AI Governance Best Practices

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 a set of best practices and policies that build control and accountability so that an organization can implement and manage AI responsibly

AI governance can span several interconnected layers, including AI model governance, which covers the models themselves, use case governance, which includes policies and controls that dictate how and which models are used within an organization, and AI data governance, which helps organizations control and secure AI use add trust to model outputs by governing the data they can access and are trained on.  

Each of these three areas are addressed in AI governance best practices, including building a governance committee, setting clear policies, managing data responsibly, and training your teams.  
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How does AI 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 AI risks such as security vulnerabilities, biases, and operational failures.

7. Regular monitoring

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

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Why AI governance is now a C-suite priority

For many organizations, the response to the introduction of advanced AI has been as simple as it is reflexive: Adopt at all costs to accelerate innovation, or ban the technology and hope employees don’t use AI under the table.  

But without governance, both strategies have risks. Right now, Gartner predicts that 40% of agentic AI projects will be cancelled by the end of next year, citing unclear business value, risk controls and costs. Meanwhile, those that block AI rather than govern it’s use often end up with unknown risk in the form of shadow AI, used by over half of employees according to a recent survey by Okta. At these rates, the risk of sensitive customer data making its way into a large language model is uncomfortably high.

Common issues from lack of AI 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.

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AI governance best practices

1. Build a cross-functional AI governance committee

To make AI governance work, focus on inclusion over siloed governance. 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.

Who should we involve?

Start with an accountable executive sponsor with approval authority such as a chief AI officer. Include leaders from legal, ethics and compliance, privacy, information security, records and information governance, research and development, and product engineering and management.

How will we define AI systems?

The definitions and approaches required for the EU AI Act and GDPR compliance that can be extended to AI Governance offer a roadmap to define AI systems.

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 and agents?

‍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. This includes governing and controlling access to data, which is often more important than the models themselves.  

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 and extensive testing, leading to high costs.

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2. Set policies, and built them into controls that operate in daily workflows

AI policies that exist only on paper don’t protect your business. Today, auditors and regulators want to see which tools are approved, what data each use case can touch, and who signed off. Build these controls into intake, approval, and access workflows to prevent teams from bypassing them. AI policies should cover:

• Acceptable AI use: 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 testing and mitigating bias

• 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 use: Address risks associated with vendor-provided AI tools and AI features embedded in existing software

• AI agents: Define which actions agents can make autonomously, and which a person must execute and approve.
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3. Understand 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.

4. Govern the data that AI models have access to

AI systems are only as trustworthy as the data they’re trained on, retrieve from, and act on. If you don’t know what data you have, where it lives, who owns it, or where sensitive information sits, every AI initiative inherits data uncertainty, which leads to a lack of trust in outputs.

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.

Data quality and integrity

Make sure models train on and retrieve from data that’s classified, current, and compliant. This starts with mapping your entire data estate, and includes removing redundant, obsolete, or trivial (ROT) data.

Data provenance and lineage

Track where data comes from, make sure it’s classified, and track how it is processed, so you can trace any AI output back to its source and ensure its reliability.

Privacy and compliance

Enforce data minimization, anonymization, and retention policies under GDPR, the CCPA, and emerging AI regulations, and keep the evidence: audit trails and lifecycle records that show privacy by design is operating, not just written down.

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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5. Keep training continuous

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 explainbility

• 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.

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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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