RandstackUpdated
Firewalls protect systems. An AI assistant also needs protecting at the level of behavior: what it can be talked into, what data it can reach, and what it says.
Traditional security protects the doors: networks, servers, logins and passwords. Those still matter. An AI assistant adds something new behind the door, a system that reads language, makes decisions and can reach your data. It can be persuaded, and a firewall has no view of the conversation.
Picture a support chatbot that a customer talks into applying a discount it was never meant to give, or an assistant that answers one customer with details from another customer's account. Nothing was hacked in the usual sense. The system did what it was asked. That is why AI security has to cover how the model behaves, not only where it runs.
| Risk | What it looks like | What addresses it |
|---|---|---|
| Manipulation | A user, or text hidden in a document, tells the AI to ignore its instructions | Instructions users can't override, input checks, limits on the actions the AI can take |
| Data leakage | Private data appears in an answer to the wrong person, or is sent to an outside model | Permission checks before data is retrieved, data kept separate per user, rules on what leaves your environment |
| Behavior drift | Answers slowly become less accurate, biased or out of line with policy | Ongoing monitoring, regular review of real conversations, alerts on unusual output |
Prompt injection is any attempt to make an AI ignore its instructions. It can be typed directly, or hidden inside content the AI reads, such as an uploaded file or a web page. We keep the system's instructions separate from user content, let the AI call only the actions it needs, and check permissions for those actions outside the model. Anything with real consequences, like a refund or a change to an account, asks for confirmation.
The safest data is data the model never sees. Permissions are applied before anything is retrieved, so the AI can only work with what the current user is allowed to access. Each user's data stays separate, and there are explicit rules on what may be sent to an external model, with sensitive fields masked when it is.
Risks show up in real use, so monitoring doesn't stop at go-live. We log conversations, review samples on a schedule, rerun a set of known attack prompts after every change, and alert on output that looks unusual, so problems are caught before a customer reports them.
A secure assistant is useful to customers and gives nothing away to people trying to misuse it.
An attempt to make an AI ignore its instructions, either typed by a user or hidden in content the AI reads, such as an uploaded document or a web page.
It still protects the infrastructure, and you need it. It doesn't see what the assistant is asked or what it answers, so behavior needs its own controls and monitoring.
Yes, when permissions are checked before data reaches the model, each user's data is kept separate, and you decide what may leave your environment.
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