Adding an AI feature has never been easier: a few lines of code can connect your product to a powerful model. That ease is also the risk. A feature that works brilliantly in a demo can leak data, give a customer a confidently wrong answer, or be talked into doing something it should not.
None of that is a reason to avoid AI. It is a reason to build it properly. Here is what that means.
Know where your customers’ data goes
When your product sends something to an AI model, that text leaves your systems and goes to a provider. Before you build, know three things: which provider, where they process the data, and what their terms say about keeping it or using it for training. Business terms from the major providers usually rule out training on your data, but check, and send only what the feature needs.
Check every answer before a customer sees it
Models are fluent, not reliable. Treat their output the way you would treat a form filled in by a stranger: check it. If the AI should return a date, confirm it is a date. If it should pick from a list, confirm it picked from the list. If it produces advice, make sure the product says what it is and where it came from.
Assume someone will try to trick it
If customers can type into your AI feature, some will try to make it ignore its instructions, reveal other people’s information, or say something embarrassing. This is called prompt injection, and it sits first on OWASP’s list of the top risks for applications built on large language models. The defence is design: give the AI access only to what that customer is allowed to see, and never let it take an important action on its own say-so.
Keep a person in the loop where it matters
AI is excellent for drafting, summarising, sorting and suggesting. Decisions with real consequences (money, health, legal standing, someone’s account) should end with a person, or at least with a clear confirmation step for the customer.
Test it like the rest of your software
An AI feature needs tests: a set of example inputs, including awkward and hostile ones, with the answers you expect. Run them whenever the prompt, the model or the provider changes. Models change behaviour between versions, and you want to find out before your customers do.
Watch the costs and the logs
AI usage is billed by volume, and a popular feature or a misbehaving one can grow the bill quickly. Set limits, watch usage, and log enough (without logging personal data you do not need) to understand what went wrong when something does.
Tell your customers
Say when they are dealing with AI, what it does with their information, and how to reach a person. Being plain about it builds trust, and in more and more places it is also becoming a legal expectation.
Built this way, AI features stop being a gamble and become what they should be: one of the most useful things you can add to a product.