Generative AI is being adopted faster than almost any technology before it, and its risks are specific enough to deserve their own dedicated governance attention. Leaders do not need to become machine-learning experts, but they do need a practical grip on what can go wrong and how to keep it within sensible bounds. This is a working checklist for putting generative AI to use across the business responsibly — capturing the value without walking into the traps.
The risks that are particular to generative AI
- Hallucination. Fluent, confident output that is simply wrong — and convincing enough to slip straight past a busy reviewer who assumes the machine knows what it is talking about.
- Data leakage. Sensitive information entered into prompts leaving your control, potentially stored or used to improve a third-party model you have no visibility into.
- Intellectual property. Genuinely open questions over the ownership and provenance of generated content, and the risk of inadvertently reproducing others’ protected material.
- Prompt injection. Manipulated inputs — sometimes hidden inside documents or web pages the model reads — steering it into unintended and potentially harmful behaviour.
- Confident automation. Wrong outputs flowing unchecked into decisions, code or customer communications precisely because the tool sounded so authoritative.
A governance checklist
- Policy. A clear acceptable-use policy covering approved tools and, above all, exactly what data must never be entered.
- Approved tooling. Sanctioned, appropriately configured services — ideally ones that contractually commit not to train on your data — rather than an unmanaged free-for-all of whatever staff happen to find.
- Data rules. Explicit, concrete limits on what may be entered into any generative tool, expressed in terms people can actually apply to their daily work.
- Human review. A named person accountable for checking output before it is used, published or acted upon — especially for anything customer-facing, contractual or high-stakes.
- Vendor diligence. A real understanding of how providers handle your data, whether they train on it, and what their security and retention practices actually are.
- Monitoring. Genuine visibility of how AI is being used across the business, and a mechanism to adjust course as you learn what works and what does not.
Start where the risk is
You do not need to govern every use equally, and trying to do so usually produces a thicket of rules that nobody follows and that quietly pushes people back toward shadow tools. Focus your attention first on the uses that touch personal data, customers, or important decisions — that is where the consequences genuinely concentrate. Low-stakes internal uses can reasonably operate under lighter-touch guidance. This proportionality is what keeps governance credible and keeps people on side, working with your framework rather than routing around it.
Generative AI rewards organisations that adopt it deliberately. A little governance up front — clear rules, approved tools, human review where it genuinely matters — is exactly what lets you say a confident yes to it rather than an anxious maybe.
The leadership role
Governing generative AI well is ultimately a leadership responsibility, not a purely technical one. Leaders set the tone: whether the organisation treats AI as a reckless shortcut, a forbidden fruit, or a powerful tool to be used with judgement. By putting a clear, proportionate framework in place and modelling good use themselves, leaders turn a source of anxiety and shadow activity into a governed, defensible capability that people can lean on with confidence.
The takeaway
Governing generative AI is not about slowing it down; it is about capturing its considerable value without walking into its specific and well-understood traps. Set clear rules, provide safe and capable tools, insist on human review for what matters, understand your vendors, and concentrate your effort where the stakes are highest. Do that, and generative AI becomes a genuine advantage you can defend to a customer or regulator — rather than a liability you did not see coming until it arrived.