Building AI Governance in Biotech : Why Policies, SOPs, and Guardrails Are Essential for Trustworthy Innovation

Sid Parulkar, M Pharm, CEO, PAR Clinical

Introduction

Artificial intelligence (AI) is rapidly transforming the biotech industry. What began as a set of experimental tools has evolved into a critical capability embedded across the drug development lifecycle—from discovery and clinical trials to regulatory submissions. However, as AI adoption accelerates, a deeper and more consequential realization has emerged across regulators and industry leaders alike:

The success of AI in biotech is not determined by algorithms alone, but by the governance frameworks that guide their use.

Across regulatory guidance, global policy frameworks, and industry research, there is growing consensus that AI must be deployed within structured governance systems supported by clear policies and robust standard operating procedures (SOPs) to ensure data integrity, privacy, and trust.

AI at the Core of Regulated Drug Development

AI has moved beyond supporting functions and is now integrated into regulated decision-making processes that directly influence patient safety and clinical outcomes. Regulatory agencies are responding to this shift by setting clearer expectations.

The U.S. FDA has introduced a risk-based framework for assessing AI credibility, requiring organizations to demonstrate that AI-generated outputs used in regulatory decision-making are reliable, validated, and appropriate for their intended purpose¹. Similarly, the European Medicines Agency (EMA) expects AI systems used in clinical trials to be transparent, controlled, and supported by detailed documentation, particularly when they affect trial data or results².

At a broader level, joint FDA–EMA principles reinforce the need for lifecycle governance, human oversight, and risk-based control of AI systems, ensuring that innovation does not compromise safety or compliance³.

In essence, AI is now part of the GxP-regulated environment, and its outputs must meet the same standards as any other clinical or scientific data.

A Growing Gap Between Adoption and Governance

Despite increasing regulatory clarity, industry data reveals a persistent gap between AI adoption and governance maturity.

A significant proportion of life sciences organizations—estimated at around 75%—are already deploying AI in their operations⁴. However, governance mechanisms have not kept pace. Only 17% of pharmaceutical organizations have implemented automated safeguards to prevent sensitive data leakage through AI tools⁵.

More concerning still, studies suggest that up to 90% of healthcare and life sciences organizations may have sensitive data exposed to systems that AI can access, often due to weak controls or unregulated tool usage⁶.

This imbalance has led to the widespread emergence of “shadow AI”, where employees use external or unapproved tools without organizational oversight, increasing the risk of data leakage, compliance violations, and operational disruption.

Data Privacy: The Most Immediate Risk

Biotech companies rely on highly sensitive datasets—patient records, genomic data, imaging files, and proprietary research. AI systems amplify the scale and complexity of data use, making privacy protection a critical challenge.

Healthcare consistently experiences the highest cost of data breaches, with an average breach exceeding $7.4 million, and AI-driven environments further increase exposure and attack surfaces⁷. In addition, reports indicate that a substantial portion of AI-related policy violations involve protected health information (PHI), often resulting from improper handling or use of unapproved tools⁸.

The global nature of clinical trials adds further complexity, particularly in managing cross-border data transfers and compliance with regulatory frameworks such as GDPR and HIPAA, which regulators now expect companies to address proactively⁹.

Without clear policies that define acceptable use, data access, and tool authorization, organizations risk turning AI into a vector for systemic privacy failures.

Data Integrity and Authenticity: Protecting Scientific Trust

Beyond privacy, the most critical risk in biotech is the potential compromise of scientific data integrity. Clinical trial data must be accurate, traceable, and reproducible—principles that can be challenged by AI if not governed properly.

AI systems may generate outputs that appear credible but are incorrect, lack explainability, or cannot be easily audited. In recognition of these risks, regulators require strong controls. The EMA emphasizes traceability from raw data through AI processing to final outputs, ensuring complete auditability¹⁰.

Additionally, regulators often expect AI models to be fixed (“frozen”) during critical stages of analysis to prevent uncontrolled changes that could affect results¹¹. Importantly, human oversight remains essential, with responsibility for final decisions resting with clinical and operational stakeholders rather than the AI system itself¹².

Regulatory research also highlights that accuracy, reliability, and data governance are among the highest priorities for AI in drug development, reflecting the importance of maintaining scientific rigor¹³.

Cybersecurity: Expanding the Threat Landscape

AI introduces new dimensions to cybersecurity risk. Its reliance on complex data pipelines, interconnected systems, and external vendors creates multiple points of vulnerability.

AI systems can be targeted through techniques such as data poisoning or adversarial manipulation, potentially altering outputs in ways that are difficult to detect¹⁴. At the same time, healthcare remains one of the most targeted industries for cyberattacks, with ransomware and data breaches occurring at a high frequency¹⁵.

Experts now emphasize that AI should be treated as enterprise-critical infrastructure, requiring governance frameworks comparable to those used for electronic health records or cloud systems—but enhanced to address AI-specific risks¹⁶.

This underscores the need to integrate cybersecurity directly into AI governance, rather than treating it as a separate concern.

Bias, Ethics, and the Need for Trust

AI systems are inherently shaped by the data on which they are trained. If that data is biased or incomplete, the resulting outputs may reinforce inequalities or produce misleading conclusions.

