The Convergence of AI Governance: A Framework for Privacy, Security, and Model Management (Published)
As artificial intelligence continues to reshape enterprise operations, the need for comprehensive governance frameworks has become increasingly crucial. This article examines the convergence of data privacy, model governance, and cybersecurity in AI systems, presenting an integrated approach to addressing these interconnected domains. The article analyzes the implementation of privacy-preserving techniques, model accountability frameworks, and cybersecurity measures across various industries, including the public sector and biopharmaceutical industry. Through examination of current practices and emerging trends, this article demonstrates how organizations can effectively bridge technical, ethical, and organizational considerations in AI governance. The article highlights the importance of cross-functional oversight, unified policies, and continuous risk assessment in building and maintaining trusted AI systems, while emphasizing the role of stakeholder communication and regulatory compliance in successful AI deployment.
Keywords: AI governance framework, cybersecurity integration, data privacy protection, model accountability, stakeholder trust management