Cloud computing has made powerful AI systems accessible to researchers and organizations worldwide. However, this convenience brings serious concerns about data privacy, ownership, and compliance.

As machine learning models require large datasets, organizations often face trade-offs between innovation and data protection. To maintain privacy, techniques such as homomorphic encryption, differential privacy, and federated learning are increasingly being adopted.

The future of ethical AI in the cloud depends on collaboration between researchers, policymakers, and industry leaders. Balancing innovation with responsible data practices will be the cornerstone of sustainable digital progress.