The foundations of AI governance

Key Takeaways

• Any AI governance strategy must integrate data governance from the start.

• Form a cross-functional AI governance committee with oversight and accountability.

• Establish clear AI policies covering ethics, compliance, and regulatory frameworks.

• Manage AI lifecycle with governance across development, deployment, and retirement.

• Implement robust risk controls to reduce bias, security gaps, and operational failures.

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With the growth of advanced AI, companies have been struggling to govern the technology and reduce risk. Learn the key elements of a strong AI governance function and how to implement them in your organization.

Get the AI governance committee checklist

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What is an AI governance strategy?

AI governance is the framework that outlines how organizations manage AI systems responsibly. While AI model governance is the part of AI governance that explicitly focuses on how businesses can incorporate AI and machine learning models within their operations as safely and responsibly as possible.

In this guide, we’ll cover the four pillars of AI governance: building a governance committee, setting clear policies, managing data responsibly, and training your teams. You’ll also get answers to all your burning questions and tools for supporting safe AI use.  
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How does AI model governance work?

For AI model governance to stick, businesses need to implement policies, processes, and controls that certify their artificial intelligence systems as ethical, transparent, and compliant with industry standards.

Below are some core principles that guide AI model governance and how they function in different industries.

1. Data governance

AI models rely on vast amounts of data to perform their functionality, and trusted data governance helps maintain data quality, security, and privacy throughout the AI lifecycle.

Without it, models can be more prone to errors or expose sensitive information that could have serious consequences for businesses.

2. Regulations

AI model performance must comply with legal frameworks such as the Global Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and industry-specific AI regulations. Governance guarantees that AI follows these rules to protect users and organizations.

3. System ownership

Accountability gaps can form in companies with a lack of clear ownership or responsibility for the operation of their AI models. Organizations should assign cross-functional teams to develop, deploy, and maintain their AI usage.

4. Lifecycle management

AI models go through various stages during their lifecycles, including development, training, deployment, and eventually retirement. Proper governance ensures these models are regularly updated and assessed.

5. Bias

AI must be fair and unbiased. Organizations are obliged to actively identify and correct biases in data intelligence and algorithms.

6. Managing risk

Model AI governance practices provide organizations with a method of assessing and countering risks such as security vulnerabilities, biases, and operational failures.

7. Regular monitoring

For AI models to remain effective, they need continuous monitoring. Automated tools can detect slips in performance and new data security threats.

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Why does my team need an AI governance strategy?

For many organizations, the response to the introduction of advanced AI has been as simple as it is reflexive: ban the technology and hope nobody finds their way to the platforms on their own.  

While this makes for an easy-to-communicate policy, it's not very effective. Telling people not to use technology that the rest of the world – particularly the people filling their LinkedIn feeds – is telling them is essential, just leads to people finding workarounds.

According to a study from Software AG, half of all employees are using “Shadow AI” (unsanctioned AI), with those who do citing a quest for productivity gains, a desire to be independent, and the fact that their employers are not offering the tools they need.  
Another survey from Cyberhaven shows that 73.8% of workplace ChatGPT usage occurred through public, non-corporate accounts, and the numbers were higher for Gemini (94.4%) and Bard (95.9%).  

This pattern is reminiscent of the growth of “Shadow IT”, where employees, who in the age of App Stores have grown used to downloading and using apps in their personal lives, use unsanctioned hardware or software (most often cloud-based SaaS) to get their work done.  

Like with Shadow IT, Shadow AI brings a host of potential issues, primarily privacy and security-related. Data entered into a consumer version of platforms like ChatGPT may be used to train their models. With employees seeking efficiency gains, and without a coherent policy to guide them, the risk of sensitive customer data making its way into a large language model is uncomfortably high.

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Getting started with AI governance

We’ve established you need an approach beyond “don’t use AI,” and some form of AI governance in your organization. The next question is logically: what form should it take?

To implement AI governance, organizations need:

• An AI governance committee responsible for oversight.

• AI policies and procedures built around AI governance frameworks.

• Data governance for AI.

• AI training

Let’s dive into each of these elements.

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Building an AI governance committee  

To unlock AI governance, you need to focus on inclusion over isolation, bringing your team along for the ride. Declarations from leadership are typically ineffective and can lead to alienated employees finding their own tools, putting your organization and its customers’ data at risk.  

A more effective way to build a sense of inclusion, along with oversight, alignment with regulatory requirements, and ethical AI use, is with an AI governance committee. Indeed, a well-structured AI governance committee is the backbone of responsible AI deployment.

