The best AI governance tools for your business

What’s the biggest challenge to artificial intelligence (AI) deployment? For 99% of organizations, it’s ensuring AI governance, according to a report from DLA Piper.

Many respondents experienced the same issues: bias, transparency, and compliance. They also don’t know which tools could minimize risk.

We’ve written this guide to AI governance tools to help them, by unraveling its complexities.

What exactly is AI governance?

Responsible AI governance refers to the frameworks, policies, and tools that ensure AI is being leveraged throughout a business in an ethical, transparent, and regulatorily compliant manner.

By addressing issues like fairness, bias, accountability, and security, AI governance helps organizations better manage the risks posed by AI while maximizing the technology’s untapped potential.

AI governance vs IT governance 

AI governance and IT governance are closely related but serve distinct purposes.

IT governance focuses on managing an organization’s overall technology infrastructure to ensure it's secure, efficient, and compliant. On the other hand, an AI governance solution specifically addresses the ethical, regulatory requirements and operational challenges of artificial intelligence, such as bias, transparency, and accountability.

While IT governance establishes the foundation for the secure and efficient use of technology, AI governance builds upon it to regulate AI’s decision-making processes and societal impact. 

Here is an overview of some of their key differences:

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What benefits can AI governance tools bring to my company?

Any organization that wants to use artificial intelligence responsibly would be well advised to invest in AI governance tools.

To start with, they help mitigate risks like bias, security vulnerabilities, and ethical concerns by providing companies with monitoring, auditing, and bias detection capabilities. The upshot of this is that AI systems would be better able to make fair and reliable decisions.

With global regulations like the EU AI Act and GDPR to contend with, companies can use AI governance tools to automate documentation, track model decisions, and better adhere to legal requirements.

Other benefits AI governance software tools offer companies relate to transparency and accountability because these tools can provide insights into AI decision-making.

Subsequently, it makes it easier for businesses to build trust by explaining and justifying outcomes. This is particularly critical in industries like finance and healthcare, where AI decisions have significant real-world consequences.

What should I look for in an AI governance platform?

Some AI governance platforms may be better equipped for the needs of your business than others. It’s vital to select the one that will work best for your organization.

Here are some essential and desirable features to look out for.

Must-have features:

• Bias detection and mitigation: It's vital to identify and reduce biases in AI models to verify that fair and ethical decision-making is taking place.
• Model monitoring and performance tracking: By continuously tracking AI models in real time, you will be able to detect drift, anomalies, and performance issues much more quickly.
• Risk assessment and management: It's important to be able to evaluate AI risk management concerns, such as security vulnerabilities, ethical concerns, and compliance gaps, at any given point in time.
• Compliance reporting and auditing: This function can be extremely useful as it automates reporting for regulations like the EU AI Act, GDPR, and industry-specific guidelines to confirm there is proper accountability.
• Data lineage and transparency: By tracking the origin and transformation of data, your company will be better able to certify model traceability and explainability.
• Access controls and user management: Such attributes provide role-based access to sensitive AI and ML models, which guarantees that only authorized personnel can modify or deploy models.

Nice-to-have features:

• AI model inventory management: This feature keeps a centralized catalog of all AI models, which makes governance much easier to manage across different teams and projects.
• Customizable dashboards and reporting: A good, easy-to-use, and understandable dashboard and reporting system enables organizations to see their AI performance, risks, and compliance status quickly.
• Integration with existing machine learning operations (MLOps) platforms: The ability to seamlessly connect with AI/ML tools like TensorFlow, AWS SageMaker, and Azure ML would improve your workflows.
• Alerting and notification systems: To stay on top of things, it's worth having automated alerts sent to you when governance issues, such as model drift, security breaches, or compliance failures, arise.

The more of these features you can find in an AI governance platform, the more your business’s AI usage will remain transparent, compliant, and trustworthy and minimize operational risks.

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What are the different types of AI governance tools?

AI governance frameworks are in high demand, and there are several different types available that cater to various aspects of managing AI responsibly.

Here are the four main types.

1. Dedicated AI governance platforms

These are comprehensive platforms designed specifically for AI governance, and they cover compliance, risk management, and ethical AI practices deployment. 

They are designed to help organizations set the seal on transparency, accountability, and regulatory compliance.

