AI Governance
AI governance is the set of policies, processes, and technical controls an organization uses to manage the risks of deploying AI systems. This includes deciding which use cases are appropriate for AI, how models are evaluated before deployment, who is accountable when they fail, and how you handle data privacy, bias, and regulatory compliance. The EU AI Act, which took effect in 2024, made governance a legal requirement for companies operating in Europe—classifying AI systems by risk level and imposing obligations that scale accordingly. But governance is not just a compliance exercise. Organizations without clear AI governance end up with shadow AI: employees using ChatGPT to draft contracts, analyze customer data, or make recommendations with no oversight, no audit trail, and no idea what the model was trained on. Governance is how you use AI aggressively without using it recklessly.
Related terms:
AI Copilot
An AI copilot is a model-powered assistant embedded in workflows—such as code editors, email clients, or design tools—that suggests next actions while keeping the human in control. This “propose, human dispose” pattern boosts productivity on familiar tasks and sidesteps deployment challenges like accountability and error correction.
Agentic AI
Agentic AI refers to systems that autonomously pursue goals—planning actions, employing tools, and adapting based on feedback—without waiting for human instructions at every step. Unlike passive AI that only responds when prompted, agentic AI can monitor systems, diagnose issues, and propose fixes on its own.
Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open standard from Anthropic that standardizes how AI models connect to external tools and data sources via a client-server interface, eliminating custom integration code. It handles tool discovery, authentication, and structured I/O so developers build each connector once for use by any MCP-compatible model.