Large language models (LLMs) hold immense promise for revolutionizing enterprise automation. However, their widespread adoption is hindered by concerns surrounding data privacy, regulatory compliance, and the potential for bias. We introduce SeLLMA, a novel secure LLM adaptation framework designed to enable organizations to harness the power of cloud-hosted LLMs while maintaining stringent data privacy standards, ensuring regulatory compliance, and mitigating biases during inference. SeLLMA employs a multi-faceted approach to safeguard sensitive information, leveraging advanced personally identifiable information (PII) detection techniques, implementing secure data transformation methods, and incorporating bias mitigation strategies. Our framework achieves 90% accuracy in PII detection while maintaining high performance across diverse enterprise use cases. This paper presents SeLLMA’s architecture, outlines our evaluation methodology, and demonstrates through empirical results its effectiveness in enabling secure and compliant LLM adoption for enterprises of all sizes.