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Blog | Aug 30, 2023

How to Ensure AI Governance in Your Organization

How to Ensure AI Governance
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With artificial intelligence (AI) evolving rapidly, organizations utilizing these AI technologies must consider safety, security and governance above all else. Those disregarding AI governance run the risk of data leakage, fraud and bypassed privacy laws.

Any organization utilizing AI will be expected to maintain transparency, compliance and standardization throughout their processes. So, how do you ensure AI governance?

What is artificial intelligence?

AI can include a wide range of applications designed to emulate human intelligence capabilities. It learns and adapts through data analysis and pattern recognition, which allows it to perform tasks using predefined rules and algorithms.

In the realm of automation is intelligent automation (IA), which expands AI’s capabilities with several other cognitive technologies, such as robotic process automation (RPA), business process management (BPM), process intelligence, no-code development, natural language processing (NLP) and more, to automate business processes and transform how work is done. More organizations are taking an automated approach to the future of their workforce. RPA and AI together bring better efficiency, cost savings and regulatory compliance.

What is generative AI?

Generative AI generates novel content based on training data, such as images, videos and scripts. While a lot of work is still going into the security and regulations of generative AI, the technology is becoming more present in today’s market. Generative AI has many use cases, so long as it’s implemented with best practices in mind.

Find out how you can prepare for AI in our comprehensive guide.

What is AI Governance?

Artificial intelligence governance initiatives are organizations’ practices of managing and monitoring AI activities. This should include AI model documentation and auditing pipelines to show how the AI is trained and tested, and how it behaves throughout its lifecycle. Good AI governance should also outline any potential risk aspects, which should be assessed and validated before going into production.

AI governance is especially important in heavily regulated industries in both the private sector and the public sector, such as in banking and financial services, insurance and healthcare. All organizations should also have transparency in their AI models to ensure well-documented auditability, expand on their capabilities and avoid penalties.

What are the techniques of AI governance?

AI governance focuses on developing strategies to ensure the secure and effective development and deployment of AI technologies. These can include:

  • Transparency: Ensure all AI systems are documented and transparent so users and stakeholders know how decisions are made. Providing clear, well-documented results is critical, particularly for complex or highly regulated industries such as banking, where ample consideration of financial risk elements should be imposed.
  • Algorithm regulation: Your audit models should include evaluating your data testing grounds for accuracy and potential biases.
  • Ethical frameworks: Adopt ethical guidelines for how you will run your AI systems can help promote responsible behavior and ensure you’re meeting regulations within your business and from the government. These ethical rules of behavior should include informed consent, privacy protection, bias mitigation, responsible content generation, regular audits and stakeholder collaboration. Ethical practices promote a strong and trusted brand identity for your organization.
  • Legal frameworks: Understand the essential requirements of your government’s regulations in relation to AI. Establish how your governance models will reflect this. This mandate could stem from your national government, such as the U.S. federal government.
  • Auditability: Regular audits of your AI systems will help you identify risks, biases and any ethical concerns, giving you more agility to respond to changing public policy.
  • Data security: A robust enterprise data governance plan will ensure your AI models are trained in an environment with accurate and ethically-sourced data. You should also apply these same practices when choosing your LLMs (large language models). Be aware that they might operate within the public domain and rely on various underlying data sources.
  • Forecasting: Establish business outcomes and assessments for how you would like your AI systems to run can help uncover possible issues before they arise and ensure your models stay on track and improve your business functions rather than impede them.

What Are the Governance Issues in AI?

If organizations don’t adopt AI governance, they run a lot of risks.

Data quality

Machine learning (ML) requires good data. Without good data quality, the processes and decisions made on that data lead to poor or inaccurate results. Good training data and management are essential for a secure and scalable AI program.


Clear documentation will show regulators how your AI model was built and how it’s performing. Without this, your model will be difficult to track, scale or reproduce.

External risks

With a lack of governance also comes catastrophic risks such as adversarial attacks, data breaches, lack of privacy protection and infringement. This, in turn, puts your organization’s reputation at risk. It’s important to understand the dangerous capabilities of the technology as much as its benefits.

Here are some of the risk categories you should look out for:

  • Racial profiling in AI facial recognition, as an example. Biases can also stem from gender, age, culture, among others, and can lead to discriminatory outcomes.
  • Intellectual property infringement by training AI on copywritten material.
  • Penalties or fines for not adhering to government legal requirements.

What is AI Model Governance?

AI model governance is the process of how your organization controls access, establishes and implements policies and audits the AI model’s performance. It’s how you can bring accountability and transparency to your AI system.

