The foundations of AI governance

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

What is an AI governance strategy?

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 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.

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 being ethical, transparent, and compliant with industry standards. 

Below are some of the core principles that typically 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 its quality, security, and privacy throughout the AI lifecycle.

Without it, the models could be prone to errors, which, in the case of a healthcare AI system that might make an incorrect diagnosis, could have serious repercussions and consequences.

2. Regulations

As all AI model performance must comply with legal frameworks such as GDPR, HIPAA, and industry-specific AI regulations, governance guarantees that AI follows these rules to protect users and organizations.

For instance, a financial institution must ensure its AI credit-scoring system complies with fair lending laws. Otherwise, companies could open themselves to accusations of favouritism or discrimination.

3. Ownership

Accountability gaps form in companies when there is no clear ownership or responsibility for the operation of their AI models.

For this reason, organizations should assign cross-functional teams to develop and deploy, as well as maintain, their entire artificial Intelligence usage. Retail companies do this when they appoint a specific team member to monitor how reliable it is using AI to forecast stock demand.

4. Lifecycle management

AI models go through various stages of development, training, deployment, and eventually retirement during their lifecycles. Within this timeframe, conducting proper governance makes certain that these models are regularly updated and assessed to ensure they remain effective.

A good example of this is a self-driving car’s AI model, which might undergo periodic software updates to improve its safety and quickly adapt to new traffic laws.

5. Bias

AI must be fair and unbiased, which is why organizations are obliged to actively identify and correct biases in data intelligence and algorithms.

For instance, a hiring AI system must be regularly audited to ensure they do not favor certain demographics over others. Similarly, sports betting apps must give accurate factual information based on past results and not on their personal interests.

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.

This can be evidenced by the AI fraud detection model banks use, which should undergo regular risk assessments to prevent false positives that could block genuine customer transactions.

7. Regular monitoring

For AI models to remain effective, they need to be continuously monitored, and a good way for businesses to do this is to use automated tools that can detect slips in performance and the onset of new data security threats.

For instance, a chatbot used to correspond with customers for whom English is not their first language should be continuously monitored to ensure it provides relevant and non-offensive, lost-in-translation responses.

Why does my team need an AI governance strategy?

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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.

Getting started with AI governance

So, 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?

In our view, AI governance goes beyond a simple policy. AI governance comes from a combination of:

• An AI governance committee responsible for oversight

• AI policies and procedures

• 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: 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 Global Data Protection Regulation (GDPR) compliance can be extended to AI Governance, offering a roadmap to define AI systems. You should also keep in mind new AI regulatory frameworks like the EU AI Act as a good blueprint for the future. But the most important thing? Any data containing sensitive information or IP fed into generative AI systems poses risks and must be governed.

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 like ChatGPT, and how will we support its safe use?

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?

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, extensive testing, leading to high costs.  

If we don’t want to use existing models but can’t build our own, what are our options?

A faster, more efficient approach is using solutions that enable quick chatbot creation and deployment. That’s where Rexbot comes in. With Rexbot, you can easily build an internal chatbot for any use case, from HR to sales, connect all your data sources, and search across your entire data estate with built-in access controls. Rexbot gives you the power of AI without the complexity of building from scratch — secure, scalable, and ready to use.

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

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

Data governance is the bedrock of AI governance. 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. High-quality, well-governed data is essential for building trustworthy AI. A well-designed AI committee with clearly defined AI policies can still get into trouble without well-governed data.

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, 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. You can have the best tools, but to get the benefit organizations must go through the process of training their employees on AI governance policies.  

Who needs AI training?

• Executives and leadership – High-level governance, risk management, 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.

In addition to these best practices, here are some practices that are not recommended:

• Relying on outdated or biased data.
• Ignoring ethical standards and risks until problems arise.
• Failing to assign accountability for model failures.

Key AI training areas

The foundation for your AI trainings should come from the AI governance policies and compliance frameworks you’ve established. From there, you can move on to important issues like:

• Recognizing and mitigating AI bias

• Transparency and explainability in AI decision-making

• Incident response for AI-related risks

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.  

Rex AI

We know now that developing a proprietary LLM requires massive data, extensive testing, and high costs. A faster, more efficient approach is using solutions that enable quick chatbot creation and deployment.

That’s where Rex AI comes in. With Rex AI, you can easily build an internal chatbot for any use case, from HR to sales, connect all your data sources, and search across your entire data estate with built-in access controls. Rex AI gives you the power of AI without the complexity of building from scratch — secure, scalable, and ready to use.

Rex AI is our AI-driven tool offering a natural language conversational interface for seamless interaction with the RecordPoint platform. It enables users to conduct searches and queries using everyday language, returning relevant documents and records from the RecordPoint system. 

Rex AI allows users to configure settings, customize prompts, and adjust the "strictness" of search results — giving them the flexibility to narrow results to closely matched items or broaden the scope to include a wider range of documents.

What’s more, for users with AI Governance, Rex AI offers functionality to build and test Retrieval-augmented generation (RAG) chatbots without having to deploy ChatGPT Enterprise, Copilot, or others.

Why is AI model governance important?

Without a robust model AI governance solution in place, businesses can find themselves facing serious risks, being non-compliant, and having to undertake major PR damage control. 

Amazon is one company that knows this only too well, as they were forced to scrap an AI hiring tool back in 2018 that was trained on male-dominated hiring data and, therefore, discriminated against female candidates.

Here are a few of the main issues that a lack of governance can cause.

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.

Learn more

A strong AI governance committee, well-defined AI policies and procedures, and comprehensive AI training are essential for responsible AI use. However, governance cannot succeed on policy alone — you also need the right tools to execute.  

Curious about the essential tools that serve as the building blocks for effective AI governance?

• Request early access to our new AI Governance solution — prepare and protect your data to speed up AI rollout

• Reach out to our team about Rexbot — harness the power of AI without the hassle of building from scratch.  

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 and their ongoing operations.

What is the best approach to AI governance?

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

How can organizations enhance their security posture management in AI? 

Organizations are recommended to put in place strong data security practices, monitor input data, and use governance tools to detect vulnerabilities and potential risks in their AI systems.