Generative AI refers to artificial intelligence systems that can create new, original content based on patterns and knowledge learned from large training datasets. Unlike traditional AI that classifies or predicts, generative AI produces novel outputs including text, images, code, music, video, and synthetic data.
Key types of generative AI include: Large Language Models (LLMs) like GPT-4, Claude, and Gemini that generate text, Image Generation models like DALL-E, Midjourney, and Stable Diffusion, Code Generation tools like GitHub Copilot and Cursor, Audio/Music generation, and Video generation models.
Enterprise generative AI governance challenges include: controlling which generative AI tools employees use (Shadow AI), preventing sensitive data from being entered into prompts, managing intellectual property implications of AI-generated content, ensuring accuracy and preventing hallucinated information from being used in business decisions, compliance with emerging regulations like the EU AI Act, and establishing clear policies on AI-generated content disclosure.
The rapid adoption of generative AI across enterprises has made AI governance a critical priority, as traditional security controls were not designed to address the unique risks of conversational AI interfaces.
