What is effective AI Governance, and why does it matter?

This blog has been expertly reviewed by Jason Vigus, Head of Portfolio Strategy, Commercialisation and Governance at Nasstar.

While AI can substantially improve how we access knowledge, improve efficiency, and accelerate innovation, it can also bring challenges. Among the most pressing are data privacy concerns, unintended bias, and unreliable outputs. Removing, or merely minimising these risks has a huge impact on how effectively we can benefit from AI. That’s where AI governance comes in.  

Using governance frameworks to mitigate risks and promote ethical AI practices, we can build systems that bring trustworthy and beneficial results. At the same time, the governance of AI reduces potential harm to others. In this blog, we’ll look at AI governance, the pillars of a firm AI policy, and how your business can use AI responsibly. 

AI governance explained 

‘AI governance’ is an overall term covering the frameworks that make sure AI systems are used responsibly. In many cases, it also checks that AI complies with regulatory requirements.  

Effective AI governance helps businesses and policymakers: 

• Establish AI guardrails, by using policy to set guidelines for AI development and use. 
• Address concerns related to AI safety, misuse, and bias. 
• Comply with data protection laws and regulatory standards, such as GDPR and the EU AI Act. 
• Build trust by promoting transparency and accountability in their AI decision-making. 
• Create governance structures for auditing AI models and assessing their real-time impact. 
• Simultaneously, it aims to ensure AI models are aligned with business goals and deliver the intended outcomes that maximise the benefits of AI’s promise. 

Jason Vigus, Head of Portfolio Strategy, commercialisation and Governance at Nasstar, said: "Without effective AI governance, businesses risk facing legal, reputational, or operational challenges. Instead, a structured AI governance framework gives transparency over results and decisions. It also allows for accountability of actions and compliance with AI regulations. Combined, this helps organisations build trust and use AI to its full potential."

What is the AI governance lifecycle? 

AI systems progress through various stages of development. As such, the AI governance lifecycle needs to align with these phases to help develop, deploy, and manage AI systems responsibly.  

While AI projects vary in their aims and methods, most will take into account some or all the following governance stages: 

1. AI policy development to define the objectives, scope, ethical considerations, and legal compliance measures required. 
2. Data collection and pre-processing to ethically gather and produce high-quality, unbiased datasets that align with AI regulations. 
3. AI model development to create and train AI models that ensure transparency and fairness, while using risk management techniques to minimise AI bias and errors. 
4. Verification and validation to test AI models and ensure they meet the required standards and perform as expected. This stage includes bias detection and performance evaluation techniques. 
5. Deployment of AI solutions in real-world environments. Ongoing evaluation of the system is established to ensure it operates within ethical and legal boundaries. 
6. Ongoing operation and monitoring to ensure the AI system remains compliant with ethical guidelines and performs reliably. Additionally, continuous improvement takes place to updating AI governance frameworks in accordance with emerging AI risks and technological advancements. 

Why is AI governance needed? 

"Good AI governance turns uncertainty into trust, and potential into progress." said Jason. 

AI governance is becoming more and more crucial. This is especially true as the future of AI brings more advanced systems, including agentic AI and generative AI, that directly impact billions of lives.  

But no matter the type of AI initiative, governance is concerned with ensuring AI is operated safely, ethically, legally and helps to manage risks. These could be associated with machine learning, agents, automation, AI-powered decision-making, or any other kind of AI solution.  

Without proper governance during the creation and use of AI models and systems, it’s possible to encounter various issues: 

• Without careful data preparation, training, and AI audits, algorithms can reinforce bias. In the real world, this can lead to unfair outcomes. Poor data quality, incomplete datasets, and unverified AI tools can all potentially create models that discriminate against certain groups.  
• Likewise, AI systems can process enormous amounts of personal data. This raises massive concerns around privacy and data protection. Regulations such as GDPR have mandated strict controls on AI-driven data processing for years, but may not be updated frequently enough to cover the newer technologies in development. These safeguards help developers comply with legal frameworks and ethical AI principles. 
• For companies, poorly designed AI systems can result in major business disruptions. For example, imagine incorrect AI-driven financial decisions - who is to blame? We could also see this with healthcare diagnoses or misinformation causing real-life personal, reputational, and legal damage. Responsible AI governance structures help provide oversight and reduce risk exposure. 
• As organisations use AI applications in increasingly high-risk areas, like cyber security and finance, there’s increased pressure to produce reliable results. Governance helps monitor AI results to check for any potentially harmful output. 

Why is AI so hard to regulate? 

If AI governance is so obviously necessary, why isn’t it in place across the board? Fundamentally, regulating AI presents several challenges. For instance, AI policies vary across geographies and jurisdictions - making compliance complex for multinational businesses. 

