AI Governance | Ethyca

AI Governance

The discipline of setting policies, roles, controls, and oversight mechanisms for an organization's development and use of AI, covering risk, compliance, ethics, security, and data protection across the AI lifecycle.

AI Governance is the discipline of overseeing an organization's development and use of AI systems — across risk, compliance, ethics, security, and data protection — throughout the AI lifecycle. Where information governance covers data and information security covers access, AI governance covers the models themselves: how they are trained, evaluated, deployed, monitored, retired, and held accountable.

Modern AI governance programs rest on a few common components:

• AI inventory (a catalog of every model in production, including third-party APIs)
• risk classification (which models are high-risk under EU AI Act, NIST AI RMF, or internal policy)
• lifecycle controls (data, training, evaluation, deployment, monitoring)
• roles and accountability (a designated AI owner per system, escalation paths, board-level oversight where appropriate)
• incident response (what happens when a model misbehaves).

The discipline is converging fast with privacy and data governance because the same questions — what data, on whose behalf, for what purpose, with what controls — apply to both. Organizations that have invested in mature data governance have a meaningful head start. Those that have not will find AI governance is the forcing function that finally makes them invest.