AI governance: guide to frameworks and best practices

As isolated AI experiments continue to move towards organisation-wide adoption, there is still a large gap between AI implementation and meeting AI governance best practices. 

Although 76% of companies with dedicated AI strategies recorded having management in place, only 41% made their AI policies known to employees. And a shocking 97% of companies did not consider the environmental impact of their AI systems. 

With this in mind, organisations now face a critical need to implement governance successfully as part of their wider AI strategy. 

AI governance in a nutshell

• AI governance establishes how an organisation develops, purchases, deploys, and monitors AI.

• Organisations implementing Microsoft Copilot, ChatGPT Enterprise, Gemini or internal LLM solutions often discover that governance challenges emerge long before technical deployment is complete.

• Questions around data access, acceptable use, accountability and regulatory compliance typically require cross-functional oversight.

• Frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework provide structured approaches to managing AI risk.

• Regulation is increasing, with the EU AI Act introducing requirements according to the risks posed by different AI applications.

• Training helps technical, legal, risk, and business teams understand their responsibilities when working with AI

What is AI governance?

AI governance is the system of policies, responsibilities, controls and processes used to direct and oversee the development, deployment and use of artificial intelligence within an organisation.

Good governance defines which AI systems can be used, who is responsible for them, and what standards they must meet. This can include requirements around data, security, privacy, transparency, human oversight, risk assessment, and ongoing monitoring.

Governance therefore applies beyond AI development teams. Legal, compliance, cyber security, procurement, HR, and business leaders may all have responsibilities depending on how and where AI is used.

Why AI governance matters

Without governance, organisations can struggle to understand which AI is being used, which information it accesses, and who is accountable when something goes wrong.

Effective AI governance provides a structure for managing these issues while supporting innovation. It helps businesses establish acceptable use cases for AI, identify higher risk applications, and make informed decisions about where additional controls are necessary.

Beyond risk reduction, effective AI governance should accelerate AI adoption by providing employees and stakeholders with clear guidance on how AI should be used.

Well-defined governance frameworks can improve confidence in AI initiatives, support regulatory compliance, reduce implementation delays, and help organisations scale successful AI use cases more consistently.

The growing need for responsible AI

Businesses may use AI to analyse data, create content, assist recruitment, interact with customers, support software development, or inform important decisions.

Governance helps ensure these use cases remain appropriate and controlled as adoption grows. The risks of AI use can include, but are not limited to:

• Inaccurate or unsourced AI outputs

• Bias and discriminatory outcomes

• Privacy and confidential-data exposure

• Cybersecurity vulnerabilities

• Intellectual property concerns

• Insufficient transparency or explicability

• Regulatory non-compliance

• Over-reliance on automated decisions without a “human in the loop”

For businesses, AI governance is therefore about more than reducing regulatory risk. It may also establish greater confidence in which systems are suitable for enterprise use and how employees should use them.

A recent IBM report also found that 69% of workers were more likely to accept a role with a socially responsible organisation, showing the importance of transparency and trust from AI policies. 

AI governance and generative AI

Generative AI tools such as Microsoft Copilot, ChatGPT, Gemini and Claude have accelerated AI adoption across organisations. While these tools can improve productivity and support innovation, they also introduce governance challenges around data handling, accuracy, intellectual property, accountability and acceptable use.

Implementing governance helps organisations establish clear policies for the use of generative AI, including who can access AI tools, what information can be entered into prompts, how outputs should be reviewed, and where human oversight is required.

As employee use of AI becomes more widespread, governance plays a critical role in balancing innovation with security, compliance and risk management.

AI governance frameworks and standards

Organisations do not need to build an AI governance model from scratch. Several established frameworks and standards provide practical foundations.

ISO/IEC 42001

ISO/IEC 42001 is the international standard for Artificial Intelligence Management Systems (AIMS). It specifies requirements for establishing, implementing, maintaining, and continually improving an AIMS and it is designed for organisations that develop, provide, or use AI products and services.

It gives businesses a structured management-system approach to AI risks and opportunities, incorporating areas including accountability, transparency, and responsible AI use.

