The pattern we kept seeing
We've talked with a lot of teams shipping AI features. Support chatbots, internal assistants, agents wired into customer records. Different products, different stacks. And one thing kept showing up.
There was no security layer at all.
A user typed something into the chatbot. It went to the agent. The agent passed it to the LLM. Nothing in between looked at what was being sent. If a customer pasted their credit card number into a support chat, that number went to a third-party model, word for word. If someone tried to push the bot into misbehaving, nobody was watching.
Nobody built it this way on purpose. Teams were moving fast, the demo worked, and security was going to be "phase two." Phase two rarely shows up.
A checkpoint in the middle
So we asked a simple question. What if every LLM call had to pass through a checkpoint first?
That's the idea behind Igris. It sits between your application and the model, and every prompt goes through it. Igris reads the full prompt, checks it against your policies, and if anything breaks a rule, the call is blocked before it reaches the model.
Clean prompts go on to the model. Anything that breaks a rule is blocked or redacted, and every request is logged either way.
Your developers don't have to rebuild anything for this. Igris speaks the same API format as OpenAI, so the app keeps making LLM calls the way it always did. Each call just takes a short detour through a layer that knows your rules.
What the policies actually block
The policies themselves are practical. Most teams want the same short list kept away from the model:
- Email addresses
- Phone numbers and other contact details
- Social Security numbers
- Credit card numbers
- Any other personal data your industry treats as sensitive
None of this is exotic. It's the data that turns an ordinary chatbot conversation into a compliance problem. When Igris finds it in a prompt, it can block the call outright or redact just the sensitive part, depending on how you set the policy.
When one user hammers the chatbot
Rules catch bad content. They don't catch bad behavior.
Picture one user firing message after message at a company's chatbot. Each message might look harmless on its own. Together they look like someone probing for a weakness, scraping answers, or running up the token bill.
Igris watches for that pattern. When a single user suddenly bombards a customer's AI with messages, it detects the anomaly and raises a flag, so the team can look into it before it turns into a real problem.
Log everything, and anomalies stand out
You can't spot strange behavior if you don't know what normal looks like. That's why Igris logs every request a user sends to our customer's AI. Every single one.
Those logs do two jobs. Day to day, they're the baseline that makes anomalies visible. And when something does go wrong, they're the record that shows who sent what, when, and what happened to it.
Protection in both directions
We think of this layer as protecting two sides at once.
On one side are your customers. The personal data they share with your chatbot shouldn't end up held by an AI agent. Igris stripped out the sensitive and confidential data before agents sees it. Igris can remove or redact the sensitive data and then send it to the agent.
On the other side is your company. The chatbot you put in front of the public shouldn't become something users can exploit. The same layer that checks what goes in also watches how people are using it.
What this looked like for one client
We have seen many companies where their user share sensitive data wihtout noticing that might be used for different purpose or user sometimes as for different user data where this problem get more interesting. Igris catch any sensitive data like credit card number, medical report numbers, SSN, email, phone number, etc. Igris give logs where all user queires are stored, flaged, or blocked.
Three questions to ask about your own AI app
If you're not sure whether your chatbot has a middle layer, these will tell you fast:
- If a user pastes a credit card number into your chatbot, where does it go?
- Would you know if one user sent hundreds of messages in a few minutes?
- If a customer asked what your AI did with their data last week, could you show them?
If any answer is "I'm not sure," that's the gap.
Most of the teams we talk to aren't careless. They just never put anything between their users and the model. Adding that layer doesn't have to slow anyone down, and that's the reason we built Igris.
Want to see what's passing through your own AI app? Try Igris.
Top comments (0)