Top 8 AI Governance Platforms for 2025

Artificial intelligence (AI) often elicits a range of reactions. Some people grin with excitement at the possibilities AI offers; others wince with worry at the very real ethical and business concerns that AI brings.  

Whichever reaction you have, you’ll feel more comfortable knowing that AI governance tools are quickly becoming more common. AI governance tools are software that ensures AI is being implemented in your organization in a way that’s ethical, responsible, and secure — while encouraging your business to use AI to accelerate growth and efficiency. AI governance tools aren’t here to hold you back; they’re designed to propel your business forward safely and consciously. 

If your organization uses ChatGPT, Microsoft Copilot, or any other AI, you’re already going down this track. This article covers what you need to know about AI governance, as well as the top 8 AI governance tools for 2025. 

What is AI governance?

AI governance is the set of guidelines a company puts in place to make sure its AI is following all policies, laws, regulatory requirements, and other necessary obligations. However, that’s the bare minimum. Your company’s governance likely goes beyond simply checking legal boxes. AI governance requirements may also include other guidelines that help your company use AI in a safe, responsible, and ethical way. For example, your company may have an AI governance framework for respecting human rights and protecting copyright and intellectual property. 

Governance can also include best practices. With model governance tools as guardrails, employees can know how to implement rules that reduce busywork, improve visibility, streamline workflows, and document changes and ownership. Best practices also extend to cybersecurity. After all, the models and data you’re working with could be confidential or contain your company’s trade secrets. AI governance can help keep your company’s information safe while reducing risk, proactively defending against threats, detecting vulnerabilities, and conducting risk assessments. 

What to look for in an AI governance platform 

When looking for an AI governance platform, consider AI governance software that does more than just regulatory compliance. Yes, documentation and metadata are necessary for regulatory compliance, but your model governance tools can do so much more. 

Look for a platform that prioritizes safety, both for you and for human rights. Safety can include aspects like transparency of AI models, using appropriate sampling techniques for data sets, and monitoring risks and cybersecurity needs.

Other features to look for are an activity log and an audit trail. These are great for compliance audits and fulfilling regulatory requirements, but they’re also one of the simple things you can do for better security and a better understanding of how to improve your models. 

Some other features you should look for in an AI governance platform include:

• Integration with your existing databases and other tools
• Adoptability and ease of use. The point of AI governance software is to keep people compliant, and users are less likely to be compliant if they don’t understand how to use the software. 
• Drift, anomaly detection, and bias alerts. For AI to be ethical and accurate, you’ll need to make sure your data doesn’t have bias and isn’t drifting. Alerts can help you know when your data set and models are experiencing these. When you are alerted, you can intervene so you aren’t wasting time and resources on inaccurate models. 
• Customization. Different organizations prioritize different metrics, so you’ll want AI governance software that can be tailored to your company’s goals. You’ll also want customizable visuals for easy-to-read dashboards, relevant reports, and precise model training. 

Top 8 AI governance tools for 2025

AI governance tools are in high demand. Countries are increasingly adding laws and regulations around the use of AI, such as the European Union’s AI Act and the US’s EO 14110. Additionally, as AI continues making business more efficient, a lack of AI governance won’t just make you not compliant — it will slow down your business, make you less competitive, and leave you vulnerable to cybersecurity risks while your company wastes resources on inefficiencies.

To keep your organization both compliant and productive, here are the top 8 AI governance tools for 2025.

1. Domo

By integrating AI-powered experiences into its software, Domo makes it easier for users to register and manage external AI models securely. Data safety is top-of-mind with this platform. When leveraging OpenAI’s generative AI capabilities, Domo transmits only metadata from the tables, not the data itself, which helps companies avoid risks of data exposure. Domo also emphasizes data literacy as an important component of AI governance. Domo’s software helps establish guidelines, policies, and safeguards so organizations can create an environment where all users can get the most out of AI tools without stepping into ethical gray areas or risking the business’s data.

Pros:

• Robust visuals and easy-to-create dashboards
• Easily connects to multiple data sources with a broad range of connectors
• Great AI chat that can hold contextual conversations about data with high transparency

Cons:

• Steep learning curve
• Rigid data structures

2. Azure Machine Learning

If you’re looking for an ethical AI governance tool, Microsoft’s Azure Machine Learning is impressive. The platform was built on six fundamental principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. This is great for companies worried about the morals of AI. 

There are business benefits, too: when using Azure, users get more reliability because the platform was built with consistency in mind. Transparency means that results are easily interpretable for all stakeholders, which improves performance issues and helps users understand both the local and global impacts of the model predictions.

