AI model governance: What it is and why it’s important

Thought leadership

In November 2022, generative AI exploded into public awareness, surging in popularity with the introduction of ChatGPT. 

While the hype has settled down, AI — specifically, generative AI — continues to be a primary focus of organizations who want to leverage this game-changing technology for a wide range of capabilities. The collective impact of generative AI on global productivity could be as high as $4.4T annually, according to McKinsey Digital (1). And more than 50% of CIOs expect AI use to be widespread or critical in their organization by 2025.

If you strive to be data-driven and are leveraging AI technologies to gain a competitive edge, increase efficiency, and drive innovation, then you need to recognize, in addition to its potentially remarkable benefits, generative AI also presents significant risks. 

Bias. Regulatory compliance. Privacy. These risks can have significant legal and reputational consequences. 

That’s why AI governance is crucial in mitigating risks and ensuring your AI initiatives are transparent, ethical and trustworthy.

At Collibra, we define AI governance as the application of rules, processes and responsibilities to drive maximum value from your automated data products by ensuring applicable, streamlined and ethical AI practices that mitigate risk and protect privacy.

Why governance is so important

Data governance has always been an integral part of data management, ensuring data is managed, protected and utilized responsibly. Historically, data governance catered to conventional databases and structured data systems.

When we talk about AI governance, we refer to a comprehensive AI governance framework designed to oversee and guide AI’s development and application. Think of it as the master plan or the roadmap for building and deploying successful AI products. This framework does more than just set rules; it provides a clear, repeatable process, ensuring AI programs are sustainable and reliable over the long haul. 

By adhering to an AI governance framework, businesses can anticipate challenges, implement best practices, and maintain ethical standards, all of which are vital in today’s data-driven landscape.

AI models and governance

An AI model is, at its core, a mathematical construct. It takes data, processes it, and produces outputs, which could be predictions, decisions or insights. But how do we ensure that these models are making the right predictions? How do we ensure they aren’t biased or opaque? 

That’s where AI governance steps in. It’s the system of checks and balances for AI models, ensuring they are transparent in their operations, accurate in their predictions and fair in their outcomes.

However, even though every CEO wants generative AI applications, creating them can be time-consuming and costly.

If you want to implement AI governance, you may face several challenges, including: 

  • AI models are complex and require a cross-functional team for development and deployment that includes expertise in various areas such as data science, software engineering and compliance. 
  • Performance tracking requires continual monitoring and assessment of the accuracy and effectiveness of AI models. 
  • Ensuring the security and compliance of AI models is a critical challenge. 
  • Standardized practices are lacking and this creates further difficulties in implementing AI model governance. 

The essential components of AI model governance

Despite these challenges, nearly 8 out of 10 CIOs said scaling AI and ML use cases to create business value is their top priority over the next 3 years (2). 

If you want to establish effective AI model governance, you’ll need your organization to utilize several essential components:

Clarify ownership and accountability

Organizations should clearly define ownership and accountability of AI model development and deployment. It is essential to establish clear roles and responsibilities, making sure that the team tasked with developing and deploying AI models has the necessary data and tools and follows best practices.

Establish cross-functional teams

Creating cross-functional teams that include individuals with expertise in various areas is critical in ensuring that AI models are accurate, ethical and compliant. Collaborating with different departments, such as legal, compliance, and security, ensures that AI models align with an organization’s objectives and comply with regulations.

Implement data tracking and issue resolution

Data tracking allows organizations to catch any issues during the development process, monitor performance, and make informed changes when necessary post-deployment. Real-time monitoring of data tracking can help identify and resolve development issues, such as bias or non-compliance, more efficiently.

Make informed AI model governance choices

To make informed solution choices, organizations must consider various factors such as model explainability, ethical considerations, and compliance with regulations such as GDPR and CCPA. Tools such as governance frameworks that incorporate these considerations help organizations make informed AI model governance solution choices.

Define internal governance standards 

Defining regulations such as ethical considerations, data privacy rules, and legal compliance is critical in promoting transparency and accountability in AI models and ensuring they align with organizational objectives. Organizations must define regulations for both the development stage compliance, as well as post-deployment monitoring.

Create comprehensive model documentation

Creating comprehensive documentation for AI models ensures transparency and accountability. The documentation should outline each AI model’s objectives, processes, and limitations and explain the data used to develop the model. Documentation should also include information about monitoring and performance tracking metrics.

To illustrate the importance of these essential components in AI model governance, consider this hypothetical example: an AI algorithm used in a health system. The algorithm aims to detect early signs of cancer based on patient symptoms. In this scenario, the processes involved in developing and deploying the AI model would require a cross-functional team that includes expertise in both data science and the medical field. Clear definitions of roles and responsibilities and regulations that promote data privacy and ethical considerations are also critical components in this scenario.

Collibra and AI model governance

In any context — from governing nations to playgrounds — governance ensures order, safety and productivity. It provides structure and direction. In today’s AI-powered landscape, effective governance is vital. The stakes are high and the complexities can be overwhelming. It’s why AI model governance is essential in navigating the challenges and potential pitfalls of this rapidly evolving technology.

Still feeling overwhelmed? We can help.

We developed an easy-to-implement guide to assist in the creation of successful AI products. 

Discover our AI Governance Framework to learn more about how Collibra can help your organization.


  1. McKinsey Digital, ‘The economic potential of generative AI’
  2. Databricks, CIO Vision 2025

Related resources


AI Governance Framework: Why our tested framework is essential in an AI world


From blind trust to responsible AI: balancing opportunities and consequences


Solving the data-centric versus model-centric AI governance debate

Thought leadership

AI Governance Glossary

View all resources

More stories like this one

Jul 15, 2024 - 4 min read

How to observe data quality for better, more reliable AI

Read more
Jul 2, 2024 - 4 min read

Collibra AI Governance and de-risking unstructured data at Ohalo

Read more
Jun 27, 2024 - 5 min read

Defining responsible AI governance with UCLA Health

Read more