AI Governance

What is AI Governance?

AI governance is the framework of policies, processes, standards, and controls organizations use to develop, deploy, and operate AI systems responsibly. It helps ensure AI systems are trustworthy, aligned to business objectives, and managed with appropriate oversight. 

This includes how organizations assess risk, assign accountability, enforce policy, monitor system behavior, and maintain compliance across the AI lifecycle. Credo AI’s glossary defines AI governance in terms of developing, deploying, and operating AI systems within a well-defined set of policies, procedures, and standards.

Why is AI Governance Important?

AI governance helps organizations manage the risks that come with scaling AI. Without governance, teams can struggle with limited visibility, inconsistent review processes, weak accountability, and gaps in compliance. Governance creates structure around how AI systems are approved, monitored, and maintained over time. IBM similarly frames AI governance as the processes, standards, and guardrails that help ensure AI systems are safe and ethical.

How AI Governance Works

AI governance usually works as a structured, repeatable process that helps organizations oversee AI systems from initial review through ongoing use.

1. Identify the AI system

The organization documents the AI system, its purpose, the data it uses, the team responsible for it, and where it will be applied.

2. Define applicable policies and requirements

The organization determines which internal policies, business standards, and regulatory requirements apply to the system based on its use case, risk level, and operating context.

3. Assess risk

The AI system is evaluated for potential risks such as privacy, fairness, security, safety, transparency, and compliance concerns. This helps determine the level of oversight and control required.

4. Apply controls and approvals

Based on the assessment, the organization applies the necessary controls, documentation, review steps, and approvals before the system is deployed or expanded into production use.

5. Monitor and review performance

After deployment, the system is monitored to track performance, detect drift or issues, and confirm that it continues to operate within policy and risk thresholds.

6. Maintain evidence and update governance

The organization keeps records of assessments, approvals, controls, and monitoring results so it can support audits, demonstrate accountability, and update governance as the system evolves.

Key Components of AI Governance

AI governance typically includes:

• Policies and standards to define how AI systems should be developed, reviewed, and used
• Oversight and accountability to clarify who is responsible for decisions and outcomes
• Risk assessment to evaluate issues such as fairness, privacy, safety, security, and compliance
• Controls and workflows to support approvals, documentation, and enforcement
• Monitoring and auditability to track performance, drift, and evidence over time

These elements align with how Alteryx describes AI governance as policies, processes, and oversight, and how IBM describes governance as guardrails supported by standards and oversight.

AI Governance vs. AI Risk Management

AI governance is the broader framework for overseeing AI systems. It defines how decisions are made, who is responsible, what standards apply, and how organizations maintain control over AI across the lifecycle. AI risk management is one part of that framework. It focuses more narrowly on identifying, assessing, prioritizing, and mitigating the risks tied to specific AI systems or use cases. Credo’s glossary also treats AI risk management as a related but separate concept within its governance vocabulary.

A simple way to distinguish them:

• AI governance sets the structure
• AI risk management applies that structure to risk

So governance answers questions like: What review is required? Who approves deployment? What evidence must be documented? Risk management answers: What could go wrong? How severe is the risk? What controls are needed? Alteryx makes a similar distinction in its glossary by separating overall governance from the risk-management frameworks used to address model failures, compliance issues, and other AI-related harms.

Common AI Governance Frameworks and Standards

Most organizations use established frameworks and standards to structure their AI governance programs. These frameworks help translate broad goals like trust, accountability, and compliance into practical requirements for documentation, controls, monitoring, and oversight. Credo’s platform and glossary ecosystem explicitly connect AI governance to standards and regulations, including the EU AI Act, NIST AI RMF, and ISO/IEC 42001.

The most common frameworks and standards to reference on this page are:

• EU AI Act: Regulatory framework that introduces obligations based on AI system risk and places strong emphasis on documentation, transparency, human oversight, and conformity requirements
• NIST AI RMF: Risk management framework that helps organizations govern, map, measure, and manage AI risks
• ISO/IEC 42001: Management system standard for establishing, implementing, maintaining, and improving AI governance processes within an organization

You do not need to explain each one at length on a glossary page. The important point is that these frameworks give organizations a structured way to define controls, assign accountability, and demonstrate that AI systems are being governed consistently. Alation likewise frames AI governance around regulatory frameworks and standards that support responsible deployment and legal alignment.