Regulatory Area
AI Governance
AI governance is the framework through which an organisation decides how artificial intelligence may be developed, acquired, deployed, monitored and retired.
In this briefing
- What it is
- Key things to know
It brings together accountability, policies, risk management, technical controls and operational oversight so that AI systems are used lawfully, safely and consistently with the organisation's objectives and risk appetite.
Effective AI governance is broader than compliance with the EU AI Act. It should cover the full lifecycle of an AI system, from use-case approval and vendor selection through data, testing, deployment, human oversight, monitoring, incident response and decommissioning. It also needs to account for the organisation's role in the AI value chain, including whether it develops models, integrates third-party tools or simply uses AI within internal or customer-facing processes.
Key things to know
AI governance starts with a clear inventory
Organisations cannot govern AI systems they do not know they are using. A reliable inventory should capture the use case, owner, provider, model, data involved, affected users, level of autonomy, critical dependencies and applicable regulatory requirements.
Accountability must be assigned across the lifecycle
AI governance should define who can approve a use case, who owns the risk, who validates the system and who monitors it after deployment. Responsibility should not sit only with technology teams, particularly where AI affects customers, employees, regulated decisions or core business processes.
Risk depends on the use case, not only the model
The same model may present very different risks depending on how it is used. An internal drafting assistant is not equivalent to a system making credit, employment, onboarding or fraud decisions. Governance should therefore assess purpose, context, affected individuals and potential harm rather than classify risk solely by model type.
Human oversight must be meaningful
A nominal approval step is not effective oversight. Staff need the authority, information and training to challenge outputs, identify errors, pause the system and escalate concerns. Human review should be designed around the risks of the use case rather than added as a formality.
AI governance must connect to existing controls
AI should not be governed as a standalone technology category. It should be integrated with data protection, cybersecurity, operational resilience, model risk, outsourcing, conduct, product governance, record keeping and incident management frameworks.
AI literacy is an operational requirement
People approving, building, buying or using AI need training suited to their role. General awareness may be sufficient for some employees, while product, compliance, risk and technical teams will need deeper understanding of system limitations, regulatory obligations and escalation expectations.
For general information only. Not legal, regulatory or compliance advice.
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