Volume no :
|
Issue no :
Article Type :
Author :
Mr.Sidharth Sharma
Published Date :
Publisher :
Page No: 1 - 5
Abstract : The adoption of multi-cloud environments has become a strategic necessity for organizations seeking scalability, flexibility, and operational efficiency. However, distributing workloads across multiple cloud providers introduces significant security challenges, including authentication vulnerabilities, inconsistent security policies, data breaches, and compliance risks. Traditional security approaches often fail to address the complexity of multi-cloud ecosystems, requiring a more comprehensive risk mitigation strategy. This paper analyses key security risks in multi-cloud architectures and evaluates industry-standard risk assessment frameworks to prioritize effective countermeasures. Our findings indicate that authentication, access control, and secure cloud networking are the most critical areas requiring immediate attention. Threats such as identity mismanagement, insecure data transfers, and lack of unified monitoring further escalate security concerns. To mitigate these risks, we propose a combination of zero-trust architecture, robust identity and access management (IAM), encryption protocols, and AI-driven threat detection. Implementing these strategies can enhance data integrity, regulatory compliance, and overall cloud security posture. By adopting a proactive approach, IT leaders can optimize cybersecurity investments and ensure the resilience of multi-cloud environments. This study provides actionable insights to strengthen security in distributed cloud architectures, enabling organizations to defend against evolving cyber threats.
Keyword Multi-cloud security, cloud computing, authentication, risk mitigation, IAM, compliance, zero-trust, encryption, network security, threat detection.
Reference:
  1. Hunt, E. B. (2014). Artificial intelligence. Academic Press.
  2. Holmes, J., Sacchi, L., & Bellazzi, R. (2004). Artificial intelligence in medicine. Ann R Coll Surg Engl86, 334-8.
  3. Winston, P. H. (1992). Artificial intelligence. Addison-Wesley Longman Publishing Co., Inc..
  4. Winston, P. H. (1984). Artificial intelligence. Addison-Wesley Longman Publishing Co., Inc..
  5. Boden, M. A. (Ed.). (1996). Artificial intelligence. Elsevier.
  6. Plattner, H., Bog, A., Schaffner, J., Krueger, J., & Zeier, A. (2020). U.S. Patent No. 10,713,253. Washington, DC: U.S. Patent and Trademark Office.
  7. Plattner, H., Zeier, A., & Juergen, M. (2014). U.S. Patent No. 8,756,686. Washington, DC: U.S. Patent and Trademark Office.
  8. Monash, C. A. (2006). Memory-Centric Data Management. Monash Information Services, Version1.