Cloud security architectures for AI-enabled healthcare diagnostics and personalized treatment plans

The integration of artificial intelligence (AI) into healthcare diagnostics and personalized treatment planning has introduced unprecedented opportunities for precision medicine, yet it concurrently escalates the complexity of ensuring secure, compliant, and resilient computing infrastructures. This research explores the design and deployment of cloud security architectures tailored to the sensitive operational context of AI-enabled healthcare ecosystems. Emphasis is placed on the security frameworks and protocols necessary to safeguard patient data confidentiality, ensure integrity and availability of diagnostic algorithms, and maintain regulatory compliance under frameworks such as HIPAA and GDPR. The study evaluates architectural models including multi-cloud and hybrid-cloud deployments, examining their implications for access control, federated identity management, secure data storage, and real-time threat detection. Additionally, it investigates cryptographic techniques, secure multi-party computation, and homomorphic encryption in the context of distributed AI workloads. The paper concludes by identifying current limitations, proposing optimized security mechanisms, and outlining directions for future research in building robust, scalable, and privacy-preserving cloud infrastructures for AI-driven healthcare solutions.