AI Governance

AI governance is the framework a business uses to manage how AI is selected, developed, deployed and used. It can cover:

• Policies
• Responsibilities
• Risk management
• Data
• Security
• Privacy
• Human oversight
• Testing
• Monitoring
• Transparency
• Accountability
• Regulatory requirements

In practice, governance should help a business answer practical questions such as:

• Who is allowed to use AI?
• What information can they put into AI systems?
• Which AI applications can be used?
• Who approves AI systems before deployment?
• What happens when an AI system makes a mistake?
• How are AI systems monitored?
• Who is responsible for the outcome?

AI governance isn't about stopping people from using AI. It is about creating a framework in which AI can be used appropriately.

AI can introduce new types of business risk because it can process information, generate content, influence decisions and increasingly take actions. Potential areas of concern include:

• Sensitive or confidential information
• Personal data
• Incorrect or misleading outputs
• Bias and unfair outcomes
• Intellectual property
• Security
• Lack of transparency
• Uncontrolled employee use of AI
• Third-party AI providers
• Automated decision-making
• Regulatory requirements
• Reputational risk

Governance becomes particularly important when AI moves beyond experimentation and into business-critical processes.

Compliance is one part of AI governance, but governance is broader. Good governance also helps businesses:

• Decide which AI use cases are appropriate
• Prioritise AI investment
• Define responsibilities
• Establish approval processes
• Manage suppliers
• Protect business information
• Create consistent AI practices
• Give employees clear guidance
• Scale successful AI initiatives

Good AI governance should enable responsible AI adoption, not simply create more bureaucracy.

There is no single AI governance framework that fits every organisation. A typical framework may include:

Different AI use cases create different levels of risk. An internal AI tool helping employees summarise documents may present different risks from an AI system involved in significant customer decisions. Governance should therefore be proportionate.

This is a conceptual model, not a legal classification. The actual requirements depend on the organisation, the use case, the jurisdiction and the applicable rules.

Employees may already be using public AI tools without formal organisational approval. Examples include:

• ChatGPT
• Generative AI writing tools
• AI meeting assistants
• AI research tools
• AI image generators
• AI browser tools

The concern is straightforward: employees may enter confidential information, customer data or intellectual property into external systems without realising the implications.

Simply banning AI may not address the underlying issue — people use these tools because they help them work. A better governance approach can include:

• Clear acceptable-use policies
• Approved tools
• Employee guidance
• Training
• Data handling rules
• Appropriate monitoring
• Easy routes for requesting new AI tools

Data is central to AI governance. Businesses should consider:

• What information is being used?
• Where is it stored?
• Who can access it?
• Is it personal or confidential?
• Is it being sent to a third-party AI provider?
• How long is it retained?
• Is it accurate?
• Can it be used for the intended purpose?
• What happens to data generated by the AI system?

Data governance and AI governance often overlap — the rules about what data the business holds, who can use it and how it is protected apply directly to AI systems.

AI systems can create additional security considerations, particularly when they have access to business systems or can take actions. Areas to consider include:

• Identity and access
• Permissions
• API access
• Data security
• Third-party providers
• Prompt and input handling
• Output handling
• Monitoring
• Logging
• Human approval
• System boundaries

For AI agents, governance should consider exactly what an agent is permitted to do — which systems it can access, which actions it can take and where a person must approve its work.

Responsible AI is the practical approach to designing and using AI in ways that consider its impact on people and the organisation. Areas can include:

• Fairness
• Transparency
• Accountability
• Privacy
• Safety
• Security
• Human oversight
• Reliability

Responsible AI principles need to become practical business processes rather than simply statements in a policy document.

AI regulation is developing internationally, and requirements vary depending on the organisation, the technology and the jurisdiction. For UK businesses, governance may intersect with existing requirements around areas such as:

• Data protection
• Privacy
• Employment
• Consumer protection
• Financial services
• Sector-specific regulation
• Information security

Regulatory requirements depend on your organisation, the AI system, how it is used and the jurisdictions involved. Specialist legal or regulatory advice may be required for specific situations.

This page is general guidance, not legal advice.

An AI governance consultant helps an organisation understand its current AI use, identify risks and establish practical governance. Typical AI governance consulting work includes:

Governance work often sits alongside a wider AI strategy or AI transformation programme.

You may need to consider formal AI governance if:

• Employees are already using AI extensively.
• You are introducing AI into customer-facing processes.
• AI is being used with sensitive information.
• AI is being integrated with core business systems.
• You are developing AI products.
• AI is influencing important business decisions.
• You are deploying AI agents.
• You operate in a regulated industry.
• You are scaling AI across multiple departments.
• Different teams are adopting AI independently.

For smaller or lower-risk use cases, governance may initially be relatively simple. The appropriate level depends on the business and the use case.

Start by understanding what is actually happening. Ask:

• Where is AI being used today?
• Who is using it?
• What information is being put into AI systems?
• Which AI tools are approved?
• Which processes depend on AI?
• What happens when AI gets something wrong?
• Who is responsible for AI decisions?
• Which AI projects are planned?

You don't necessarily need a complex governance framework on day one. A sensible starting point may be understanding current usage, identifying the highest-risk areas and establishing clear practical rules.

Not every organisation needs a large governance programme. A small business using AI to help draft internal marketing content may require a very different approach from a financial organisation deploying AI into customer decision-making.

Governance should be:

• Proportionate
• Practical
• Risk-based
• Understandable
• Embedded into normal business processes

The objective isn't maximum governance. It's appropriate governance.