Adaptive cybersecurity frameworks for multi-cloud and edge computing : addressing threats, regulatory challenges, and autonomous intelligence

Abstract

As businesses move toward using multi-cloud and edge computing systems, keeping data and systems secure has become more challenging. This thesis introduces an adaptive cybersecurity framework designed to improve security in these complex environments. The framework uses artificial intelligence (AI) and machine learning (ML) to detect threats in real-time, automatically adjust security policies, and help organizations stay compliant with data protection laws like GDPR, CCPA, and China’s Cybersecurity Law. The design is based on a layered structure that allows the system to monitor, analyse, and respond to risks quickly and efficiently. A case study on the Capital One data breach is used to show how the framework could help prevent similar incidents. Overall, this research provides a smarter and more flexible approach to cybersecurity in today’s cloud-based world.