AI-driven threat detection in pharmaceutical R and D: Mitigating cyber risks in drug discovery platforms

The integration of Artificial Intelligence (AI) into pharmaceutical research and development (R&D) has transformed drug discovery, biomarker identification, and clinical trial automation, significantly reducing costs and expediting breakthroughs. However, the increasing reliance on AI-driven processes exposes pharmaceutical R&D to evolving cybersecurity threats, including adversarial AI manipulations, ransomware attacks, and AI poisoning. To address these challenges, this study explores AI-driven cybersecurity solutions, with a focus on machine learning-based Intrusion Detection Systems (IDS) capable of identifying anomalies in AI-generated predictions. Furthermore, it examines the role of federated learning in securing sensitive research data and proposes a national AI security framework aligned with the Cybersecurity and Infrastructure Security Agency (CISA) directives. By leveraging AI-powered anomaly detection, deep learning models, and automated incident response, organizations can enhance their resilience against sophisticated cyber threats. Despite these advancements, challenges such as algorithmic bias, false positives, and adversarial vulnerabilities persist.