## LLMs are powerful, but not inherently privacy-aware

LLMs operate as highly capable, non-deterministic pattern matchers. But they come with two significant privacy challenges:

• - **They don’t automatically distinguish between sensitive and non-sensitive data**
• - **They're fundamentally non-forgetful and non-auditable**

If you pass raw user input, internal logs, or structured data directly into an LLM without safeguards, you’re risking the exposure of names, emails, credit cards, health info, and more.

Even more concerning: LLMs can *memorize* and *regurgitate* this data in unrelated contexts, especially if that data appears frequently in your prompts or agent memory.

Imagine a customer’s social security number showing up in a completely different query weeks later. It happens. Or at least it could happen. This potential often acts as an immediate no-go and blocker for any organization that actually wants to roll out production-grade AI services as either consumer-facing products or internal productivity engines.

## This problem must be solved before organizations can fully leverage agentic AI

[Agentic AI](https://konghq.com/blog/learning-center/agentic-ai)Agentic AI — systems that combine LLMs with memory, APIs, and decision-making — introduce even more exposure vectors:

• - **Tool use**: Agents might query APIs with sensitive parameters.
• - **Multi-turn interactions**: PII might persist across long sessions.
• - **Autonomy**: Agents might write logs, store messages, or share info downstream — all without a clear boundary or data contract.

The net result? **You lose control of where PII goes, and you can’t easily trace what the model saw or said.** That’s a compliance and security nightmare for enterprise environments.

## Sanitization is the first line of defense

To safely build with LLMs and agents, **PII sanitization needs to be built into the flow** — not bolted on as an afterthought.

This means intercepting and managing data at the points of entry (requests), generation (responses), and interaction (prompts/memories). You want to ensure that:

• - **Only safe, redacted data reaches the LLM**
• - **No sensitive tokens or context leak during generation**
• - **Downstream consumers and logs are free of raw PII**

PII sanitization isn’t just about masking names. It's about contextual data control across the entire AI interaction surface — especially when that surface is drastically expanded by agentic workflows and interactions.

## How is this being done today?

Today, many teams attempt to manage this risk by building ad hoc solutions — embedding regex-based redaction libraries, relying on prompt engineering best practices, or adding pre- and post-processing layers to scrub sensitive data from inputs and outputs. In some cases, developers hardcode filters or use external data loss prevention (DLP) tools to flag potential leaks. 

While these approaches can be effective in controlled environments, they often lack consistency, observability, and scalability, making it difficult to ensure compliance and maintain trust across dynamic, multi-model architectures. 

This is especially true when organizations are using many different models and have many different clients and consumers who want access to the data in those models. As use cases arise, developers will build and implement another ad-hoc sanitization mechanism. Like the issue with one-off, ad-hoc API authorization, this [results in governance and security nightmares](https://www.linkedin.com/posts/alexdrag_apiplatform-multicloud-activity-7300933030234206208-ctLs?utm_source=share&utm_medium=member_desktop&rcm=ACoAACYKmYsBZxivSum9b2npyzxl_5MWRq_nRhc)results in governance and security nightmares. 

If an organization is interested in implementing a consistent PII sanitization practice that scales, the best way forward is to abstract the actual PII sanitization away from the developers as much as possible. Platform teams should invest in AI infrastructure that enables consistent PII sanitization as a standard policy that can be enforced across any (or potentially every) LLM exposure use case within the organization.

This is where the AI gateway — and Kong’s AI Gateway PII sanitization policy — comes in.

*Want to learn more about moving past the AI experimentation phase and into production-ready AI systems? Check out the upcoming webinar on how to *[*drive real AI value with state-of-the-art AI infrastructure*](https://konghq.com/events/webinars/state-of-the-art-ai-infrastructure)*drive real AI value with state-of-the-art AI infrastructure**.*

## The AI gateway as *scalable* PII leak-proofing

Just as an API gateway manages, secures, and transforms API traffic (and abstracts away the logic required for this from the backend API layer) an AI gateway gives you **control, visibility, and policy enforcement** for LLM traffic — ideally including built-in PII sanitization — and abstracts the PII sanitization logic away from the LLM and/or application layers.

At Kong, we just released a brand new PII sanitization policy that enables just this.

Here’s how it works in practice:

**1. Policy config**: The producer configures the sanitization plugin to automatically sanitize any inbound request of certain types of PII

**2. Inbound**: A client app sends a user request to the AI gateway. The gateway detects and redacts PII (names, emails, etc.) before forwarding it to the LLM.

