Your employees are already using AI, whether or not you have a policy. Governance isn't about stopping that. It is about making sure the AI your business relies on is secure, accurate and accountable, so you can scale it with confidence instead of anxiety.
Three forces make AI governance urgent for growing companies. Regulation is expanding, with the EU AI Act, US state laws and sector rules all setting expectations. Customers and partners increasingly ask how you use AI in security questionnaires. And agentic systems that take actions raise the stakes of mistakes. A lightweight framework now is far cheaper than retrofitting one later.
Write one or two pages that cover which AI tools are approved, what data may and may not be entered into them, how AI-generated content must be reviewed and who to ask when unsure. Keep it practical, with examples. Update it every quarter as tools change.
Define which data classifications can be used with which AI services. Public and internal data might be fine with approved enterprise tools; confidential customer or health data may need private deployments, extra contracts or be off-limits. Make sure vendors do not train their models on your data unless you explicitly agree.
Not every AI use case needs the same scrutiny. A simple three-tier model works well:
Every AI system needs a named business owner who is accountable for its outcomes. For higher-risk tiers, decide where a person must review or approve outputs before they take effect. This is especially important for AI agents that can act on systems.
Before adopting an AI product, check where data is stored and processed, retention terms, security certifications, how the vendor handles incidents and whether you can export your data. Add these questions to your standard procurement process.
Track quality, cost and usage for production AI systems. Give users an easy way to flag bad outputs. Define what counts as an AI incident, such as a data leak, a harmful response or a wrong action, and route it through your existing incident process.
Protect against prompt injection, restrict which tools and data each AI system can reach, log prompts and actions for audit and red-team important systems before launch. These sit alongside, not instead of, your existing security controls in your cloud environment.
You do not need a large committee. A small working group from IT, security, legal and one or two business leaders can draft the first version of these policies in a few weeks. Pair that with the use case scoring in our AI readiness roadmap and you have a foundation that lets the business move quickly and safely. If you'd like a head start, our AI governance and security service includes policy templates and a risk review of your current AI use.
It is the set of policies, roles and controls that decide how an organisation selects, builds, uses and monitors AI systems so they remain secure, compliant, accurate and accountable.
Yes, though it can be lightweight. A short acceptable use policy, data rules, risk tiers and named owners cover most of the risk without slowing teams down.
A small cross-functional group spanning IT, security, legal and business leadership works best, with a named owner accountable for each AI system in production.
A practical AI readiness roadmap for mid-size businesses: pick the right use cases, fix data gaps, prove ROI in a short pilot, and scale AI into production safely.
Read articleAgentic AI explained in plain language: how AI agents differ from chatbots, where they create value today, the risks to manage, and how to launch your first agent safely.
Read articleA 12-step cloud migration checklist for moving workloads to Microsoft Azure with minimal risk: discovery, landing zones, security, cutover planning, cost control and more.
Read articleBook a free 30-minute consultation and we'll map your quickest wins in AI, cloud and Microsoft 365.