Responsible AI Controls for Identity Governance, Data Trust, and Security Assurance in Multi-Cloud Customer and Patient Data Environments
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2025
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| _version_ | 1866901746183307264 |
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| author | Srinivasa Chakravarthy Seethala |
| author_facet | Srinivasa Chakravarthy Seethala |
| contents | <p><strong><span lang="EN-US">This study investigates the growing need for responsible AI controls that protect identity, maintain data trust, and ensure security assurance across multi cloud environments supporting both customer and patient information. As organizations expand into distributed computing models, the complexity of managing identities, safeguarding sensitive data, and maintaining regulatory alignment has intensified, creating a critical gap between traditional governance models and the demands of AI enabled platforms. The purpose of this research is to develop a comprehensive framework that integrates responsible AI mechanisms with identity governance, risk based access controls, automated policy validation, and real time monitoring to enhance protection across heterogeneous cloud ecosystems. The study applies a mixed methodology, combining qualitative analysis of governance practices, regulatory expectations, and risk taxonomies with quantitative examination of cloud identity workflows, anomaly detection signals, and AI driven policy enforcement patterns. Key findings highlight that responsible AI controls significantly strengthen data trust by improving consistency, transparency, and auditability in identity management operations while reducing access related security deviations. The proposed model advances current practice by aligning AI driven risk scoring, data lineage intelligence, and federated identity orchestration with compliance structures required in customer centric and patient centric environments. Strategic contributions include a new governance architecture for secure AI adoption and a validated control model that can support scalable, compliant, and ethically aligned data ecosystems. This research strengthens academic understanding of responsible AI oversight while offering practical pathways for industry implementation across healthcare and enterprise multi cloud platforms.</span></strong></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17878867 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Responsible AI Controls for Identity Governance, Data Trust, and Security Assurance in Multi-Cloud Customer and Patient Data Environments Srinivasa Chakravarthy Seethala Responsible AI, Identity governance, Multi cloud security, Data trust, Patient data protection, Customer data ecosystems, Automated compliance, Cloud access governance, Federated identity, AI risk scoring, Data lineage intelligence, Secure cloud architectures, Zero trust environments, Ethical AI controls, Policy automation, Cloud security assurance. <p><strong><span lang="EN-US">This study investigates the growing need for responsible AI controls that protect identity, maintain data trust, and ensure security assurance across multi cloud environments supporting both customer and patient information. As organizations expand into distributed computing models, the complexity of managing identities, safeguarding sensitive data, and maintaining regulatory alignment has intensified, creating a critical gap between traditional governance models and the demands of AI enabled platforms. The purpose of this research is to develop a comprehensive framework that integrates responsible AI mechanisms with identity governance, risk based access controls, automated policy validation, and real time monitoring to enhance protection across heterogeneous cloud ecosystems. The study applies a mixed methodology, combining qualitative analysis of governance practices, regulatory expectations, and risk taxonomies with quantitative examination of cloud identity workflows, anomaly detection signals, and AI driven policy enforcement patterns. Key findings highlight that responsible AI controls significantly strengthen data trust by improving consistency, transparency, and auditability in identity management operations while reducing access related security deviations. The proposed model advances current practice by aligning AI driven risk scoring, data lineage intelligence, and federated identity orchestration with compliance structures required in customer centric and patient centric environments. Strategic contributions include a new governance architecture for secure AI adoption and a validated control model that can support scalable, compliant, and ethically aligned data ecosystems. This research strengthens academic understanding of responsible AI oversight while offering practical pathways for industry implementation across healthcare and enterprise multi cloud platforms.</span></strong></p> |
| title | Responsible AI Controls for Identity Governance, Data Trust, and Security Assurance in Multi-Cloud Customer and Patient Data Environments |
| topic | Responsible AI, Identity governance, Multi cloud security, Data trust, Patient data protection, Customer data ecosystems, Automated compliance, Cloud access governance, Federated identity, AI risk scoring, Data lineage intelligence, Secure cloud architectures, Zero trust environments, Ethical AI controls, Policy automation, Cloud security assurance. |
| url | https://doi.org/10.5281/zenodo.17878867 |