Addressing Security Challenges in Artificial Intelligence Driven Clinical Environments

This research aims to develop a novel security architecture to overcome complex security concerns in AI-powered hospitals and protect sensitive healthcare data. Symmetric key encryption, decoding, and enhanced post-processing ensure data privacy, security, and dependability. The framework employs key management for secure encryption. It also detects errors to ensure data accuracy. We compared the framework's performance to existing security approaches using data encryption standards, access control mechanisms, user identification, and anomaly detection accuracy. The comparison shows that the proposed technique outperforms the others in all categories. It outperforms conventional approaches in system stability, event response time, and healthcare regulatory compliance. The framework's stringent post-processing processes provide consistent decryption and validation. It provides an additional layer of protection against unauthorized access, data breaches, and alterations. Combining encryption and post-processing into one structure enhances healthcare data safety and management. This new system exceeds security norms. Healthcare organizations can securely employ AI-powered technologies while adhering to security protocols.