Today, large technology companies like Microsoft and Meta are establishing internal AI committees, often referred to as "AI Ethics Boards" or similar, to review and oversee the development and implementation of their AI technologies.

Key questions when forming your committee

Who should we involve?

Our take: A well-structured AI governance committee is the backbone of responsible AI deployment. Consider including representatives from key functional areas like legal, ethics, compliance, privacy, information security, and product engineering and management.

Include representatives from key functional areas of the organization with diverse experience. Consider leaders from teams like legal, ethics and compliance, privacy, information security, research & development, and product engineering and management. With innovation at a rapid pace, it truly takes a village.

How will we define AI systems?

Our take: The definitions and approaches required for GDPR compliance can be extended to AI Governance, offering a roadmap to define AI systems. You should also keep in mind newer AI regulatory frameworks like the EU AI Act as a good blueprint for the future.  

How will we define risk levels?

Our take: As a starting point, keep in mind that any data containing sensitive information or IP fed into generative AI systems poses risks and must be governed accordingly. There is also an AI risk classification system outlined in the EU AI Act, which is a great place to start when defining AI risk across your organization.

How will we ensure human oversight for high-risk systems?

Our take: Prohibit banned AI systems and set processes for evaluating all other risk categories. Consider leveraging Third-Party Risk Management (TPRM) tools and AI-specific extensions to assess AI-linked risks with privacy, security, and ethics standards. Although TPRM tools are automated, human review ensures flagged risks can be properly addressed.

What’s our stance on generative AI?

Our take: We support the use of proprietary AI systems, including generative AI, provided the systems undergo thorough vetting and have guardrails to mitigate known risks. Through our new AI Governance solution, we prepare and protect your data for accelerated AI system rollout. With this solution, you can power responsible AI with clean, compliant, unbiased data, ensuring you get value from AI safely.

Should we use existing AI options or build our own?

Our take: Another option is building your own large language model (LLM). In a survey of 1,300 enterprise CEOs, 51% said they were planning to build their own generative AI implementations, leveraging foundational models such as ChatGPT, Claude and Llama and extending them into their particular domain, industry, and expertise.  

But this comes with significant challenges — developing a proprietary LLM requires a massive amount of data and extensive testing, leading to high costs.

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AI policies and procedures

Comprehensive AI policies provide the framework for responsible AI usage within an organization.

Key areas to address in AI policies

• Acceptable AI usage – Define permissible AI use cases and restrictions

• Data management and security – Require AI models to use high-quality, compliant data

• Bias and fairness – Establish guidelines for mitigating bias and ensuring fairness

• Transparency and explainability – Require AI models to be interpretable and auditable

• Human oversight – Define human-in-the-loop requirements for AI decisions

• Third-party AI usage – Address risks associated with vendor-provided AI tools
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Understanding AI governance frameworks

An AI governance framework is a playbook for managing AI from start to finish. It helps organizations stay compliant with rules like the EU AI Act and NIST AI Risk Management Framework (AI RMF), while embedding responsible AI practices in everyday work, not just on paper.  

To do that effectively, a strong AI governance framework needs to cover five core pillars: visibility, risk management, policy enforcement, compliance alignment, and lifecycle oversight.

The table below maps the most common AI governance framework components to the pillar they support.

Data governance for AI – the foundation for all AI policies and procedures

Data governance is the foundation of AI governance. High-quality, well-governed data is essential for building trustworthy AI.

Just as data professionals follow the principle of "garbage in, garbage out," AI systems are only as reliable and secure as the data they are trained on.  

Implementing key data governance principles with RecordPoint

Data quality and integrity

Ensure AI models use compliant, safe data. RecordPoint’s intelligence engine prioritizes data integrity, offering diverse content classification options while ensuring complete security and confidentiality throughout the training process.  

Data provenance and lineage

Track where data comes from and how it is processed. Powerful data discovery and classification features enable you to track the sources and origins of your data, ensuring that data is reliable .

Privacy and compliance

Enforce data minimization, anonymization, and retention policies. Proactively manage data to ensure compliance with the GDPR, California Consumer Privacy Act (CCPA), and emerging AI regulations. Track data sources, maintain audit trails, and create structured review processes to manage risk.

Access control and security

Restrict who can access and build safe data sets for AI. Apply granular access controls and enforce least privilege principles, ensuring only authorized users can access sensitive data.

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AI training  

For AI governance to be effective, employees must be well-trained on AI risks, policies, and best practices.

Who needs AI training?

