ZERO TRUST AT SCALE: ARCHITECTING A FRICTIONLESS, AI-POWERED CYBERSECURITY FRAMEWORK FOR THE GLOBAL DIGITAL ECONOMY

In the face of an increasingly complex and interconnected digital economy, traditional cybersecurity models are proving inadequate to protect against sophisticated threats. This research paper, titled Zero Trust at Scale: Architecting a Frictionless, AI-Powered Cybersecurity Framework for the Global Digital Economy, explores the role of Zero Trust (ZT) security models and artificial intelligence (AI) in enhancing cybersecurity for modern enterprises.The core thesis of this paper centers on the implementation of Zero Trust at scale, enabling organizations to effectively secure their environments without compromising on user experience or performance. In the context of growing cyber threats, the research underscores the importance of a Zero Trust framework that assumes no implicit trust within internal or external networks. Instead, Zero Trust requires rigorous identity verification, access control, and continuous monitoring to ensure that only authenticated users and devices gain access to critical resources.A major aspect of this paper is the integration of AI and machine learning (ML) to optimize Zero Trust frameworks. By leveraging AI, organizations can automate and streamline security operations, detect anomalies in real-time, predict potential threats, and ensure dynamic access control based on user behaviours. The adoption of AI-driven Zero Trust is presented as a powerful strategy to enhance threat intelligence, reduce incident response times, and increase overall cybersecurity resilience.Furthermore, the paper highlights the challenges faced by organizations when scaling Zero Trust systems, such as performance bottlenecks, legacy system integration, and regulatory compliance. It discusses solutions and best practices for overcoming these challenges, offering a roadmap for organizations looking to implement Zero Trust in a cost-effective and scalable manner.