Picture this: it is late on a Friday, you are already behind on contract reviews, and the business drops a question in your lap - what exactly is an "AI agent" and who is responsible if it goes rogue? These questions are not hypothetical anymore. AI is fast becoming part of everyday business, and for in-house lawyers, that means grappling with a new vocabulary while trying to keep pace with legal risk.

Thankfully, there is now a clear, practical resource to help: the International Association of Privacy Professionals (IAPP) has compiled a comprehensive glossary of Key Terms for AI Governance. It is free, updated regularly and a treasure trove for lawyers needing to navigate conversations about AI confidently.

Why AI governance terms matter for legal teams

As AI tools embed themselves in every corner of the business - from chatbots to decision-making engines - the legal implications grow. Whether you are shaping AI policies, responding to a regulator, or advising the board on a new AI-driven product, having a grasp of the language is now a must.

This glossary gives you a shared vocabulary with your colleagues in tech, compliance and risk. That alone can save hours of confusion (and a fair bit of embarrassment). For example, if you are asked by the board to explain the risks of "shadow AI" when drafting an internal policy, this glossary helps you quickly craft a clear, confident response.

Five terms every in-house lawyer should know right now

Here are a handful of key definitions from the glossary that will make you sound instantly more fluent in AI:

1. AI governance
A system of frameworks, practices and processes for managing AI responsibly. Think of it as corporate governance, but for AI. It is about ensuring AI is developed and used ethically, in line with organisational objectives, and compliant with relevant regulations.

2. Accountability
This refers to holding the developers, deployers and distributors of AI responsible for how the system behaves. Crucially, actions and decisions made by AI must be traceable back to a responsible party .

3. Bias
Bias in AI can creep in from data, design or even societal prejudices. It can cause outcomes that disadvantage individuals or groups, so understanding where bias might arise is key to legal risk assessment.

4. Explainability (XAI)
This is the ability to explain how an AI model arrives at a decision. Regulators are increasingly focused on this, particularly in high-risk contexts like recruitment or lending.

5. Shadow AI
Unofficial, unsanctioned AI tools being used by employees. Just as shadow IT has been a headache for years, shadow AI brings security, privacy and compliance risks.

Why this glossary is worth a place in your bookmarks bar

The updated glossary does not just cover these big-ticket concepts. It also includes:

• The difference between "deep learning" and "machine learning".
• What "red teaming" means in an AI context.
• The nuances of terms like "hallucinations" (yes, that is a thing in AI).
• The building blocks of AI models, from data provenance to model cards.

And because AI technology evolves fast, the glossary is regularly updated with input from AI governance experts.

A practical step you can take this week

If AI already feels like an extra full-time job on top of your actual full-time job, do yourself a favour: skim this glossary and share the link with your team. Even a 20-minute browse will help you:

• Speak the same language as your tech colleagues.
• Ask better questions when new AI tools appear.
• Identify where you might need policies, training or external advice.

For in-house lawyers, knowledge really is power - especially when the robots are coming.