Last Updated on April 18, 2026 by KnownSense
Generative AI is quickly becoming part of everyday work. Teams use it to build chatbots, search experiences, workflow automation, copilots, and agent-based systems. But as adoption grows, so do the risks. The moment you connect an application to a large language model, you also create new questions about data privacy, security, and safe use.
This article is for builders who want to use LLMs responsibly. Maybe you are designing a chatbot. Maybe you are building a RAG pipeline, an internal assistant, or a more advanced AI agent. Whatever the use case, the goal is the same: understand how to use generative AI safely while protecting sensitive data.
Thinking in Systems, Not Just Apps
When we talk about an “app,” we usually mean a full system. That system may include a frontend and a backend. It may also include multiple backend services, APIs, and data stores. In many cases, it includes more than one client experience too, such as a web app, mobile app, or desktop app.
Now imagine adding an LLM to that system. The model could be hosted internally, or it could come from a third-party provider. Either way, the integration decision matters. A common first instinct is to connect the frontend directly to the model. That may feel simple, but it is rarely the safest design.
A better way to think about this is to compare the LLM to a database. You would not let a frontend talk directly to a production database without strong controls in the middle. The same principle applies here. In most cases, the safer pattern is to connect to the LLM through the backend, where you can enforce authentication, authorization, logging, redaction, rate limits, and policy checks.
How Applications Connect to LLMs
There are several ways to integrate with an LLM. The most direct option is to use model APIs. In this approach, your system sends prompts or messages to the model and receives responses. This gives you low-level control, but it also means you must handle more of the safety and privacy concerns yourself.
Another common option is to use an SDK provided by the model vendor. SDKs wrap the underlying APIs and make integration easier. They often improve developer experience, but they do not remove your security responsibilities. Whether you use raw APIs or SDKs, you still need to think carefully about what data you send, how you process responses, and where risks can appear.
Why OWASP Matters for Generative AI
To build safe GenAI systems, we need a strong security foundation. That is where OWASP comes in. OWASP, the Open Worldwide Application Security Project, is a nonprofit organization that helps the industry improve software security. Many engineers know OWASP from its work on web application risks and secure development guidance.
As generative AI adoption accelerated, OWASP expanded its focus to include AI-powered applications. Starting in 2023, it began publishing guidance focused on the security risks specific to LLM-based systems. That guidance gives teams a practical framework for understanding where GenAI systems fail and how to design them more safely.
Where We Go Next
Once we understand how LLMs fit into modern systems, the next step is learning how to use them safely. That means looking at the most important security risks, the operational guardrails needed to protect data, and the architecture patterns that reduce privacy exposure. Let’s, explore the core areas of generative AI risk, privacy and safe use: