The rapid proliferation of multi-tenant cloud infrastructures has intensified the need for verifiable privacy guarantees and automated governance, risk, and compliance (GRC) enforcement in data processing workflows. Traditional encryption-at-rest and in-transit safeguards are increasingly insufficient for modern regulatory and security demands, as sensitive computations often occur in untrusted environments. Confidential computing a paradigm leveraging hardware-based Trusted Execution Environments (TEEs) has emerged as a critical solution to address this challenge by ensuring that data remains protected even during computation. Evaluating confidential computing runtimes, such as Intel SGX, AMD SEV, and emerging cloud-native enclaves, reveals their potential to establish cryptographic attestations and verifiable execution proofs that enhance trust among tenants and auditors alike. At a broader level, these technologies underpin a shift toward transparent accountability frameworks where compliance verification becomes continuous, rather than periodic. Integrating confidential runtimes with automated GRC systems enables dynamic risk assessment, audit traceability, and policy enforcement without compromising data confidentiality. Yet, challenges remain: performance overhead, limited interoperability across providers, and the complexity of cryptographic attestation pipelines hinder seamless deployment. Addressing these obstacles requires standardized enclave management, decentralized identity integration, and AI-assisted anomaly detection for compliance deviation. Ultimately, confidential computing is not merely a security enhancement it represents a governance transformation. By embedding verifiable privacy and automated control logic into computational workflows, organizations can align operational transparency with regulatory trust, thereby redefining secure multi-tenant cloud computing for the next generation of privacy-centric digital ecosystems.