**3. LLM interaction**: The prompt is processed with sanitized data, ensuring no sensitive info reaches the model.

This makes the AI gateway a trusted policy enforcement point between applications and models. But is it enough?

***Learn more about how to ***[***start sanitizing PII***](https://docs.konghq.com/hub/kong-inc/ai-sanitizer/how-to/)***start sanitizing PII****** using the AI Sanitizer plugin.***

## Building in PII sanitization and AI security at scale with global policies, control plane groups, and APIOps

The reality is that just having an AI gateway with this functionality isn’t enough to enforce proper AI security and PII sanitization at scale.

You must build a platform practice around the other layers of AI security as well. And that means you must combine the power of the AI gateway’s PII sanitization functionality with other layers of protection around content safety, prompt guarding, rate limiting, etc. And then, to drive AI governance and security at scale, you’ll need to combine the power of multi-layer protection with the power that comes from a federated platform approach to provisioning and governing AI gateway infrastructure. 

Kong enables all of this through the unification of the industry’s most robust AI gateway with the platform power of Kong Konnect control plane groups, global policies, and [APIOps](https://konghq.com/blog/enterprise/what-is-apiops)APIOps. How does this work?

We cover the concept of control plane groups in [this video](https://www.linkedin.com/posts/alexdrag_apiplatform-governeverything-activity-7311409676502323202-0Fz7?utm_source=share&utm_medium=member_desktop&rcm=ACoAACYKmYsBZxivSum9b2npyzxl_5MWRq_nRhc)this video, but here’s a quick summary: 

1. Platform owners can create control plane groups within Konnect — typically mapping onto lines of business and/or different development environments

2. Once the control plane group is created, the platform owner can then configure global policies for that group. In this instance, the PII sanitization policy could be enforced as a non-negotiable policy for any AI Gateway infrastructure that falls under this group.

3. Now, any time somebody from this specific team spins up Gateway infrastructure for their LLM exposure use cases, that PII sanitization policy is automatically configured and enforced.

Notice what this approach does. Yes, the AI Gateway is abstracting away the PII sanitization logic from the LLM or client app layers, as already mentioned. But, with the larger platform in place, platform owners can also abstract away the actual configuration of the PII sanitization policy from the developer — which both lowers the possibility of human error upon policy config and removes yet another task from the developer’s workflow, enabling them to focus on building core AI functionality instead of security logic on top of that functionality.

**One thing to note**: The process above was manual and “click-ops” oriented. But, like everything we do here at Kong, we believe the best practice is to enforce best practices such as these via automation and APIOps, ultimately enabling an “AI governance as code” program that leaves as little room for human error as humanly (or machine-ly?) possible. 

Kong makes APIOps simple, with support for:

• - Imperative configuration via our fully-documented Admin API
• - Declarative configuration (for non-Kubernetes teams) via our decK CLI tool and/or Terraform provider
• - Declarative configuration for Kubernetes teams with Gateway Operator

## Final thoughts and how to sell this to your boss: The business value and impact

Oftentimes, when we talk about APIs, gateways, gateway policies, etc., conversations can end up getting into the technical weeds. 

However, as necessary as these technical weeds are, the conversations shouldn’t start or end there. 

The organizations that are finding the most AI and API success are the organizations that start and end from a place of thinking about AI and API platform strategy from a business value point of view. And this makes sense, as the API really is the hero of the AI story. And AI is the hero of many organization’s innovation and disruption stories.

PII sanitization as a practice belongs in the realm of the business impact discussion. So, if you find yourself in a room with business leadership and you’re trying to make sure that they understand the business value of implementing the Kong API Platform for AI make sure they're aware of just how critical AI governance and PII sanitization is for:

• - **Compliance by default**: automatically bake-in GDPR, HIPAA, etc. compliance into every AI, LLM, and agentic workflow
• - **Trust and brand reputation**: from users, customers, and internal stakeholders
• - **Cleaner data and innovation**: the easier it is to ensure clean data, the safer it is to use that data for training, auditing, and reuse in larger AI innovation practices
• - **Faster time to market**: by abstracting and automating away today’s manual approaches to AI compliance and PII sanitization, it’s easier to drive greater AI adoption and, ultimately, start shipping innovation to market faster

LLMs and agentic AI aren't inherently safe for sensitive data — but they can be. With the right infrastructure patterns, like an AI Gateway with built-in PII sanitization as a part of a larger API Platform practice, you can unlock the power of these systems **without compromising trust, compliance, or safety**.

You're building the future of AI. Just make sure you're building it responsibly. If you want help, [just let us know](https://konghq.com/contact-sales)just let us know.