Large Language Models (LLMs) are the core of enterprise Generative AI applications. They can process and generate natural language, but they require additional components to handle user interactions, security, and other functionality to respond to or act on user inputs. The collection of these components and services that form a functional solution is called a Generative AI application. A best practice when developing a Gen AI application is to follow a standard AI Lifecycle and embed security at each phase of the AI lifecycle. Implementations must not neglect the basics such as authentication, authorization, protecting customer data, etc., which are required for AI solution just like for any other cloud solution. After we have addressed the basics, there are specific aspects for LLM and Generative AI security requiring special attention. Details vary depending on the implementation and are constantly changing in this fast-growing field, but understanding something about the properties allows us to address specific implementation needs. One way to think about securing it is to view the model as essentially intelligence in a black box that has certain defining characteristics: • AI models are stochastic/non-deterministic, requiring an engineer to account for them spontaneously failing or being incorrect. In some sense it doesn't matter if the failure is intentional (due to poisoned training data, adversarial input, etc.) or unintentional (the model is not accurate). Engineers need to expect failure, and it is likely best to assume failure in the worst and most spectacular way. • If you're giving it any data at all, either in the context window (via patterns like RAG) or by fine-tuning the model, it may include/leak data in its response. Building on these two attributes the session provides security guidance for the phases of the Generative AI lifecycle that will ensure that your AI solution has healthy security posture from the start.