The increasing adoption of Artificial Intelligence (AI) and Machine Learning (ML) within pharmaceutical enterprises introduces profound opportunities in process automation, predictive maintenance, quality assurance, and decision-support systems. However, such adoption also imposes significant challenges regarding regulatory compliance, data governance, privacy, system validation, and the overarching integrity of GxP‑regulated computerized systems. This paper presents a comprehensive conceptual framework that integrates established GxP guidelines with contemporary concerns of data lifecycle management, ML robustness, differential privacy, and system validation strategies. Through extensive theoretical analysis and literature synthesis, we identify critical compliance gaps, unintended risks (e.g., bias and privacy degradation), and propose a multi-layered, risk-based approach for validation, data governance, and system lifecycle management. The framework addresses data ingestion, profiling, transformation, model training, deployment, and monitoring — ensuring adherence to regulatory principles while preserving privacy and robustness. The paper further explores trade‑offs between data utility and privacy, the impact of adversarial vulnerabilities, and operational strategies for risk mitigation. We conclude with practical recommendations for organizations and regulators to foster safe, compliant, and effective AI‑driven GxP systems.

AI/ML in Pharma, GxP Compliance, Computer System Validation, Data Lifecycle Management

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