As companies increasingly adopt generative AI technologies, securing these systems has become a critical concern due to rising threats such as cyberattacks and data breaches. Large language models (LLMs) and retrieval-augmented generation (RAG) systems, which integrate proprietary knowledge, introduce unique vulnerabilities that traditional web applications do not face. Among these is prompt injection, a type of exploit where attackers manipulate LLMs through crafted input prompts to generate sensitive or malicious outputs. Securing LLM applications requires a comprehensive approach involving traditional web security standards, machine learning security operations (MLSecOps), and new LLM-specific concerns. Mitigation strategies should include threat modeling, robust infrastructure, and secure plugin design, alongside maintaining a strong security culture and integrating security measures early in the development process. Real-world examples illustrate the potential for sensitive information disclosure, fraudulent scams, and supply chain attacks, emphasizing the need for continuous monitoring and collaboration across the AI ecosystem to address evolving threats effectively.