2. MLOps platforms with governance

MLOps platforms streamline the deployment and lifecycle management of AI models while incorporating governance features.

These tools can provide model tracking, versioning, and bias monitoring within existing machine-learning workflows.

3. Specialized LLMOps governance

Thanks to the rise of large language models (LLMs), specialized governance tools focus on monitoring and controlling LLM behavior, along with biases and security risks. 

These tools make certain that any LLM-generated content a company produces meets ethical and regulatory standards.

4. Data governance for AI systems

Since AI models rely on data, governance tools for data management are essential.

The best tools check data integrity, security, lineage tracking, and compliance with privacy laws before the data is used in AI models.

It's worth noting that as each type of AI governance tool serves a unique purpose, organizations may require a combination of them to fully govern their AI systems.

Ultimately, choosing the right tools depends on how complex the company’s AI deployments are and the compliance requirements of the specific industry in which they operate.

Key considerations before choosing an AI governance tool

If you’re considering using an AI governance tool within your business, there are a few things you should keep in mind. 

1. Identify AI applications and risks

Take the time to determine how AI is being used in your organization and assess what potential risks you might face from using it.

2. Assess data sensitivity

To get a handle on what is required for your company to practice proper governance and AI compliance, first understand the types of data your AI models process.

Personal, financial, or healthcare data, for instance, all have data sensitivities around them, which your business will need to safeguard as effectively as possible.

3. Assess model complexity and scale

Whether your AI models are simple or large-scale is something you will need to get a handle on because if it's the latter, you’ll require advanced monitoring, explainability, and risk-assessment features in your platform.

We recommend determining what your compliance requirements are and then making sure the AI governance tool supports regulations like GDPR, the EU AI Act, HIPAA, or industry-specific standards that are relevant to your business.

4. Understand user needs and skill levels

Various teams within your organization will probably use the AI governance tool, such as data scientists, compliance officers, and the IT department. So, it's a good idea to identify who they are and check that it fits their expertise and workflows.

How to choose the best AI governance tool

Knowing that your company needs an AI governance tool is one thing. But selecting one that adds value to your business is another thing entirely.

Follow this step-by-step process to find the best fit for your organization.

Step 1: Define your AI governance needs

Identify what your AI use cases are and the key risks you will need to manage (e.g., bias, security, compliance).

Next, determine if you need a dedicated AI governance platform or governance features within an MLOps tool.

Step 2: Evaluate compliance and risk management features

Check to see if the tool supports regulations like the EU AI Act, GDPR, or HIPAA that are relevant to your business.

Look for bias detection, explainability, and automated compliance reporting, as they will simplify audits.

Step 3: Assess integration and scalability

Choose a tool that integrates with your existing AI/ML stack. However, if you are considering scaling your operation, you should make sure the platform supports your current AI models and future growth plans.

Step 4: Think about user experience and accessibility

As different teams are likely to be using the tool, it's worth thinking about how easy its interface might be for them to use.

At the same time, test out the platform’s role-based access control functionality, to enable you to manage permissions securely.

Step 5: Compare costs and support

Some tools charge based on usage, while others offer enterprise licensing. Additionally, review the customer support experience and whether they have documentation and community resources for troubleshooting.

Below are some key features of a selection of popular AI governance tools.

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Summing up

AI governance tools can play a significant role in enhancing how your business’s AI activities are conducted.

In addition to ensuring they are completed in an ethical, transparent, and regulatorily compliant manner, they will also streamline your operational processes.

Looking for effective AI governance solutions? Contact our team today to learn how our platform can help you stay ahead.

FAQs

Why is AI governance important?

By practicing AI governance, companies certify that they remain compliant with AI regulations. Doing this also mitigates AI risks, promotes AI ethics, and assists companies in assessing and mitigating issues in AI applications.

How do AI governance tools help mitigate risks?

These tools provide features like risk assessments, continuous monitoring, and model governance, which can detect and address bias, security threats, and ethical concerns. They also ensure data quality and reduce risk in generative AI projects.

What role do AI developers play in AI governance?

AI developers are expected to ensure ethical AI by implementing governance insights, leveraging ML governance, and utilizing AI solutions that support AI lifecycle management and model performance tracking.

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