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AI Governance and Citizen Development for GenAI in Life Sciences

AI Governance and Citizen Development for GenAI in Life Sciences

The rapid evolution of Generative AI (GenAI) technologies has the potential to accelerate drug discovery, improve patient engagement, streamline operations, and drive significant advancements in personalized medicine.

Discover a comprehensive approach to developing and governing GenAI applications in life sciences and emphasize the role of citizen developers in cultivating agility and accelerating innovation.

Learn how to plan for AI governance and citizen development—download the white paper > > >

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.

Get the white paper to see how AI governance and your QMS
ensure that GenAI applications are safe and compliant > > >

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


  • AI decision-making limitations
  • Data privacy and security measures
  • Bias detection and mitigation
  • Emergency stop and intervention protocols

Download the white paper for details > > >

Citizen Development, Proofs of Concept, and ProcessX

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) in ProcessX combine people, processes, and technology to oversee the initial planning and development of a software application and ensure that you maintain proper GxP controls.

Contact us 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

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