Learn how generative AI governance works, the risks it manages, and the frameworks teams use to deploy safe, compliant, and responsible GenAI systems at scale.

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Generative AI governance provides oversight across the AI lifecycle through policies, technical controls, and monitoring. Key risks include hallucinations, bias, safety issues, privacy exposure, and IP violations. A strong framework combines usage guidelines, data governance, model evaluation, audit trails, and technical safeguards like guardrails and red-team testing. Organizations must embed governance into MLOps pipelines with clear roles, approval workflows, and continuous monitoring. Regulatory requirements like the EU AI Act demand transparency and documentation. Effective governance turns experimental AI into dependable business components while maintaining accountability and enabling safe scaling.

Generative AI governance best practices

Why generative AI requires new governance approaches

Technical governance for LLMs and generative AI

The regulatory and policy landscape for generative AI

Enterprise implementation: How to operationalize GenAI governance

Best practices for generative AI governance

How Domino supports generative AI governance