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The white paper explicitly rejects creating a dedicated AI regulator, relying instead on existing sector regulators applying the cross-sectoral principles within their remits. It names the Information Commissioner's Office, the Financial Conduct Authority, the Medicines and Healthcare products Regulatory Agency and the Equality and Human Rights Commission among them.

For most organisations this is practical news rather than an abstraction. If you use AI to make decisions about people, the ICO is your regulator. If you use it in financial services, the FCA's rules already apply. If the output could disadvantage people with a protected characteristic, the Equality Act framework applies and the EHRC is involved. A consultant proposing an AI governance programme that does not name which regulators cover your use is selling a template.

The ICO's AI and data protection guidance is organised around the data protection principles and covers accountability and governance, transparency, lawfulness, accuracy and statistical accuracy, fairness, automated decision-making, security and data minimisation, and individual rights. It also addresses the harder questions specific to AI, including inferences, affinity groups and special category data.

An impact assessment is frequently required rather than optional. The ICO lists the use of innovative technology, including artificial intelligence and machine learning, among the processing types likely to need a DPIA, alongside large-scale profiling, automated decisions that deny someone a service, invisible processing where data was obtained indirectly, and data matched from multiple sources. Training an AI system usually ticks at least two of those.

Data minimisation is the principle AI projects breach most casually, because the instinct is to collect everything in case the model needs it. The ICO treats minimisation and security as a standalone subject within its AI guidance, and the discipline of justifying each field is a good early test of whether a consultant has done this before.

Article 22 of the UK GDPR deals with solely automated decisions producing legal or similarly significant effects, and the ICO's AI guidance gives it a dedicated chapter on the safeguards that apply. The word solely does the work. Human review only takes a decision outside that category if the reviewer is competent, has the authority to change the outcome, and actually considers the case rather than approving a recommendation by reflex.

Explanation is the paired obligation. The ICO and the Alan Turing Institute jointly produced guidance on explaining decisions made with artificial intelligence, covering how to build systems that can extract the information needed for different kinds of explanation and which organisational roles and procedures support it. If a consultant proposes a model whose outputs nobody can explain to the person affected, that is a design decision with consequences, and it should be taken deliberately rather than discovered after the first complaint.

Sending a prompt to a hosted model is a transfer of whatever is in the prompt. Read the vendor terms for three specific things: whether your inputs are used to train the vendor's models, how long the inputs are retained, and where they are processed. If the answer to the last one is outside the UK, the international transfer rules apply and you need adequacy regulations or an appropriate safeguard with a transfer risk assessment.

Staff pasting customer information into consumer AI tools is the version of this problem that arrives without a project. It is worth writing an internal position on which tools are approved and what may be put into them, because the alternative is that the policy is set by whoever has the most enthusiasm and the fewest doubts.

Most organisations get more from switching on AI features inside software they already run than from a bespoke model. The vendor has done the integration, the security review is about one supplier rather than a stack, and the feature improves without a project. The limit is that you cannot change what it does, and the data protection questions remain yours as controller even though the vendor built it.

A bespoke build is justified when the task is specific to your organisation, when you hold data nobody else has, and when you have somewhere to put the output. It is a longer commitment than the demonstration suggests: models need monitoring, retraining and someone who owns them. Ask any consultant recommending a build what the second year looks like, in staff time rather than in licence terms.