Like, what did you find?

Yeah, so my co-founder, Nati, the CEO, we've been working together for eight or nine years straight because we were together in our previous company.

So we have a very good understanding and a very good relationship, of course.

But one of the benefits that Nati has is that everyone loves him.

It's annoying at the same time when you go to him with meetings, you know, when you're trying to, you know, put on your own charm and get some love.

And Nati, like kidding aside, Nati is a really dominant force.

So he has lots of great connections in the industry.

So how do you work with the customer to figure out, to tune

Like, what did you find?

Yeah, so my co-founder, Nati, the CEO, we've been working together for eight or nine years straight because we were together in our previous company.

So we have a very good understanding and a very good relationship, of course.

But one of the benefits that Nati has is that everyone loves him.

It's annoying at the same time when you go to him with meetings, you know, when you're trying to, you know, put on your own charm and get some love.

And Nati, like kidding aside, Nati is a really dominant force.

So he has lots of great connections in the industry.

So how do you work with the customer to figure out, to tune

Like, what did you find?

Yeah, so my co-founder, Nati, the CEO, we've been working together for eight or nine years straight because we were together in our previous company.

So we have a very good understanding and a very good relationship, of course.

But one of the benefits that Nati has is that everyone loves him.

It's annoying at the same time when you go to him with meetings, you know, when you're trying to, you know, put on your own charm and get some love.

And Nati, like kidding aside, Nati is a really dominant force.

So he has lots of great connections in the industry.

So how do you work with the customer to figure out, to tune

So, uh, you know, to your point about how important, uh, the data is here, it is one of the key things that attackers are using, uh, to get, to get paid, to try and extort money out of companies.

Yeah, ex- exactly.

So, so the way we look at it is that, um, we see ourselves as an endpoint data security platform, and what that means is that it is a modern approach for a DLP.

But it's also a modern approach for the insider threat, and it's also a modern approach for the endpoint governance, because you can't really separate those worlds.

So when customers say, and a lot of customers do talk to us and say the same, "I want to protect my AI on the endpoint," so what exactly do they mean? So they want to make sure that the different AI processes that run on the endpoint, whether it's local agents, whether it's MCP connections, or whether it's just the employees trying to chat with the latest AI service that, you know, exists.

They want to make sure that the data that goes into this AI doesn't hurt them, whether it's intellectual property that shouldn't be, uh, you know, out there, or whether it's not customer data that is being used with unauthorized tools.

And they want to make sure that no one is doing something malicious or with a malicious intent, or even more so, to make sure that the, um, unaware users don't make really silly mistakes.

Yeah