Analyzing the Security and Privacy Challenges in Implementing Ai and Ml Models in Multi-Tenant Cloud Environments

Abstract

This paper aims to establish a perspective of how privacy and security were affected due to early integration of AI/ML in a multi-tenant cloud computing environment. It pertains to the need to protect personal data, intellectual property, and AI/ML models with reference to the shared computing assets. The paper also looks at the countermeasures which have already been adopted in the advanced forms that include deep FPGA frameworks for multi-tenant environments and hybrid block chain-homomorphic encryption. In this case, threat modeling, risk analysis and security approach assessment are employed in order to outline critical risks and proffer feasible counter measures. Therefore, the outcomes and assessments can be concluded as pinpointing the need for user training, constant security evaluations, and the integration of new technologies, including the zero-trust concept and the usage of artificial intelligence to detect threats. Implications for enhancing cybersecurity when adapting to new cloud systems are discussed in the summary of the given research.