• Executives and Leadership: High-level governance and ethical considerations

• Developers and Data Scientists: Technical compliance, bias mitigation, and explainability

• Business and End-Users: Understanding AI-assisted decision-making and responsible AI use

• Legal and Compliance Teams: AI risk assessment, regulatory compliance, and audit readiness

Seven AI model governance best practices to teach

If your company wants its AI systems to be ethically responsible, transparent, and compliant, there are some best practices everyone should be aware of.

1. Regularly audit models for bias: Conduct frequent fairness checks to identify and mitigate discrimination in AI decisions.

2. Establish a clear chain of responsibility: Assign ownership to a specific team or AI Ethics Officer to oversee governance and compliance.

3. Use explainable AI (XAI) tools: Implement transparency measures to ensure AI decisions are understandable and justifiable.

4. Maintain strong data governance: Ensure data quality is high, unbiased, and securely managed with access controls and encryption.

5. Implement continuous monitoring: Use automated tools to detect performance drift, security vulnerabilities, and ethical AI concerns.

6. Test models in real-world scenarios: Validate AI outputs in practical situations before full deployment to avoid unforeseen risks.

7. Ensure compliance with legal regulations: Stay updated on evolving AI laws such as GDPR, ensuring all models meet current requirements.

RecordPoint’s two-pronged AI governance solution

Many AI projects are stalled right now, or will be soon — nearly a third, according to Gartner – but they don’t have to be. The key to unlocking their potential lies in strong AI governance. By implementing the right policies, data controls, and oversight, organizations can move AI initiatives forward with confidence. Here’s how RecordPoint can help.

AI governance

Through our new AI Governance solution, we prepare and protect your data for accelerated AI system rollout. With this solution, you can power responsible AI with clean, compliant, unbiased data, ensuring you get value from AI safely.    

RexAI

Building a proprietary large language model (LLM) requires massive volumes of data, extensive testing, and significant cost. For most organizations, a faster and more practical approach is using an AI solution that enables secure chatbot creation and deployment without building everything from scratch.

That’s where RexAI comes in.

RexAI is RecordPoint’s AI-powered conversational interface, designed to help users search, retrieve, and interact with governed information using natural language. Users can ask questions in everyday language and receive relevant documents and records from across the RecordPoint platform.

Rex AI includes built-in access controls, so users only see the content they’re authorized to access.

Key RexAI capabilities

With RexAI, organizations can:

• Build internal chatbots for use cases like HR, compliance, legal, and sales

• Connect multiple data sources and search across their information estate

• Customize prompts and settings, including adjusting the strictness of search results

• Build and test Retrieval-Augmented Generation (RAG) chatbots without deploying ChatGPT Enterprise, Copilot, or other external AI tools

RexAI gives teams the benefits of AI-powered chat and search while keeping governance and security intact.

Why is AI model governance important?

Without a robust model AI governance solution, businesses face serious risks, non-compliance, and potential reputational damage.

Common issues from lack of governance

Data bias

Bias in AI can result in unfair outcomes, with the fields of hiring, lending, and law enforcement being especially susceptible. Typically, if there is a lack of oversight, biased training data can go undetected and lead to discrimination.

Legal and regulatory requirements

These can arise when AI models fail to comply with sensitive data protection regulations and anti-discrimination laws. Under such circumstances, it can potentially expose companies to lawsuits and hefty fines.

Reputational damage

If an AI system produces unethical or incorrect results, it may lead to a public backlash and unwanted headlines in the media. The effects can be severe and could possibly lead to loss of customers and revenue.

FAQs

What is an AI risk management framework?

An AI risk management framework is a structured approach to identifying, assessing, and mitigating risks in AI systems. It helps organizations implement responsible AI governance by ensuring minimal risk in model deployment.

What is an AI governance framework?

An AI governance framework is a playbook that helps direct your organization on managing AI across its entire lifecycle. These frameworks help organizations stay ahead of compliance obligations under emerging regulations.

What is the best approach to AI governance?

The best approach to AI governance involves setting clear policies, using governance platforms and tools, and ensuring compliance with guiding principles set by regulatory bodies like the Personal Data Protection Commission and development authorities.

What should be included in AI policies?

AI policies should address acceptable usage, data management, bias mitigation, transparency, human oversight, and third-party AI usage.

How often should AI models be audited?

AI models should be audited regularly, ideally on a quarterly basis, to ensure compliance and mitigate bias.

How can AI governance support business objectives?

Effective AI governance aligns AI initiatives with business goals, ensuring responsible use while maximizing the benefits of AI technology.

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