Confidential computing is used in various sectors to secure sensitive workloads. In financial services, it can secure transaction processing, preventing unauthorised access to sensitive financial data during computation. It can protect customer data, support secure multi-party transactions, and enable confidential analytics on encrypted data, enhancing fraud detection without exposing raw data. In healthcare, confidential computing can safeguard personal health information (PHI) during processing. It allows for secure sharing of medical data between institutions for research purposes while ensuring patient confidentiality. Applications include secure genome sequencing and confidential AI diagnostics. Government agencies can use confidential computing to handle classified and sensitive information securely. It supports secure voting systems, protects citizen data in public services, and enables confidential intelligence operations by ensuring data privacy during processing. These use cases illustrate how confidential computing enhances security and privacy for critical data across diverse, high-stakes environments, making it an essential technology for modern data protection.