AI Governance
AI governance is the set of policies, processes, roles, and controls that organizations and regulators put in place to ensure AI systems are developed and used safely, ethically, transparently, and in compliance with applicable law.
Definition
AI governance is the set of policies, processes, roles, and controls that organizations and regulators put in place to ensure AI systems are developed and used safely, ethically, transparently, and in compliance with applicable law.
Overview
As AI systems have moved from experimental projects to production software making consequential decisions, organizations have needed a formal structure for overseeing them — the same way software engineering relies on code review and change management, or finance relies on audit and compliance functions. AI governance is that structure: it defines who is accountable for a model's behavior, what documentation and testing are required before deployment, how models are monitored once live, and how incidents are investigated and remediated. At the organizational level, AI governance typically involves cross-functional review boards that assess new AI use cases for risk before launch, model documentation standards (often called model cards or datasheets) that record training data, intended use, and known limitations, and ongoing monitoring for issues like model drift, data drift, and AI bias. Tools like a model registry support this by tracking which model version is deployed where, along with its approval history and performance metrics — making governance auditable rather than a matter of institutional memory. At the regulatory level, AI governance is increasingly formalized in law. The EU AI Act classifies AI systems by risk tier and imposes escalating obligations — from minimal requirements for low-risk uses to strict conformity assessments for high-risk applications like biometric identification, hiring, and credit scoring. Other jurisdictions have taken varied approaches, from sector-specific rules to voluntary frameworks like the NIST AI Risk Management Framework in the United States. Effective AI governance connects these external requirements to internal practice: it operationalizes responsible AI principles and explainable AI (XAI) into processes engineering teams can actually follow.
Key Concepts
- Defines accountability, roles, and approval processes for deploying AI systems
- Requires documentation standards such as model cards and dataset datasheets
- Relies on model registries and monitoring tools to track deployed models and their history
- Covers ongoing risk monitoring for issues like model drift, data drift, and bias
- Increasingly shaped by regulation such as the EU AI Act and the NIST AI RMF
- Classifies AI use cases by risk tier, applying stricter controls to higher-risk applications
- Operationalizes responsible AI and explainability principles into repeatable processes