Cloud Misconfiguration as a Governance Failure in AI-Enabled Healthcare and Finance with Privacy Risk Implications
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Background: The problem of cloud misconfiguration has gained considerable importance in the AI-based environment of healthcare and finance due to the vulnerability to privacy attacks, data breaches, and security issues. In this study, cloud misconfiguration is viewed as a case of governance failure and its effects on privacy risks and security performance are investigated. Methods: A quantitative survey was carried out among 155 healthcare and financial practitioners in the USA. The structured questionnaire estimated cloud governance, AI governance maturity, cloud misconfiguration risk, privacy risk, and organizational security performance based on a five-point Likert scale. Descriptive statistics, Pearson correlation, and multiple regression analyses were used to explore associations between the research variables. Results: Organizational security performance was assessed with the highest average score (4.01), followed by privacy risk (3.97) and cloud governance (3.91). Data privacy (24.5%) and access control (21.9%) appeared to be the key cloud misconfiguration problems while enhanced data privacy (25.2%) and improved security (21.3%) represented the most significant governance benefits. Cloud governance had a positive effect on AI governance maturity (r = 0.642) and organizational security performance (r = 0.603). Regression analysis showed that cloud governance (β = 0.328) and AI governance maturity (β = 0.274) positively impacted security performance whereas cloud misconfiguration risk (β = −0.287) and privacy risk (β = −0.231) negatively affected it. Conclusion: Enhancement of cloud and AI governance will help mitigate misconfiguration risks, protect privacy, increase security performance, and ensure secure, resilient, and trustworthy digital transformation in AI-based healthcare and finance.
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