A Study On Advanced Frameworks For Enhancing Personal Identity Protection And Information Security In Digital Environment

Abstract

This study investigates advanced frameworks for enhancing personal identity protection and information security within digital environments, addressing the growing risks associated with data breaches, identity theft, and cyber-attacks. As digital interactions proliferate, traditional security frameworks struggle to keep up with increasingly sophisticated threats, highlighting the need for proactive, adaptive, and technology-driven solutions. This research explores the potential of integrating advanced technologies such as artificial intelligence (AI), machine learning (ML), and blockchain to improve real-time threat detection, enhance data
integrity, and create flexible security systems capable of evolving alongside emerging threats.
Using a qualitative methodology, data was gathered from 138 respondents, reaching sample saturation, and analyzed through thematic analysis to identify key themes, including the limitations of existing frameworks, the role of user education, and the significance of global best practices. Findings reveal that implementing Privacy-by-Design, adopting predictive analytics, and developing modular security frameworks can address current security gaps.
Additionally, user education and adherence to global standards like GDPR and NIST are essential in fostering a secure, user-centered approach to digital interactions.
This study contributes to the field of cybersecurity by proposing actionable recommendations for organizations, policymakers, and technology developers, emphasizing a multi-layered approach to identity protection and information security. The insights from this research offer a pathway toward building resilient digital security frameworks that safeguard personal data while adapting to the dynamic landscape of modern cyber threats.