AI model governance is crucial for building and maintaining trust in AI technologies. It ensures that AI models are developed in a responsible and accountable manner, helping to prevent potential harm and maximize the positive contributions of AI to various domains.

There are several strategic approaches organizations can employ when establishing AI governance:

  1. Development guidelines: Establish a regulatory regime and best practices for developing your AI models. Define acceptable data sources, training methodologies, feature engineering and model evaluation techniques. Start with governance in theory and establish your own guidelines based on predictions, potential risks and benefits, and use cases.
  2. Data management: Ensure that the data used to train and fine-tune AI models is accurate and compliant with privacy and regulatory requirements.
  3. Bias mitigation: Incorporate ways to identify and address bias in AI models to ensure fair and equitable outcomes across different demographic groups.
  4. Transparency: Require AI models to provide explanations for their decisions, especially in highly regulated priority sectors such as healthcare, finance and legal systems.
  5. Model validation and testing: Conduct thorough validation and testing of AI models to ensure they perform as intended and meet predefined quality benchmarks.
  6. Monitoring: Continuously monitor the performance metrics of deployed AI models and update them to adapt to changing needs and safety regulations. Given the newness of generative AI, it’s important to maintain a human-in-the-loop approach, incorporating human oversight to validate AI quality and performance outputs.
  7. Version control: Keep track of the different versions of your AI models, along with their associated training data, configurations and performance metrics so you can reproduce or scale them as needed.
  8. Risk management: Implement security practices to protect AI models from cybersecurity attacks, data breaches and other security risks.
  9. Documentation: Maintain detailed documentation of the entire AI model lifecycle, including data sources, testing and training, hyperparameters and evaluation metrics.
  10. Governance board: Establish a governance board or committee responsible for overseeing AI model development, deployment and compliance with established guidelines that fit your business goals. Crucially, involve all levels of the workforce — from leadership to employees working with AI — to ensure comprehensive and inclusive input.
  11. Regular auditing: Conduct audits to assess AI model performance, algorithm regulation compliance and ethical adherence.
  12. User feedback: Provide mechanisms for users and stakeholders to provide feedback on AI model behavior and establish accountability measures in case of model errors or negative impacts.
  13. Continuous improvement: Incorporate lessons learned from deploying AI models into the governance process to continuously improve the development and deployment practices.

While keeping all of these in mind, it may be worthwhile to consider specific governance frameworks or a set of rules to follow with your AI model to ensure best practices.

Best Practices for AI Governance

Once you’ve established clear ethical and security guidelines for your AI model, here’s what you need to do:

  1. Inform your teams. At every level and across your business, ensure everyone understands what your AI guidelines are, including privacy and compliance rules. Use accountability and oversight mechanisms and set up the kinds of roles and responsibilities your people will have so nothing is missed. Ensure everyone understands your desired business outcomes and that your teams and project stay aligned with those goals.
  2. Identify use cases. Determine where and how you’d like to use AI in your systems and how they will benefit the business. Include any potential risks and issues.
  3. Maintain the human connection. Implement comprehensive training and education within your governance structures. That goes for your human and digital workforce. Keep clear documentation and oversight to ensure everything is running smoothly.
  4. Adapt. Changes in the market, regulations, customer expectations and evolving technology will all influence your AI pursuits. Collect regular feedback from employees and customers and monitor your AI for output quality, confidentiality and efficiency.

Who is Responsible for AI Governance?

Rightly, AI governance is everyone’s responsibility in the business. Having a coherent and cohesive set of guidelines to follow will ensure regulatory compliance, security and adherence to your organization’s values. But ultimately, AI leadership will be the guiding beacon for AI governance.

Who regulates AI?

There are a few ways to establish and maintain an AI governance model:

  • Top-down: Effective governance requires executive sponsorship to improve data quality, security and management. Business leaders should be accountable for AI governance and assigning responsibility, and an audit committee should oversee data control. You may also want to appoint a chief data officer from someone with expertise in technology who can ensure governance and data quality.
  • Bottom-up: Individual teams can take responsibility for the data security, modeling and tasks they manage to ensure standardization, which in turn enables scalability.
  • Modeling: An effective governance model should utilize continuous monitoring and updating to ensure the performance meets the organization’s overall goals. Access to this should be given with security as an utmost priority.
  • Transparency: Tracking your AI’s performance is equally important, as it ensures transparency to stakeholders and customers, and is an essential part of risk management. This can (and should) involve people from across the business.

Key Takeaways on AI Governance

Well-planned governance strategies are essential when working with the ongoing evolution of artificial intelligence. Ensure your organization understands the legal requirements for using these machine learning technologies. Set up safety regulations and governance policy regimes to keep your data secure, accurate and compliant.

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