A Large Language Model (LLM) is a type of artificial intelligence system trained on massive text datasets — often billions to trillions of tokens — that can understand, generate, and manipulate human language. LLMs form the foundation of most modern generative AI tools.
Major LLMs include: GPT-4 and GPT-4o (OpenAI, powering ChatGPT), Claude (Anthropic), Gemini (Google), Llama (Meta, open source), and Mistral (Mistral AI). These models are typically based on the Transformer architecture and use self-supervised learning on internet-scale text data.
Enterprise security considerations for LLMs include: data privacy (information entered as prompts may be retained or used for training), hallucinations (LLMs can generate plausible but incorrect information), prompt injection vulnerabilities, inconsistent outputs that may affect business decisions, intellectual property questions about AI-generated content, and the need for human oversight of LLM outputs.
Organizations managing LLM usage need policies covering: which LLM-powered tools are approved, what data can be used in prompts, how AI-generated content should be reviewed and attributed, API security for LLM integrations, and monitoring of LLM usage across the organization.
