AI Act Compliance in Pharma: Data Governance, Collaboration, and Strategic Advantage

AI Act: Data Governance and Compliance Strategy Implications in Pharma

The AI Act introduces a higher compliance threshold for pharmaceutical companies using AI within the EU, especially for high-risk applications. It forces companies to rethink data governance, emphasize traceability and bias reduction, align compliance efforts across existing regulations, and adopt new technologies for proactive adherence. This is both a challenge and an opportunity to build trust and encourage innovation.

The regulation sets a clear risk-based framework, categorizing AI systems by their potential impact on health, safety, and fundamental rights. AI systems integrated as safety components in products regulated under EU laws like the Medical Devices Regulation (MDR) and the In Vitro Diagnostic Medical Devices Regulation (IVDR) are deemed high-risk.

Pharmaceutical AI applications—such as diagnostic algorithms, patient monitoring solutions, and clinical decision-support systems—fall directly within these high-risk categories. This classification triggers strict requirements on data governance, algorithm transparency, human oversight, accountability, and lifecycle management throughout AI development and deployment.

Compliance Realities and Industry Responses

Pharma companies face uncertainty around compliance implementation and expect clearer guidance from regulators. Smaller companies have adapted quickly, integrating compliance into AI projects efficiently. Larger firms, often slowed by legacy governance, show a more cautious approach, leading to mixed results with AI tools like diagnostic algorithms and clinical trial platforms.

French authorities, notably CNIL in collaboration with the Haute Autorité de Santé (HAS), have announced upcoming guidance specifically addressing AI deployment in healthcare. This aims to clarify how AI Act requirements align with GDPR, providing practical direction for pharmaceutical firms.

Data Sharing and Collaboration

Pharmaceutical AI depends on accessing large, diverse datasets from multiple countries. Cross-border data flows must comply with both GDPR and the AI Act, which impose overlapping and sometimes differing obligations. Success requires clear, well-documented protocols covering anonymization, consent, role assignment, transparency, and ongoing compliance, balanced with the need for innovation speed.

Anonymisation versus Technical Utility

Only irreversibly anonymized data fall outside GDPR. Pseudonymized data remain regulated due to re-identification risks. AI developers need detailed metadata to check for bias and validate models. Protocols must balance privacy and utility, document residual risks, and schedule regular reviews as linkage methods evolve.

Consent and Secondary Use

GDPR requires informed, specific, and revocable consent. AI projects often anticipate secondary or future uses; broad scientific consent remains disputed across member states. The AI Act adds transparency obligations, ensuring data subjects know when AI processes their data and generates outputs. Effective frameworks combine detailed patient notices, flexible consent terms, and live opt-out options throughout the AI lifecycle.

Documentation, Roles and Legal Bases

Both GDPR and the AI Act demand comprehensive records of data provenance, processing flows, and algorithmic logic. When data moves across borders, contracts must clearly define roles, notification duties, and jurisdictional issues. Additional legal bases are often necessary to govern reuse and sharing of AI outputs and real-world feedback, balancing scientific goals with patient rights.

AI Regulation Impact on M&A Activities

Compliance with GDPR and the AI Act now plays a critical role in due diligence for pharmaceutical mergers and acquisitions, especially in late-stage deals. Due diligence focuses on datasets’ origins, consent validity, data governance maturity, transparency, and intellectual property. Documenting human inventorship in AI innovations has become crucial, linking IP considerations closely to data governance. Expert external counsel is often needed to address compliance and IP risks effectively.

Infrastructure Investment in AI Development and Usage

Pharmaceutical companies are shifting from general IT setups to specialized “AI factories.” These environments are optimized for the computational, regulatory, and operational demands of pharmaceutical AI. Equipped with GPU clusters, scalable storage, and advanced networking, AI factories accelerate model training and inference.

They enable continuous generation of outputs like diagnostic analytics, digital biomarkers, and patient stratification models essential for R&D, manufacturing, and clinical operations. Designed to meet strict regulatory and security requirements, these facilities ensure secure data segregation, tailored access controls, and audit readiness. They also support compliant validation aligned with regulations when AI impacts clinical or therapeutic decisions.

Governance Challenges within Pharmaceutical Companies

AI affects multiple areas within pharma companies—IT, strategy, compliance, ESG, liability, and long-term planning. Often, decision-making authority and accountability are unclear, complicating validation and risk management. A structured three-tier governance model is advisable:

• An AI standing committee with cross-functional experts managing operational AI issues and reporting to senior leadership.
• A strategic executive committee, including roles like Chief AI Officer and Chief Legal Officer, approving major AI projects and managing risks.
• Board-level oversight delivering structured reporting and enforcing accountability for AI risks.

This approach clarifies responsibilities and improves management of operational and legal AI risks.

Regulatory Coherence as Strategic Advantage

The alignment between the AI Act, GDPR, and other regulations offers European pharmaceutical innovators clear compliance pathways, reducing legal fragmentation and operational complexity. Europe’s stringent regulatory environment enhances credibility and trust in pharmaceutical AI solutions worldwide.

Although criticized for rigidity, these regulations support ethical standards that are essential for patient and public trust—key for long-term commercial success.

Strategic Recommendations

• Adopt a three-tier governance model: operational AI committee, strategic executive committee, and board oversight.
• Embed privacy by design in data flows using strong anonymization, dynamic consent management, and transparent patient communication.
• Ensure data quality with documented provenance, bias testing, and periodic risk reassessment.
• Align IP and data strategies by recording human inventorship, clarifying ownership of training data and model outputs, and updating contracts.
• Define liability clearly in internal policies and vendor agreements, supported by explainable AI techniques that enable auditing of decision logic.
• Invest in secure, high-performance infrastructure for compliant model training, validation, and monitoring across jurisdictions.
• Maintain exhaustive documentation of data sources, processing steps, and algorithm performance to meet transparency requirements and simplify due diligence.

Pharmaceutical executives focusing on strategy will find these actionable steps crucial to meet regulatory demands while preserving innovation momentum.