How are you going to utilize some of these different AI models to do some of the, the drudgery work that you need to do, not only with data, but in, in the SOC, in just regular IT operations, in, in data analysis, whatever have you.

And, and I'd argue that, that AI is, is data on top of data on top of data on top of data, um, and you need guardrails.

So, so, you know, going back before AI, we still had data quality checks.

Um, you had process checks that, that just sat within the data analyst role.

So, so when someone came to me and said, "What are my revenue numbers?" I knew from looking at the person who they were and, and what revenue numbers they wanted, and if I gave them the wrong answer, then they could yell at me about it, right? We-- As much as we might yell at our AIs, um, th-that, that doesn't matter.

So the solution is, uh, I think putting that similar architecture around it, because whether it's AI in the security space, whether it's AI in the data space, whether it's AI in service ops, the, the word that you're hearing everywhere is, is context.

So how do we tie all this stuff together? Well, what does context mean? It means five different things to five different people.

Before we wrap up, talk to me about some of the upcoming research that you're doing kind of in the space, because I think people would like to probably know a little bit about that as well.