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:
Agentic Workflows
Agentic workflows are multi-step AI processes where the system autonomously plans, executes, and iterates tasks—researching, drafting, reviewing, and...
Agentic AI
Agentic AI refers to systems that autonomously pursue goals—planning actions, employing tools, and adapting based on feedback—without waiting for human...
Fine-Tuning
Fine-tuning continues training a pretrained language model on a smaller, task-specific dataset so it internalizes particular behaviors, styles, or domain...