Securing AI/ML Operations in Multi-Cloud Environments: Best Practices for Data Privacy, Model Integrity, and Regulatory Compliance
Keywords:
AI/ML security, multi-cloud environmentsAbstract
Operations in multi-cloud artificial intelligence and machine learning depend on data security, model correctness, and regulatory compliance. Adopting AI/ML models across different cloud platforms runs businesses to risk data loss, model design problems, and regulatory non-compliance for scalability, flexibility, and processing. Multi-cloud AI/ML data privacy, model integrity, and regulatory compliance are examined in this paper. We explore in multi-cloud artificial intelligence and machine learning systems the advantages and drawbacks of cross-cloud data transfers, shared infrastructure, and cloud service provider security.
Several cloud platforms collecting and processing data makes data privacy challenging. Businesses have to abide by policies on data residency, access, and sharing. For data privacy several cloud platforms advise homomorphic encryption and secure multi-party computing. Safe enclaves, federated learning, and differential privacy provide data privacy without compromising model performance. These systems protect private information from leaks.
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