Pharmaceutical Research Databases: Balancing AI Innovation with Regulatory Compliance

Abstract

Pharmaceutical research organizations face unique database challenges requiring specialized architectures that balance AI-driven innovation with regulatory compliance. This article examines the dual imperatives of maintaining immutable audit trails for regulatory submissions while supporting agile discovery workflows. Through exploration of temporal data modeling, attribute-based access control, computational workflow capture, and hybrid implementation strategies, the article provides architectural patterns and operational frameworks that enable pharmaceutical organizations to navigate seemingly contradictory requirements. These approaches reconcile the demands for computational scalability with regulatory mandates for data immutability and traceability, offering practical guidance for regulated research environments seeking to leverage advanced AI capabilities without compromising their regulatory standing.