The OECD AI Principles provide a global framework for responsible AI, emphasizing fairness, transparency, robustness, and accountability as core requirements¹⁷. At the same time, OECD research highlights that fragmented governance structures and inconsistent policies remain major barriers to scaling AI effectively in healthcare¹⁸.

In a domain as sensitive as healthcare, trust is paramount. Governance frameworks ensure that AI systems are not only effective but also ethical and aligned with patient-centered values.

Building Structured AI Governance

To manage these risks, biotech companies must implement a comprehensive governance approach that combines strategic oversight, policy enforcement, and operational control.

This includes:

  • Defining clear governance structures with cross-functional accountability
  • Establishing policies that regulate AI use and protect sensitive data
  • Developing SOPs for model validation, monitoring, and lifecycle management
  • Implementing technical controls such as audit trails, access controls, and data lineage tracking
  • Ensuring human oversight remains central to all AI-driven decisions

A consistent principle across regulatory and industry guidance is clear: “AI does not transfer responsibility—human accountability remains fundamental”.¹²

Governance as an Enabler of Innovation

Far from being a constraint, governance is increasingly recognized as a critical enabler of scalable AI adoption. Organizations with robust governance frameworks are better positioned to:

  • Navigate regulatory requirements efficiently
  • Scale AI across clinical programs
  • Reduce cybersecurity and compliance risks
  • Build trust with regulators, partners, and patients

Conversely, insufficient governance can delay approvals, compromise data integrity, and expose organizations to significant operational and financial risks.

Conclusion

AI represents one of the most transformative forces in biotech. Its ability to accelerate innovation and improve patient outcomes is undeniable. However, this potential can only be realized if AI is implemented responsibly.

The evidence is clear:

  • AI adoption is accelerating rapidly
  • Data privacy and cybersecurity risks are increasing
  • Regulatory expectations are evolving
  • Governance gaps remain widespread

The future of AI in biotech depends not just on innovation—but on control.

Organizations that invest in governance, policies, and SOPs today will not only mitigate risk—they will define the next generation of safe, compliant, and trustworthy drug development.

References:

  1. U.S. Food and Drug Administration (FDA). Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. Draft Guidance, January 2025. [duanemorris.com]
  2. European Medicines Agency (EMA). Aligning AI Use in Clinical Trials with FDA and EMA Expectations. Clinical trial AI oversight, transparency, and data governance requirements. [forbes.com]
  3. European Medicines Agency (EMA) and U.S. Food and Drug Administration (FDA). Guiding Principles of Good AI Practice in Drug Development. Joint framework for lifecycle governance and responsible AI use, January 2026. [avancer.co]
  4. IntuitionLabs. AI Policies and Data Classification in Clinical Biotech. Industry adoption trends and governance gaps in life sciences organizations. [oecd.ai]
  5. Kiteworks / Pharmaceutical Online. AI Data Security: The 83% Compliance Gap Facing Pharmaceutical Companies. Industry survey of AI governance and data leakage risks. [varonis.com]
  6. Varonis. Healthcare & Life Sciences Data Security Report: AI Exposure Risks. Analysis of data exposure across healthcare organizations. [kpmg.com]
  7. IBM Security / Forbes. Cost of a Data Breach Report 2025: Healthcare Sector Analysis. Average breach costs and cybersecurity risk trends. [govinfosecurity.com]
  8. Netskope Threat Labs. Healthcare AI Risk and Data Privacy Report. AI-related policy violations and PHI exposure risks..pdf) [Contractin...n (Part 3) | PDF]
  9. European Medicines Agency (EMA). Reflection Paper on the Use of Artificial Intelligence in the Medicinal Product Lifecycle. Data governance, traceability, and cross-border requirements. [censinet.com]
  10. European Medicines Agency (EMA). Clinical Trial AI Guidance: Traceability and Data Lineage Expectations. Requirements for auditability and inspection readiness. [forbes.com]
  11. European Medicines Agency (EMA). Model Lifecycle Management and Validation Expectations in AI Systems. Requirements for model control, monitoring, and reproducibility. [forbes.com]
  12. Leibowitz, K. Contracting for AI in Clinical Trials: Cybersecurity, Monitoring, and Risk Allocation. Clinical Leader, May 2026. 10
  13. Regulatory Affairs Professionals Society (RAPS). EMA Research Priorities for AI in the Medicinal Lifecycle. Accuracy, reliability, and governance as top priorities. [forbes.com]
  14. Healthcare IT News / HSCC. AI Cybersecurity Governance Framework Implementation Guide. AI-specific risks including data poisoning and adversarial attacks. [healthtech...gazine.net]
  15. Forbes / FBI / AHA Data. Healthcare Cybersecurity Threat Landscape 2025–2026. Ransomware prevalence and sector vulnerability. [govinfosecurity.com]
  16. Health Sector Coordinating Council (HSCC). AI Governance Playbook for Healthcare. Enterprise-level AI risk management and governance recommendations. [healio.com]
  17. Organisation for Economic Co-operation and Development (OECD). OECD AI Principles: Framework for Trustworthy AI. Ethical and governance standards for AI systems. [linkedin.com]
  18. OECD. Scaling Artificial Intelligence in Health. Governance gaps, policy fragmentation, and barriers to AI adoption in healthcare. [aihealthca...liance.com]