AI governance,
in plain English.
Short, accurate definitions of the terms that come up when organizations start governing how employees use AI.
AI gateway
An AI gateway (also called an LLM proxy or LLM gateway) is a proxy that sits between users or applications and AI model providers. It authenticates callers, records usage and cost, routes requests to the right model, and applies security controls such as data-leakage detection, without changing application code.
Read the definition →AI data loss prevention (AI DLP)
AI data loss prevention (AI DLP) is a set of controls that inspect the content people and applications send to AI tools (prompts, pasted text and uploaded files) and stop sensitive data from leaving the organization, by warning the user, redacting the sensitive parts, blocking the request, or routing it to an approved model.
Read the definition →AI acceptable use policy
An AI acceptable use policy is an organizational policy that states which AI tools employees may use, for which purposes, with which categories of data, and under what conditions, along with how use is monitored and what happens when the policy is broken.
Read the definition →AI governance
AI governance is the set of policies, roles, processes and tools an organization uses to make sure AI is used safely, legally, cost-effectively and in ways that create value. For most organizations, the largest and fastest-growing part is governing how employees use generative AI tools.
Read the definition →LLM token costs
LLM token costs are the charges model providers bill for API use, calculated from the number of input tokens (the prompt and context) and output tokens (the response), each at a per-model rate. Tokens are word fragments; long contexts, agent loops and large models drive cost up.
Read the definition →Prompt injection
Prompt injection is an attack in which instructions are hidden in content that an AI model processes, such as a web page, email, document or tool result, so that the model follows the attacker’s instructions instead of its intended ones, for example by leaking data or taking unwanted actions.
Read the definition →AI literacy
AI literacy is the combination of skills, knowledge and understanding that lets people use AI systems effectively and responsibly: knowing what a tool can and cannot do, how to prompt it well, how to check its output, and which data must never be shared with it.
Read the definition →Shadow AI
AI tools, accounts and models used without the organization’s knowledge or approval, and how to bring them into the light.
Read the guide →Find out how your company really uses AI.
Start a free 30-day AI usage assessment: a complete shadow AI inventory, the sensitive data leaving, and the AI seats you are paying for but not using.