What is Kastron?

Kastron is a governance gateway that makes your AI applications follow your policies.

It addresses a challenge you may already be facing. Your business wants to use AI in many different ways. You are expected to help, but you are also responsible for protecting customer information and ensuring AI applications don’t say or do anything they shouldn’t. You need to keep AI within the rules without holding adoption back.

The problem is that an AI model can understand a rule and still break it. Telling it what not to do doesn’t give you reliable control.

That’s where Kastron comes in. It sits between your applications and their AI models, checking requests, responses, and tool calls before they proceed. You define the policies. Kastron applies them to remove sensitive information, block prohibited responses, stop unauthorized tool calls, and more.

This is AI runtime governance: your policies are enforced while your applications run, before a prohibited response is delivered or an unauthorized action takes place.

With Kastron, you can support AI adoption with policies you own and controls you can stand behind.

You can, and you should. But telling the AI a rule doesn’t guarantee it will follow it.

Whether you put the rule in every prompt or in an instructions file the AI reads, it is still just an instruction for the model to interpret. It is not a control that prevents a prohibited response or action.

You have probably seen an AI do something you explicitly told it not to do, then reply:

“You’re absolutely right! I shouldn’t have done that.”

That’s the problem. It can recognize the rule when you point out the mistake, yet fail to follow it when it matters.

If you are responsible for compliance, you need more than an apology after customer information has been shared or an unauthorized action taken.

Kastron checks what the AI is about to send or do against your policies and blocks what isn’t allowed. Your instructions still guide the model, but enforcement no longer depends on the model following them.

Your organization still has to answer for what its AI applications do.

Some mistakes can be reversed. Others can’t. If a customer-service AI issues a refund it was only authorized to discuss, you might be able to reverse the transaction. But if it shares someone’s personal information with the wrong person, you cannot take that information back.

Even when you can reverse the mistake, restoring trust is harder. Customers may question whether their information is safe or whether they can rely on your business. The damage to your reputation can outlast the incident.

And if you’re responsible for governance, you may have to explain what happened to your leadership, a regulator, or a court. They’ll want to know:

What controls were in place?

Why was this allowed?

What prevents it from happening again?

You need to be able to show how your policies were enforced, beyond pointing to instructions the model was supposed to follow.

Kastron gives you controls to prevent prohibited responses and tool calls, and evidence to explain its decisions. It records which policies applied, what decision it made, and why.

It means Kastron can stop a response or action from proceeding, even if the AI has decided to produce it. Kastron sits between your application and the AI models and tools it uses, checking what goes to the model, what comes back, and what the AI tries to do.

You set the rules at the level you need: across your enterprise, or for a particular app, business line, or jurisdiction. Kastron applies all the rules that govern each interaction. Your customer-support app, for example, must follow your enterprise-wide policies as well as any rules specific to that app, its business line, and the jurisdiction in which it operates.

One of those rules might say:

The customer-support application is not allowed to use the refund tool.

If the AI tries to call that tool, Kastron blocks the call before it executes. You’re not relying on the model to remember its instructions.

Other rules can remove sensitive information or block a prohibited response, while permitted activity continues. The control sits outside the model, so changing models or providers does not mean giving up your rules.

Your technical team connects the applications, but your governance team can manage the policies from there. You don’t need developers to write code every time a requirement changes or wait for a place on their roadmap.

You define what a rule should check and what should happen when it applies. Then you test it before submitting it for approval.

You also decide who can write, approve, and audit policies, and which parts of the organization each person can access. If your process requires someone other than the author to approve a rule, Kastron keeps those responsibilities separate.

Once the rules are active, your dashboard shows how much AI activity is governed and the potential violations prevented. You can show leadership what your controls are accomplishing.

When someone needs to examine a particular decision, tamper-evident records show which policies applied, what Kastron did, and why. Policy changes, approvals, and administrative activity are recorded too, so you can trace how a rule was approved and put into effect, as well as how it was enforced.

You shouldn’t create a new privacy problem in order to control an existing one. Kastron can inspect prompts, responses, and tool calls in memory, apply your policies, and discard the underlying content. By default, it retains only evidence of what was decided and why, not the prompts and responses themselves.

The information Kastron does handle is protected through encryption and isolation: data is encrypted in transit, retained information and credentials are encrypted at rest, and each customer’s data and configuration are kept separate. You can also choose the Gateway and evidence-storage regions to support your data-residency requirements.

And because Kastron checks interactions as they happen, speed matters too. Passing through a Gateway adds processing time, but Kastron keeps that additional latency low enough that users won’t notice it in most cases.

If your technical team wants a closer look, the Kastron Technical Overview explains the architecture, security controls, data handling, performance, and how to connect your applications.

Yes. The video below gives you a quick look at the platform and how it works.

Kastron product overview·2:05

To see what that could mean for your organization, request a demo. You’ll see how your governance team can turn policies into enforceable controls without waiting for developers to code each change — and how you can show leadership, auditors, and regulators that those controls are working.