SECURING DATA PRIVACY IN MULTI-CLOUD AND HYBRID CLOUD ENVIRONMENTS: ADVANCED THREAT MODELING AND MITIGATION STRATEGIES

Distributed data, multiple platforms and an extended attack surface posed by multi-cloud and hybrid cloud environments present a host of cybersecurity issues. In this review, we survey more recent threat modeling and mitigation techniques that go towards guaranteeing data privacy in the face of distributed infrastructures of these complexities and, in particular, the necessity for proactive threat identification and resilient defenses in the context of distributed systems. This discussion focuses on AI-driven threat modeling for predictive threat detection, quantum-safe encryption to safeguard data against future quantum computing threats, and blockchain solutions to ensure tamper-proof data integrity and support decentralized identity management. Moreover, the review of the related work reviews Zero Trust security frameworks, which remove implicit trust from networks and automated compliance monitoring, which continuously enforces regulatory standards for all cloud services. Organizations can use these approaches to further fortify data privacy safeguards, automate security operations (through intelligently automated processes) and remain in a perpetually compliant regulatory state. This paper distils the insights and best practices into a more resilient cybersecurity posture for enterprises dealing with multi-cloud and hybrid cloud environments.