# LLM Security

As enterprises rapidly build AI applications powered by Large Language Models (LLMs), the security paradigm is shifting. Traditional cybersecurity was built for static systems, structured inputs, and clearly defined perimeters. In contrast, LLM-based systems are dynamic, interpret natural language, and can generate unpredictable output—introducing entirely new categories of risk.

In LLM applications, every prompt is a potential attack vector. Inputs are unstructured, user-controlled, and context-sensitive. Attacks like prompt injection, jailbreaks, hallucinated code, and training data leakage don’t map neatly to conventional threats—and often bypass familiar controls like firewalls, schema validators, or access management. The result: a growing security grey zone between the LLM provider and the application developer.

This section contrasts traditional cybersecurity with emerging LLM Security (LLMSec) highlighting where classical approaches fall short, what new risks enterprises face, and how to adapt foundational security principles like defense-in-depth, least privilege, and monitoring to an AI-native environment.

Whether you’re deploying open-source models, fine-tuning foundation models, or integrating with providers like OpenAI or Anthropic, the responsibility for securing the LLM layer falls increasingly on the builder. This guide helps enterprise teams understand where the model ends and where your obligation begins—and how to architect, monitor, and defend LLM-powered systems at scale.