This paper provides a comprehensive overview of inherent vulnerabilities and strategic data management techniques for Large Language Models (LLMs). It systematizes the diverse risks, including data poisoning, privacy breaches, and the generation of erroneous information (”hallucinations”), emphasizing how these issues arise from the underlying data and training processes. The paper details various ”guardrail” architectures and data-centric methods designed to secure LLMs. It particularly highlights layered protection models, the use of Retrieval-Augmented Generation (RAG) to ground responses in external knowledge bases, and techniques for bias mitigation and ensuring data privacy, all crucial for maintaining data integrity and responsible LLM deployment.