How to Secure Your AI App

What does it take to secure an AI app?

Securing an AI app means defending three layers at once: the application that surrounds the model, the data flowing in and out, and the model interface itself. An AI feature is still ordinary software with an unusual new input — natural language — so the old discipline of authentication, input handling, and least privilege still does most of the work. The new part is treating prompts and model outputs as untrusted data, because both can be manipulated.

The mistake we see most often is teams obsessing over exotic model attacks while leaving a public API endpoint with no rate limit and a system prompt full of secrets. Start with the boring fundamentals, then layer the AI-specific controls on top. None of this is wasted effort: it is the same security maturity that pays off across your whole stack.

Lock down the application layer first

Before anything model-specific, the app needs the basics that every web service needs. The OWASP Top 10 has not stopped applying just because there is an LLM in the stack, and most real incidents exploit ordinary gaps rather than clever model tricks.

• Authenticate and authorise every request — never expose a model endpoint that anyone on the internet can hit anonymously.
• Rate-limit per user and per key so a single account cannot drain your token budget or brute-force the system.
• Validate and size-cap user input before it reaches the model, and never concatenate untrusted text directly into privileged instructions.
• Keep API keys for providers like OpenAI or Anthropic in a secret manager, never in client code or a committed .env file.

If you do these four things well, you have already closed the doors most attackers actually try. The model-specific work below builds on that foundation rather than replacing it.

Treat prompts and outputs as untrusted

The defining risk of LLM apps is that the model will follow instructions hidden in the data it reads. If your app summarises a web page, that page can contain text telling the model to ignore its rules. So output is not automatically safe. Never pass raw model output into a shell, a database query, or a browser without the same escaping and validation you would apply to user input. The OWASP Top 10 for LLM Applications calls this "improper output handling", and it is where a surprising number of real incidents start.

The same logic applies to the system prompt. It is not a secret store — assume a determined user can eventually extract it — so do not put credentials, internal URLs, or anything sensitive inside it. Treat it as instructions, not as a vault.

An AI app does not get a security exemption for being clever. The model is just another untrusted boundary, and you defend it the way you defend any other.

Constrain what the model can reach

If your model can call tools — search, code execution, internal APIs — every tool is a privilege you have granted to a system that can be tricked. Apply least privilege ruthlessly. A retrieval tool should read only the documents the current user is allowed to see. A code tool should run in a sandbox with no network and no secrets. The blast radius of a successful prompt injection is exactly the set of permissions you handed the model, so keep that set small and deliberate.

Where an action is irreversible or sensitive — sending money, deleting records, emailing customers — put a human or a deterministic check between the model's request and the action. The model proposes; something you trust decides.

Log, monitor, and plan for failure

You cannot secure what you cannot see. Log every prompt, response, tool call, and refusal with enough context to reconstruct an incident, while scrubbing personal data from those logs. Set alerts for spikes in refusals, cost, or unusual tool usage. Decide in advance what happens when the model misbehaves: a kill switch to disable a tool, a fallback to a safer response, and a named owner who gets paged. Security is not a launch checkbox; it is an operating posture you maintain, and the teams that treat it that way are the ones that never make the news.

Prefer it handled for you?

If you would rather have a team build the layered defences, threat-model the data flows, and wire up monitoring, talk to BSH Technologies. We design and harden AI features end to end through our cybersecurity services, so you ship something that is safe by default rather than secured after an incident.