Pros:

• The automated ML models are fast to test, and the ML drag-and-drop interface is easy to use 
• Model training and retraining is simple and provides end-to-end support
• Works with multiple coding languages and is easy to code and integrate with other Python libraries

Cons:

• Documentation is lacking
• When it has errors or exceptions in the pipeline, it’s not good at giving reasons why, which makes it difficult to figure out what’s wrong

3. Datatron MLOps Platform

Datatron’s MLOps Platform is all about efficiency. The development-agnostic MLOps platform is designed to accelerate MLOps with real-time model performance monitoring and a variety of alerts for bias and drift. Its explainability features and observability reports are valuable parts of its unified dashboard, providing explanations that help businesses deploy models securely and at scale. As a result, your company can implement Datatron’s AI governance for faster model deployment, higher ROI, and increased productivity.

Pros:

• Very flexible; works with any IT configuration, stack, or platform
• Excellent activity log that’s helpful for regulatory audits as well as careful collaboration
• Alerts are helpful and easily customized
• Made specifically for MLOps, not DevOps

Cons:

• Slow
• Complex interface

4. DataRobot

DataRobot is a tool that helps make machine learning more accessible to more users, even those without a strong AI or technical background. It has automated machine learning capabilities to allow users to quickly build and deploy models. One key feature of DataRobot is that it offers explainability features, ensuring transparency in AI decisions, helping users understand how models make predictions and identify potential biases. By focusing on automation and ease of use while emphasizing ethical AI, DataRobot helps companies maintain compliance and avoid ethical issues while scaling AI solutions.

Pros:

• Tools that simplify model building and deployment for non-experts
• Scalable, allowing more team members to deploy AI across the business

Cons:

• Limited customization for advanced data scientists

5. Qlik Staige

Qlik Staige is great for data visualization. Staige aims to help implement generative models, better inform business decisions, and improve outcomes, which it successfully does with its extensive chat AI features. Its natural language readouts and conversation-based data are a strength, especially when it comes to explainability and identifying opportunities.

Pros:

• Capability to co-develop analyses with others
• Well-governed catalog of AWS data
• Great sentiment analysis and predictive analytics to help boost customer satisfaction from reviews

Cons: 

• Not very good filtering options
• Sharing and exporting features are rough

6. Monitaur

Monitaur is very focused on governance, making it ideal for companies in highly regulated industries. Its centrally managed library keeps everyone on the same page. To show its dedication to honest AI governance, Monitaur tracks the whole AI lifecycle, making sure it’s efficient. Additionally, Monitaur is action-oriented, helping companies identify and implement solutions that keep their AI models responsible.

Pros:

• Good for regulation; made with NAIC principles, NIST standards, ASOP standards, OCC, and more in mind 
• Centralized governance mitigates AI risks and is good at monitoring bias
• Unifies and consolidates many teams and views for easier project management

Cons: 

• Poor customer support
• Sometimes confusing to navigate; jumbled UI

7. Holistic AI

A newer player in AI governance software, Holistic AI proactively looks for upcoming regulation changes so you can implement compliance requirements early. The platform’s command center gives you a 360* view of how your AI is being used, registers AI usage and development, and controls your AI inventory. While keeping your company in compliance, Holistic AI also finds ways to improve your business, such as creating actionable risk mitigation strategies, increasing the efficiency of AI models, and automating workflows.

Pros:

• Good role-based reporting
• Business-focused approach to AI governance and internal policy creation

Cons: 

• Poor customer support and no significant community
• Lack of customization

8. Credo AI

Credo AI offers a centralized repository of AI metadata so you can gain visibility into all aspects of your AI: risk, revenue potential, impact, mitigation strategies, and more. Some of the features to note are the Policy Intelligence Packs and the Generative AI Guardrails, both of which help your organization adopt AI tools faster and be compliant from the beginning of AI implementation.

Pros:

• Integrates well with Python libraries, AWS, Google Cloud Platform, Microsoft Azure, and more
• Public cloud, private cloud, and self-hosted options
• AI Policy Management feature makes deployment faster, more consistent, and more ethical across your organization

Cons:

• Lack of documentation and training

Navigating the complex landscape of AI governance is essential for any organization looking to harness the power of artificial intelligence responsibly and effectively. By choosing the right governance platform, you can empower your teams to innovate confidently while safeguarding your business against risks and ethical pitfalls. Embracing effective AI governance today will not only enhance your operational resilience but also position your organization as a leader in responsible AI use. Learn more with Domo.