OECD AI principles

The OECD AI Principles were adopted in 2019 and updated in 2024 to reflect developments including generative and general-purpose AI. They comprise five values-based principles alongside five recommendations for policymakers and AI actors, with an emphasis on innovative and trustworthy AI that respects human rights and democratic values.

EU AI Act

The EU AI Act establishes legally-binding requirements for organisations deploying certain AI systems in the European Union. The Act entered into force in August 2024 and became broadly applicable on the 2nd of August 2026, although different provisions of the Act have separate implementation dates. Following amendments introduced through the 2026 AI Omnibus, some requirements for high-risk systems have later deadlines. 

Businesses should therefore identify whether their AI activities fall within the Act and understand their obligations with regard to their role and use cases.

AI Risk Management

The US National Institute of Standards and Technology’s AI Risk Management Framework (AI RMF) provides another widely applicable model. It is voluntary and organises AI risk management around four core functions: Govern, Map, Measure and Manage. NIST also provides a dedicated profile addressing risks associated with generative AI. 

Other relevant standards include ISO/IEC 23894 for AI risk management and ISO/IEC 42005 for AI system impact assessments. The appropriate combination will depend on an organisation’s sector, jurisdictions, and applications.

How to implement AI governance frameworks

A framework only becomes useful when it translates into day-to-day business processes. Implementation should typically include several interconnected elements.

1. Establish AI governance policies

Define acceptable and prohibited AI uses, requirements for approving new systems, and rules covering areas such as sensitive data, external AI tools, and human oversight. Maintaining an inventory of AI systems may also help organisations understand where AI is already operating.

2. Define roles and responsibilities

Assign clear ownership. Senior leaders may set risk appetite, while tech, security, legal, compliance, and business teams provide area-specific oversight. Individual AI systems should also have identifiable owners rather than accountability being left undefined.

3. Assess and monitor AI risk

Assess systems according to factors such as their purpose, data, affected users and consequences of failure. Higher-risk systems can then receive proportionally stronger controls, testing, and human oversight.

4. AI governance maturity

AI governance requirements often evolve alongside AI adoption. Organisations experimenting with a small number of AI tools may initially focus on acceptable use policies and employee awareness. As AI becomes embedded within products, decision-making processes and business operations, governance typically expands to include formal risk assessments, oversight committees, monitoring processes and management system approaches such as ISO/IEC 42001.

AI governance best practices

While governance frameworks vary between organisations, several best practices are consistently recommended:

• Establish clear ownership and accountability for AI systems.
  Document approved AI use cases and acceptable use policies.
• Conduct risk assessments before deployment.
• Maintain appropriate human oversight for higher-risk applications.
• Monitor AI systems on an ongoing basis.
• Review governance processes regularly as regulations and technologies evolve.
• Provide training to employees using AI within their roles.

AI governance training and certifications

Developing an AI governance framework requires more than technology controls. Legal teams, risk professionals, leaders, data specialists and AI practitioners all need a shared understanding of governance principles, regulatory obligations and organisational responsibilities.

Training can help build this capability and support more consistent AI decision-making across the business.

QA provides training across a wide range of AI certifications in AI security and governance, including the Certified Artificial Intelligence Governance Professional (AIGP), which covers AI law, risk management, and trustworthy AI across its lifecycle.

QA also offers Certified ISO/IEC 42001 Lead Implementer training for professionals responsible for implementing and continually improving an AI management system.

The future of AI governance

Emerging technologies such as agentic AI, autonomous systems and increasingly capable foundation models may require organisations to revisit existing governance approaches.

As AI systems gain the ability to make decisions, interact with external systems and complete complex workflows, governance frameworks will need to evolve to provide appropriate oversight, monitoring and accountability.

This increases the importance of controls around matters such as permissions, auditability, monitoring, escalation, and human intervention. Businesses that establish clear standards, responsibilities, and skills now will be better placed to deploy AI consistently without losing oversight as usage expands.

Looking build AI governance capability in your organisations? Talk to our AI governance experts today to find out more