Data Privacy And Security Challenges In AI-Driven Cloud Health Systems: A Regulatory Perspective

Abstract

AI systems in the cloud pose a significant challenge as many of them are human-centered and involve sensitive health information. Patients have been increasingly concerned about healthcare cloud technology and data communication services. Although the cloud still presents new opportunities for data storage, computing, and transmission in healthcare, it can also expose users in the health sector to episodes or scenarios that produce unintended outcomes, and even harm when misuse occurs. Evidence suggests that the cloud environment suffers from basic vulnerabilities such as ongoing attacks on confidential data. In addition, health AI systems are increasingly scrutinized in the wake of numerous AI scandals and fraudulent scandals in numerous industries. AI designs and tools that lack transparency and explainability, which black-box results that violate accountability obligations, untested algorithms that unfairly affect people’s lives, and systems based on scraped or socially questionable information are some examples of AI use gone wrong. These examples represent opportunities for human researchers and engineers at big companies to stray from proper AI design, deployment, and use paths intentionally, thus resulting in harmful outcomes.

AI is prepared in a way that monitors, groups, seeks unusual samples, and signals alerts using past health data and behavior. The cloud is a versatile data repository and AI enhancer for health, but it processes data off-premise, away from the direct control of patients and actual data owners. Patients have clouded data streams, coupled with AI health systems, thus losing important algorithmic control semantics. They cannot define what data of theirs are used for what purpose, by whose algorithm, when, where, and how. Developer-oriented data abstraction trends make many patient-focused algorithms work directly with durable storage, scope, and type mechanisms rather than referencing individual data points. Patients’ data can be reused in ways that violate patients’ own expected purposes. Audits, permissions, shackles, and compensations are explicit governance measures that need to be securely and formally established, monitored, enforced, and updated.