Data Protection through Governance Frameworks

Authors

  • Sivananda Reddy Julakanti Independent Researcher, Southern University and A&M College, Baton Rouge, Louisiana, USA.
  • Naga Satya Kiranmayee Sattiraju Graduate Student, Trine University, Allen Park, Detroit, Michigan, USA.
  • Rajeswari Julakanti Graduate Student, Southern University and A&M College, Baton Rouge, Louisiana, USA.

Keywords:

Data Protection, Governance Frameworks, Data Security, Compliance, Risk Management

Abstract

In today’s increasingly digital world, data has become one of the most valuable assets for organizations. With the rise in cyberattacks, data breaches, and the stringent regulatory environment, it is imperative to adopt robust data protection strategies. One such approach is the use of governance frameworks, which provide structured guidelines, policies, and processes to ensure data protection, compliance, and ethical usage. This paper explores the role of data governance frameworks in protecting sensitive information and maintaining organizational data security. It delves into the principles, strategies, and best practices that constitute an effective governance framework, including risk management, access controls, data quality assurance, and compliance with regulations like GDPR, HIPAA, and CCPA. By analyzing case studies from various sectors, the paper highlights the practical challenges, limitations, and advantages of implementing data governance frameworks. Additionally, the paper examines how data governance frameworks contribute to transparency, accountability, and operational efficiency, while also identifying emerging trends and technologies that enhance data protection. Ultimately, the paper aims to provide a comprehensive understanding of how governance frameworks can be leveraged to safeguard organizational data and ensure its responsible use.

 

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Published

2023-03-07

How to Cite

Sivananda Reddy Julakanti, Naga Satya Kiranmayee Sattiraju, & Rajeswari Julakanti. (2023). Data Protection through Governance Frameworks. Journal of Computational Analysis and Applications (JoCAAA), 31(1), 158–162. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1525

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