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March 26, 2026Open Access

Architecting Trustworthy AI: Governance Frameworks For Responsible Artificial Intelligence In Enterprise Data Ecosystems

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Authors

SSSrinivasa Rao SeetalaData Management (Italy)

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Overview

Frameworks govern responsible AI implementation in enterprise data systems, suggesting improved ethical standards.

Key Points

  • The aim is to establish governance frameworks that ensure responsible use of AI in enterprise data systems.
  • Overview of existing global governance models related to AI.
  • Integration of principles from various disciplines such as ethics and law.
  • Emphasis on AI lifecycle management from data collection to auditing.
  • Identified key principles for responsible AI, including fairness and accountability.
  • Outlined the importance of governance in mitigating risks related to AI deployment.
  • Highlighted effectiveness of established frameworks like the NIST AI Risk Management Framework.

Cite This Study

Srinivasa Rao Seetala (2024) studied this question.

synapsesocial.com/papers/69c4cdcdfdc3bde44891a910https://doi.org/10.5281/zenodo.19208753
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Intelligent Data Governance and Ethical AI Framework for Enterprise Information Systems2026
  2. 2Ethical Artificial Intelligence in Business: Frameworks, Challenges, and Responsible Implementation2026
  3. 3Ethical and Governance Challenges of AI in Information Systems: Toward Responsible Adoption in Enterprise Systems2025 · 5 citations
  4. 4The Critical Importance of Risk & Governance for AI Initiatives2025 · 2 citations
  5. 5Lifecycle‐Based Governance to Build Reliable Ethical AI Systems2026 · 9 citations