The business case for AI assistants is genuine: less time on first drafts, faster answers, smoother analysis. The risk is just as real, and it is rarely the one people expect. The problem is not that AI is malicious. It is that an AI assistant inherits the permissions of whoever uses it, and most organisations have more loose access than they realise.
The permission you forgot about
When you switch on a tool like Microsoft 365 Copilot, it can surface anything the signed-in user is already allowed to reach. That includes the site shared with "everyone" three years ago, the finance workbook linked into a Teams channel, and the folder a departing employee never locked down. The assistant did nothing wrong. It simply made years of quiet over-sharing instantly searchable in plain language.
This is why "turn it on and see" is the one approach we caution against. The readiness work is not about slowing adoption. It is about making sure the assistant can only see what it should before anyone asks it a question.
A practical order of operations
Securing AI is less about a single product and more about doing a few things in the right sequence:
- Discover and classify. Find your sensitive data wherever it lives and label it, so you know what is in scope. This is where data security posture management earns its place.
- Tighten access. Resolve over-sharing, fix inherited permissions and remove standing access nobody needs, so the assistant inherits a clean picture.
- Add guardrails. Put inline controls between users and AI so prompts and uploads to unsanctioned tools are inspected, and sensitive data is blocked from leaving.
- Govern use. Decide what AI is for, what it is not for, and make those rules real with policy and education, not just a document in a library.
Two kinds of AI risk
It helps to separate the two problems, because they need different controls. The first is sanctioned AI, the Copilot you deliberately deploy inside your tenant; the answer there is data discovery, labelling and permission hygiene so it behaves. The second is shadow AI, staff pasting customer data or source code into public chatbots on personal accounts; the answer there is visibility and inline control over what leaves your environment.
We use Cyera and Microsoft Purview to handle the first, finding and classifying the data and tightening what Copilot can reach, and Netskope to handle the second, giving visibility into AI use and blocking sensitive data from flowing into unsanctioned tools. The recent EchoLeak disclosure, a zero-click flaw in Copilot, is a useful reminder that even sanctioned AI needs least-privilege thinking and monitoring around it.
Adopt confidently, not nervously
Done in this order, AI readiness is an enabler, not a handbrake. You get the productivity, and you get it without handing an eager assistant the keys to everything. The organisations that struggle are the ones that adopted first and governed later. The ones that do well decided what the tool could see before they let it look.
Our Secure AI work walks through the full approach, and the AI policy template gives you a practical starting point for the governance side. When you want to know what your assistant could currently reach, a health check is the place to begin.