Secure AI Pipelines for Drug Repurposing: A Cybersecurity Approach to Biomedical Innovation

AI-driven drug repurposing has emerged as a transformative approach in pharmaceutical research, enabling the discovery of new therapeutic applications for FDA-approved drugs. This significantly reduces research and development (R&D) timelines and associated costs. However, the increasing reliance on AI-generated insights introduces vulnerabilities, making drug repurposing platforms susceptible to cyberattacks. These attacks can manipulate AI models to produce inaccurate drug predictions, potentially compromising clinical trial outcomes and patient safety.
This article provides a comprehensive examination of cybersecurity risks associated with AI-powered drug repurposing pipelines and presents a robust AI security framework to mitigate these threats. Key threats include model inversion attacks, where adversaries exploit AI models to infer sensitive drug trial data, and poisoning attacks, where malicious datasets distort AI-generated drug repurposing predictions.
To address these challenges, the paper proposes a multi-layered security strategy that incorporates homomorphic encryption for confidential AI-driven data processing, blockchain technology for immutable research records, and federated learning for secure cross-institutional AI model training. These technologies ensure that AI-driven drug repurposing insights remain protected, transparent, and verifiable.
By integrating secure AI pipelines into drug repurposing research, pharmaceutical companies can enhance the reliability of drug discovery, protect intellectual property, and comply with evolving cybersecurity mandates. This framework also reinforces the U.S.'s competitive edge in global biopharmaceutical innovation, ensuring AI-driven drug discovery remains both secure and efficient.