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AI Governance

Practical governance for organisations developing, deploying, or using artificial intelligence responsibly.

Artificial intelligence promises real productivity and innovation gains, but without appropriate governance it also introduces risks that traditional IT controls were not designed for. Bias, explainability, data provenance, intellectual property exposure, regulatory obligations under the EU AI Act, and reputational impact all need dedicated attention.

Whether you are developing AI systems, rolling out generative AI across your workforce, or procuring AI powered tools, the questions are the same. Who is accountable? What is appropriate use? How do we know it is working safely? What happens when it does not?

What AI governance covers

A practical AI governance programme typically addresses:

  • AI inventory. Knowing what AI is in use across your organisation, whether developed in house, deployed to staff, or embedded in procured tools.
  • Risk assessment. Evaluating bias, explainability, data quality, and potential harm for each use case.
  • Policy and acceptable use. Clear rules for how employees and systems use AI.
  • Human oversight. Defining when AI decisions require human review.
  • Vendor governance. Due diligence and ongoing oversight of third party AI providers.
  • Data handling. How personal or confidential information can be used with AI tools.
  • Transparency. What users, customers, and regulators need to know.
  • Incident response. What to do when an AI system behaves unexpectedly.

Who benefits from AI governance

Dedicated AI governance programmes are particularly valuable for:

  • Organisations rolling out generative AI (ChatGPT, Copilot, Claude, internal tools) to staff.
  • Companies developing AI powered products or services.
  • Organisations in regulated sectors where AI use attracts specific oversight.
  • Businesses preparing for the EU AI Act and needing a clear compliance path.
  • Companies where enterprise customers are asking about AI governance during procurement or due diligence.
  • Organisations responding to incidents involving AI tools or AI supported decisions.

Frameworks we work with

Rather than tying you to one reference, we help you select the right framework mix for your context.

  • ISO 42001, the international AI management system standard, suitable when formal certification is a goal.
  • NIST AI Risk Management Framework, a voluntary, practical framework widely referenced globally.
  • EU AI Act, the regulatory backbone for AI use in the EU, creating legal obligations for higher risk systems.
  • OECD AI Principles, broader principles often referenced in policy and ethics work.
  • Internal frameworks. Sometimes the most practical option is a tailored approach aligned with your specific risk profile.

Where appropriate, we integrate AI governance with your existing information security (ISO 27001) and privacy (GDPR) work to avoid duplication.

How we can help

AI inventory and risk classification

We help you identify every AI system in use across your organisation, including shadow use of consumer AI tools, and classify each by risk. You will have a clear map of what you are responsible for and where the real risks sit.

Governance programme design

We design a programme that fits your organisation’s size, risk profile, and existing governance structures. AI policy and acceptable use standards, risk assessment methodology, use case approval processes, human oversight requirements, and documentation and record keeping.

Policy and training

Clear, practical policies for staff. What they can and cannot do with AI tools, how to handle confidential information, and when to escalate concerns. Training tailored to your team’s actual day to day AI use, not generic content.

Vendor and tool assessments

Due diligence on AI vendors and tools, including data handling, model provenance, security, and contractual protections. We help you establish ongoing oversight rather than one time reviews.

EU AI Act readiness

Practical preparation for the AI Act’s risk based obligations. Determining which of your systems fall into which risk category, and what conformity assessment and documentation you will need.

Privacy impact assessments for AI

AI use cases often involve personal data in ways that require dedicated assessment. We integrate AI risk assessment with GDPR Data Protection Impact Assessments for a coherent, single track approach.

What to expect

Initial engagements typically start with an AI inventory and risk assessment over 2 to 6 weeks depending on organisation size. From there, programme design and implementation is scaled to your needs. We work through your existing teams (legal, IT, security, HR, business) rather than creating parallel structures, so AI governance becomes part of how you operate rather than an overlay on top.

Common questions

What is AI governance?
AI governance is the framework of policies, processes, and oversight that guides how an organisation develops, deploys, and uses artificial intelligence. It addresses risks traditional IT controls were not designed for: bias, explainability, data provenance, intellectual property exposure, and the potential for unintended harm. It also enables the organisation to use AI confidently and at scale.
Do we need AI governance if we only use tools like ChatGPT and Copilot?
Yes. Often this is where governance matters most. Decentralised staff use of generative AI introduces real risks around confidential data exposure, intellectual property, regulatory compliance, and decision quality. A practical acceptable use policy, basic training, and a lightweight approval process for new AI tools catches the majority of issues before they become incidents.
How does AI governance relate to the EU AI Act?
The EU AI Act creates legal obligations based on AI risk classification, from minimal risk uses through to prohibited systems. AI governance is how you operationalise compliance. Identifying which of your systems fall into which risk category, implementing required controls for high risk use cases, and maintaining the documentation needed for conformity assessment. Starting now positions you for obligations as they phase in.
What is the difference between AI governance and ISO 42001?
ISO 42001 is one specific framework, the international management system standard for AI, and it supports certification. AI governance is the broader discipline that can draw on ISO 42001, NIST AI RMF, the EU AI Act, or an internal framework depending on what fits your organisation. We start with your context and goals, then select the right reference points.
How does AI governance relate to GDPR?
AI use cases commonly involve personal data, which triggers GDPR obligations around lawful basis, transparency, automated decision making (Article 22), and often Data Protection Impact Assessments. We integrate AI risk assessment with GDPR requirements so you are not running two parallel processes for the same systems.
How long does implementing AI governance take?
An initial AI inventory and risk assessment typically takes 2 to 6 weeks depending on organisation size. Programme implementation scales with scope. A focused staff use programme may take a few months. Comprehensive governance for organisations developing AI products is a longer engagement. We will give you a realistic timeline after the initial assessment.
Do we need a dedicated AI ethics committee or AI officer?
Not necessarily. For many organisations, AI governance fits into existing structures. Security, privacy, legal, and business leadership working together. Larger or more AI intensive organisations may benefit from dedicated roles, but we will help you find the right model rather than prescribe unnecessary overhead.

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