Designing cybersecurity protocols for AI systems analyzing multi-omics and radiology data in clinical decision-making applications.

The integration of artificial intelligence (AI) into clinical decision-making has revolutionized personalized medicine, particularly through the analysis of multi-omics data (genomics, transcriptomics, proteomics) and high-resolution radiology imaging.These AI-driven models enable early disease detection, risk stratification, and treatment optimization by identifying complex biological and structural patterns.However, the use of such rich and sensitive data sources introduces new cybersecurity vulnerabilities that threaten patient confidentiality, data integrity, and system reliability.AI systems trained on centralized or partially anonymized datasets are increasingly susceptible to adversarial attacks, data poisoning, model inversion, and unauthorized access, particularly in cloud-based healthcare infrastructures.This paper presents a comprehensive framework for designing robust cybersecurity protocols tailored to AI systems analyzing multi-omics and radiological data in clinical applications.The approach incorporates end-to-end encryption, dynamic access controls, blockchainbased data provenance tracking, and real-time anomaly detection using AI-enhanced monitoring agents.We explore layered defense mechanisms that address both data-level security (via homomorphic encryption and differential privacy) and model-level protection (using adversarial robustness techniques and secure federated learning environments).Emphasis is placed on the unique challenges posed by heterogeneous data integration, which demands synchronized security measures across disparate modalities.We also assess regulatory compliance requirements (e.g., HIPAA, GDPR) and propose a governance model for secure AI deployment in clinical settings.By narrowing focus from broader health data security to the specific intersection of AI, multiomics, and radiology, this work provides actionable insights into the development of resilient, transparent, and compliant AI infrastructures for precision medicine.