Likewise, there is a lack of standardised metrics. Currently, no universal benchmarks exist to measure key factors like AI fairness, transparency, or safety. At the same time, AI is still a new technology. Some AI models, including generative AI, produce unexpected outputs, making oversight even more challenging. 

There is also the concern that comes with having too much oversight. Overregulation may slow AI advancement, making its case less compelling to boards and IT teams. For this reason in particular, businesses should find a balance - implementing flexible governance structures that promote high-quality models and adapt to AI regulations. 

So, how can this be done? 

What are the pillars of an AI governance framework? 

A strong AI governance framework is built on several key principles. Each of these contributes to a framework’s overall effectiveness. 

Explainability 

Firstly, AI models must produce understandable insights into how they make decisions. In other words, it’s important to design AI systems that allow users to understand their reasoning.  

Doing so helps build trust, making sure all key stakeholders can verify the AI-driven outcomes. Explainability is especially vital in high-risk, potentially harmful applications like law, healthcare, and finance. 

Auditability 

Next, AI systems should allow for independent audits. Usually, these would assess compliance with ethical and regulatory standards.  

To achieve this, businesses should implement logging and documentation that track AI developments and behaviour. Regular audits can help identify inconsistencies and biases, while checking that AI models operate within ethical and legal requirements. 

Transparency 

Organisations should be held accountable for how their AI tools process data, make predictions, and generate outputs. This requires open communication - be it about data sources, training methods or algorithmic processes. Designing for transparency helps reduce risks and lets stakeholders understand how AI-driven decisions are made. 

Ethical considerations 

When used in the real world, there is a responsibility to reduce potential harm. That means considering both micro and macro challenges. For example, proper AI governance can help address potentially harmful biases within datasets before training begins. 

Bias and fairness mitigation 

Tackling bias isn’t optional, it’s essential. Fairness starts with diverse data, rigorous testing, and transparency in how decisions are made. Regular audits help catch hidden biases before they cause harm. The key is human oversight, ensuring AI supports inclusivity rather than reinforcing discrimination. When fairness is built in from the start, AI becomes a force for good - trustworthy, ethical, and truly unbiased. 

Human oversight and intervention 

AI is powerful, but it should never run unchecked. Human oversight ensures AI makes decisions that align with ethics, fairness, and common sense. It’s about having the right balance – letting AI handle the heavy lifting while people step in when judgment, empathy, or accountability is needed. Clear intervention points prevent AI from making harmful mistakes, whether in hiring, healthcare, or finance. The goal isn’t to slow AI down, instead the goal is to keep it trustworthy, responsible, and working for good. 

Data governance, security and privacy 

Finally, many AI solutions are designed to interact with sensitive data. So, it is critical to enforce strict controls over the collection, processing and usage of this data, ensuring only those that should have access to the data do, and that data is held securely and in accordance with all required regulatory requirements. 

What are some examples of AI governance? 

In an attempt to enforce AI governance across industries and borders, many bodies have begun to introduce AI frameworks, regulations, and acts. Some prominent examples of these include: 

• OECD AI Principles, a global framework that promotes trustworthy AI development and accountability. 
• The EU AI Act, a legal framework that aims to classify AI risk levels and sets guidelines for AI development and use. 
• GDPR, while not specifically focused on AI, GDPR is applicable as an EU law which enforces strict data protection and privacy requirements, with strong consequences for how systems (including AI) handle personal data. 

Today, any business adopting AI governance frameworks can use these regulations to guide their compliance efforts. 

How to create effective AI governance 

Any organisation looking to implement AI governance should focus on these key actions: 

1. Run AI audits and risk assessments that evaluate AI systems for fairness, bias, and compliance. 
2. Create accountability structures that clearly assign governance responsibilities, giving set roles for oversight and transparency. 
3. Write ethical AI principles that define how AI policies align with organisational values - and how these match regulatory requirements. 
4. Look to create explainability in AI models to provide insights into the decision-making process. 
5. As time goes by, update AI governance policies to make sure governance structures align with changing AI regulations and business needs. 

How Nasstar can help 

AI has limitless potential and a broad variety of use-cases for improving how we work. But a key step in its implementation is using a robust AI governance framework. Building systems this way helps make efficient and productive systems - all while promoting explainability, fairness, and compliance. 

At Nasstar, our AI & automation solutions include strong AI governance considerations, tailored to your business, by design. This helps make sure any advancements are considered from a range of key perspectives - including AI performance, ethical considerations, and regulatory requirements. 

Speak to a Nasstar AI expert to see where AI could take your business.