than "someone looked at it." It names what must be checked, against which source, and who is accountable before any output enters a decision or record.

  • Check every material claim against source documents or approved systems
  • Flag missing citations, invented facts, and stale references before reuse
  • Require sign-off rules by risk, writer review for drafts, QA for controlled uses
  • Treat AI output as draft input, never as evidence by itself
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Four controls cover most first-wave AI governance needs

Most teams do not need a 40-page AI program to start safely. They need a few controls that are visible, repeatable, and boring enough that people will actually follow them.

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Validation effort should match intended use and impact

Treat AI validation like any other control, proportionate to what the tool does, where it sits in the workflow, and what could go wrong. A tool that drafts a summary for human review is not the same as a tool that influences a GxP decision or handles sensitive data.

  • Start with intended use, users, inputs, outputs, and downstream impact
  • Increase evidence when GxP impact, sensitivity, or autonomy increases
  • Reduce scope by keeping AI advisory, bounded, and reviewable
  • Separate vendor claims from your actual in-house use case
  • Reassess when prompts, models, data sources, or workflow steps change
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Vendor diligence should test operational reality, not just marketing

Left column: core diligence questions. Right column: why each answer matters.

  • Ask where data is processed, stored, retained, and deleted
  • Review change control, versioning, and notice for model updates
  • Confirm whether customer data trains shared models or stays isolated
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Audit readiness depends on a small, credible evidence package

You do not need a phone book of paperwork. You do need enough evidence to show intended use, risk assessment, testing, human review, and change control in a form that another function can follow without guesswork.

  • Keep a short intended-use statement and named business owner
  • Document risk rating, allowed data, users, and required review steps
  • Test against realistic examples, including edge cases and bad inputs
  • Save approval records, vendor docs, and version or model references
  • Define triggers for re-testing, such as workflow or model changes
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

Choose one pilot that is boring, bounded, and easy to check

The strongest first pilot is not the flashiest use case. It is a narrow workflow with clear inputs, a known reviewer, and outputs that can be checked against source material before anything reaches a regulated record.

  • Pick one document-heavy task, not a whole process
  • Favor high volume, repeatable work with stable inputs
  • Keep a human owner accountable for every output
  • Avoid pilots that need patient data or autonomous decisions
  • Use the biotech draft-summary example as the model pattern
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk

A pilot survives review when ownership and evidence are explicit

Stakeholders on the left, proof on the right

  • QA confirms GxP impact, review steps, and records
  • IT and Security approve tool access, logging, and retention
  • Legal reviews privacy, confidentiality, IP, and vendor terms
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WEBINARHow to Use AI in Drug Development Without Creating Compliance Risk
Thanks for watching

Next step: draft a one-page AI pilot brief this week

  • Name the workflow, owner, tool, and approved inputs
  • Define 2-3 success metrics, plus stop and escalate rules
  • Book a 30-minute review with QA, Legal, and IT
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