How to Stop Your LLM from Leaking Data Before It Becomes a Breach?

Most companies often think their biggest AI risk is a chatbot saying something wrong. A hallucinated fact or an awkward customer interaction. However, the real risk is far more costly to the organisation but sits quietly underneath most LLM deployments: Data Leakage.

According to a widely cited Cyberhaven analysis of 1.6 million knowledge workers, roughly 11% of everything employees paste into ChatGPT contains confidential information including source code, patient records, internal strategy documents, and personally identifiable information (PII). Now this is not just a hypothetical case. That’s one in every nine prompts. 

And here is the most uncomfortable part, this is not a hacking problem. No one broke in your system, no credentials were stolen. The data walked out the front door because the system was never built to notice it was sensitive in the first place. If your organization is utilising LLMs without PII guardrails, you don’t have an AI safety problem. Instead, you have an open data problem that is feeding the AI system. 

Why LLMs Leak PII Without Anyone Doing Anything Wrong? 

In order to understand the problem with LLMs and their potential to disclose sensitive information, it is helpful to understand what an LLM is. It is a pattern matching engine, not a compliance officer. The model is unaware of the nature of the data that it is presented with. The model doesn’t “know” that a string of digits is a national ID number, or that a name paired with a diagnosis is protected health information. It just seeks tokens and predicts the next one. 

The OWASP Top 10 for LLM Applications (2025 edition) identifies this as Sensitive Information Disclosure. OWASP describes this category of risk as arising from poor data sanitization and input handling, not from malicious actors exploiting a vulnerability. The model is doing exactly what it was designed to do, to generate fluent and relevant output from whatever data it receives. The Cloud Security Alliance, a nonprofit that has developed security standards for cloud and AI systems over the last decade, has reached the same conclusion: most data exposure in AI systems are due to missing controls rather than attackers breaking into the system. 

This is what makes the problem so easy to miss internally. There’s no alert, no breach notification, and no obvious incident. A support agent pastes the customer's full record into a prompt to get a faster response. An analyst uploads a spreadsheet with unmasked customer PII to “summarize the trends”. None of it looks like potential data breaches, until it becomes one. 

The Missing Layer Between Users and the Model: How RezunateLLM Applies PII Guardrails

The fix isn’t to be more “careful”. Careful doesn’t scale, and it definitely doesn’t survive contact with thousands of employees and customers interacting with an LLM everyday. What scales is a particular layer that sits between the user and the model, that is inspecting every prompt and every response before it goes anywhere. 

This is the problem RezunateLLM was built to solve, an AI gateway that applies PII guardrails at the point of interaction, not after the fact. Instead of hoping employees self-police, the gateway inspects traffic with three operational controls which eliminate the need for employees to monitor themselves and for detection systems to work in real-time.  

The three operational controls are mentioned below: 

• Flag: surface sensitive content for review without interrupting the workflow
• Block: stop a prompt or response outright when it violates policy
• Redact: strip or mask sensitive fields automatically so the interaction can continue safely 

The point is not to lock every company to follow a single inflexible policy. Different industries need different risk management approaches because their data types and regulatory standards vary, a principle IAPP and other privacy bodies consistently emphasise in AI governance guidance. A healthcare provider and a retail chatbot don’t need the same rules. What every organisation needs is the ability to set that policy and enforce it consistently across languages, before sensitive data ever reaches a model or leaves one. 

That's the shift PII guardrails are offering, from a “nice to have” bolted on after an incident, to a foundational layer of any LLM deployment. An LLM without a PII guardrail isn’t a lean, fast deployment. It is a system that’s leaking data and simply hasn’t been caught yet.

The Language Gap No One Talks About 

Most commercial PII detection tools were built and trained primarily on English language data. That means that the detection layer will work well for English data input to the LLMs in an organisation. However, if the same organisation has customers in another part of the world where the data doesn’t come in English, the PII guardrails tuned only for English will likely fail to catch PII data.

This issue is more significant than it might seem at first glance. As more organizations across the Middle East, North Africa, and other regions with multiple languages adopt LLMs, a PII detection layer that only understands one language will be of no use. Any enterprise that cares about implementing PII guardrails for its customers in those regions needs to ask its one crucial question: Does our PII detection layer understand the language of our customers? 

For the majority of tools that are available today, the answer to whether they can detect PII across multiple languages is most definitely no. That is where RezunateLLM comes in, it was built to close this gap from day one. PII detection was trained to work across both Arabic and English, so a guardrail doesn’t quietly stop working the moment a customer switches language.

The Question Isn’t IF. It’s When You’ll Catch It

If you look at the inputs given to LLMs in everyday use, roughly 11% of those inputs contain some form of sensitive data. So the real question facing any company that is deploying AI isn’t whether a PII exposure problem exists. It almost certainly does. The question is whether you’ll catch it, through a PII guardrail built into your infrastructure from day one, or whether your customers, your competitors, or your regulators will catch it for you.

See how RezunateLLM can secure your LLM applications.