Generative AI governance refers to the policies, controls, monitoring, and oversight structures that enterprises put in place to deploy AI responsibly and in alignment with regulations like the EU AI Act, NIST AI RMF, and emerging laws in LATAM.
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A mature AI governance framework covers six pillars: acceptable use policy, data privacy and consent, model risk assessment, human-in-the-loop for high-stakes decisions, output monitoring and audit trail, and employee training on responsible AI.
In practice, governance starts with naming an AI governance lead, inventorying current AI use cases, classifying risk by use case, and implementing controls proportional to risk. Regulated industries (finance, healthcare, legal) face stricter requirements than marketing or creative functions.
How it works
Governance frameworks translate high-level principles (fairness, transparency, accountability) into concrete processes: prompt review before deployment, logging of AI interactions, periodic bias audits, and escalation paths for problematic outputs.
Practical example
A US bank stands up an AI governance board covering compliance, legal, technology, and business. Every AI use case goes through a three-tier review: low-risk fast-track, medium-risk deep review, high-risk committee approval.
Definition by Miss Yera, Leading Woman in Technology in Peru · AI Consultant · Favikon 2025.
Version en espanol: /glosario-ia/#generative-ai-governance