The rapid evolution of Generative AI (GenAI) has the potential to accelerate drug discovery, improve patient engagement, streamline operations, and drive significant advancements in personalized medicine.
This white paper presents a comprehensive approach to developing and governing GenAI applications in life sciences, and emphasizes the role of citizen developers in cultivating agility and accelerating innovation — without sacrificing patient safety, data integrity, or compliance.
What’s Inside This White Paper
- Why GenAI needs governance. How AI governance aligns GenAI initiatives with organizational objectives, ethical standards, and regulatory requirements through the right policies, procedures, and controls.
- The rise of citizen development. How citizen developers democratize technology by building applications for specific business needs — and why that expanded capability requires a robust governance model.
- Best practices and guardrails. A practical set of AI best practices (ethical use, data governance, regulatory compliance, validation and testing) and guardrails (decision-making limits, privacy and security, bias detection, emergency-stop protocols).
- Quality-managed AI. How AI governance extends Quality Management System (QMS) principles to GenAI so initiatives run in a controlled, quality-focused environment.
- From proof of concept to solution. How application lifecycle management (ALM) and validation lifecycle management (VLM) maintain GxP controls across the software development lifecycle (SDLC).
The Role of Citizen Development and the Need for AI Governance
Citizen developers mark a significant shift toward democratizing technology. When they create applications and solutions that address specific business needs, they increase the adoption and impact of GenAI.
Because citizen development extends capabilities beyond traditional software development teams, it requires a robust governance model to help accelerate digital transformation and innovation without sacrificing compliance.
AI governance aligns GenAI initiatives with organizational objectives, ethical standards, and regulatory requirements. It encompasses the policies, procedures, and controls necessary to guide the development, deployment, and continuous monitoring of AI technologies and uphold the highest standards of patient safety, data integrity, and compliance. Establishing this kind of AI governance and compliance framework is what lets regulated organizations move quickly with confidence.
Best Practices for AI Governance and Citizen Development
AI governance extends the principles of Quality Management Systems (QMSs) to the challenges and opportunities presented by GenAI and ensures that your initiatives are executed in a controlled and quality-focused environment.
AI best practices and guardrails that ensure GenAI technologies are developed, deployed, and used responsibly include:
Best practices
- Ethical AI use
- Data governance
- Regulatory compliance
- Built-in compliance
- Continuous learning and adaptation
- Cross-functional governance
- Transparent documentation and reporting
- Stakeholder engagement and training
- Validation and testing protocols
Guardrails
- AI decision-making limitations
- Data privacy and security measures
- Bias detection and mitigation
- Emergency stop and intervention protocols
The USDM Point of View
In regulated life sciences, the question is never whether to govern GenAI — it’s how to govern it without smothering the innovation that makes citizen development valuable in the first place. USDM’s position is that governance and speed are complementary, not opposing forces: extend your existing QMS discipline to AI, build compliance in rather than bolting it on, and give citizen developers clear guardrails so they can move fast safely. The same rigor you apply to computer software assurance (CSA) and 21 CFR Part 11 belongs around your GenAI initiatives so that patient safety, data integrity, and audit-readiness are never an afterthought.
Citizen Development, Proofs of Concept, and Lifecycle Management
While building innovative AI solutions, it’s important to maintain control over development activities in the software development lifecycle (SDLC), including iterative releases in your proof of concept and in the development and deployment of your solution.
Application lifecycle management (ALM) and validation lifecycle management (VLM) combine people, processes, and technology to oversee the initial planning and development of a software application and ensure that you maintain proper GxP controls throughout — so a fast-moving proof of concept can mature into a validated, production-ready solution.
Frequently Asked Questions: AI Governance and Citizen Development
What is AI governance in life sciences?
AI governance is the set of policies, procedures, and controls that align GenAI initiatives with organizational objectives, ethical standards, and regulatory requirements. It guides the development, deployment, and continuous monitoring of AI technologies so that patient safety, data integrity, and compliance are upheld throughout the lifecycle.
What is a citizen developer, and why do they need governance?
Citizen developers are non-traditional builders who create applications and solutions to address specific business needs, increasing the adoption and impact of GenAI. Because they extend capabilities beyond traditional software development teams, they need a robust governance model that accelerates innovation without sacrificing compliance.
How does AI governance relate to a Quality Management System (QMS)?
AI governance extends the principles of Quality Management Systems to the challenges and opportunities of GenAI. It ensures initiatives are executed in a controlled, quality-focused environment, applying the same discipline organizations already use for validated systems to AI development and deployment.
What guardrails should be in place for GenAI?
Recommended guardrails include AI decision-making limitations, data privacy and security measures, bias detection and mitigation, and emergency-stop and intervention protocols — paired with best practices such as ethical AI use, data governance, regulatory compliance, validation and testing protocols, and transparent documentation.
How do you move a GenAI proof of concept into a validated solution?
By maintaining control over development across the software development lifecycle (SDLC). Application lifecycle management (ALM) and validation lifecycle management (VLM) combine people, processes, and technology to oversee planning and development while preserving the GxP controls needed to take a proof of concept to a production-ready solution. Pairing this with continuous compliance keeps validated AI systems in a state of control over time.
Download the White Paper
Get the full white paper, AI Governance and Citizen Development for GenAI in Life Sciences, for the complete approach to governing GenAI, enabling citizen developers, and applying ALM/VLM controls in a GxP environment. Ready to put it into practice? Contact USDM to implement AI governance and best practices and move from proofs of concept to innovative AI solutions.
Contributors to this white paper:
John Petrakis, Chief AI Officer
Michelle Gardner, Senior Researcher and Writer
Vishal Sharma, VP of Digital Trust and Transformation