Data Security and Data Privacy in AI driven Healthcare System with reference to US Hospitals

Abstract Data security and privacy standards in U.S. hospitals from 2018 to 2023, a period of rapid technological change and growing number of security issues, are examined from the/embedded relationship between artificial intelligence and the use of artificial intelligence in hospitals of U.S. hospitals. As learned through a study, the study provides some interesting and significant implications for the healthcare organizations, technology vendors, security professionals, and policymakers: through a thorough statistical analysis of healthcare data breach trends, AI adoption patterns, and security measures for hospital types. Although all data points improved in physical security measures, hacking/IT incidents increased by 29 percent year over year. In fact, the hospital size correlated even stronger (r>0.98) to the hospital security capabilities and hence to the variability of healthcare settings. Statistical analysis was performed to validate that AI implementation and security measures show strong correlation (Cramer’s V=0.83), regression analysis was applied to determine its relationship with breach costs and recovery time as a function of budgets allocated for security, training hours and security credentials that explained 99% of variance of victim organization’s costs and 95% of variance in recovery time. Among the interesting findings, security investment is very different across types of hospitals (p<0.001). The amount of money spent on security by critical access hospitals is 3.1%, academic medical centers 6.8% and nonprofits 3.8%. This is because any security incidents take 9.7 days to recover — rather than 15.8. However, the gap was huge between the hospital capabilities that they do have (Only 49.3% of hospitals have dedicated AI working security teams) and the claims of hospitals being HIPAA compliant (85.7% hospitals provide HIPAA compliant AI systems). In addition, these results demonstrate that good security is not only determined by following the law, but by organizational dedication, resource allocation, specialist knowledge, proper ongoing improvement processes. The paper ends with an AI-specific security framework, a size-appropriate security strategy, a better revised budget allocation plan, and a specialized training program to train AI professionals working in healthcare.