The rise of generative AI has introduced powerful capabilities in content creation but has also surfaced complex legal, ethical, and privacy risks, particularly in the deployment of synthetic media. Traditional DevOps pipelines, while optimized for automation and speed, lack the built-in mechanisms necessary for handling these emerging compliance challenges. This paper proposes a compliance-aware DevOps framework that integrates legal risk management, data privacy controls, and model governance throughout the AI development and deployment lifecycle. Drawing upon a structured literature analysis of secondary sources, the study outlines a methodology for embedding regulatory compliance and ethical oversight directly into CI/CD workflows. Visual models are used to compare traditional and compliance-aware architectures, analyze implementation stages, and map challenges across technical and legal domains. The evaluation reveals that compliance-aware DevOps significantly enhances traceability, privacy assurance, and model accountability without impeding deployment efficiency. However, challenges such as regulatory fragmentation, lack of standardized metrics, and toolchain silos remain. This work presents a future-facing roadmap that emphasizes automation, interoperability, and adaptive risk management to support the responsible deployment of GenAI at scale.