AI-Driven Threat Detection and Response for Healthcare: Securing Patient Data in Cloud Environments

Several healthcare organizations have started adopting cloud services, which has led to the emergence of this issue, with patient data privacy and security being the most crucial. Current conventional security operating models are more reactive and based on rules that do not suffice in the case of new and more complex threats in large systems dealing with big data from EHRs, connected smart devices, and heavily used patient care access. This paper provides a detailed overview of threat detection and response systems in the healthcare sector through the help of a powerful Artificial Intelligence system. The system utilizes ML models trained on past intrusion data from cloud-native services such as Amazon SageMaker, AWS GuardDuty, and Macie. It performs realtime threat detection on the client’s network and responds to them effectively while adhering to HIPAA standards. Such technological components consist of Federated Learning, an advanced method of training Machine Learning models without compromising the data owner’s privacy, Behavioural Biometrics as an improved method of identification and authentications, and Blockchain technology to provide an unchanging record of events. Realizations of the framework were performed based on both artificial and real datasets of a hospital to show that it outperforms traditional systems with 97.2% average detection accuracy, 70% less false positive rates, and saved hours, whereas the meantime for threat detection was reduced to seconds. The study also discusses AI’s use in real-time compliance monitoring, eradicating compliance issues and operational expenses. There exist great prospects for the further improvement of health care information security utilizing AI as an instrument for advances in early, continuous, and scalable actualisation of patient data’s cloud; as for other considerable further studies, there are tendencies in explainable models, intelligence federation, and quantum insensitivity of information protection