Tom Findling on Conifers CognitiveSOC Launch

This is Techstrong tv. Hey everyone, we're back Techstrong tv. We've got another CEO to talk to.

Let me introduce you all to Tom Fin. Fin link. ai.

ai, but hopefully after today you'll know who they are and what they do. Let's welcome Tom here, though, to text on tv. Hey Tom, how are you?

Thanks for joining us. I'm doing pretty well. Yeah, how are you?

Very well, thank you. So Tom, we're gonna talk a lot about Conifers, but before I do, let's talk about you, you know, you're the CEO here. Give us kind of how did you wind up being the CEO at Conifers?

Oh, yeah, that's a very interesting story. So, you know, started my career, uh, back there in Tel Aviv as part of the intelligence community over there, doing a lot of very interesting, exciting things we can talk about. Um, then after my military service, I joined a little company called VMware.

Um, back in the days, uh, when we did public clouds, private cloud, and a lot of interesting things in the data in the data center. Um, I had a pleasure to join the Israeli office back then in Tel Aviv and work on the European market. Uh, but as our offerings took off, um, I was asked to move to California, to Palo Alto, to headquarters, um, and lead the, um, portion of a product team of the cloud management division.

Uh, so this is where I first exposed to data science and we did monitoring and data science, uh, for the data center about 12 years ago. It was remarkable. It was really interesting.

Uh, we got a couple PhDs helping us and you know, we needed to have, you know, mountains of servers in the server X in the data center just to run some simple predictive analytics, um, and machine learning. Um, after that, uh, did a lot of work on move to the startup side of the house and ran product and engineering, uh, for a sustainability startup. When we did optimize, uh, consumptions and generation of power plants in the United States based on predictive analytics, was very, very interesting.

Um, and then I moved to insights, uh, doing, um, threat intelligence, uh, great company. Uh, I moved to the dark side, moved from the product and engineering side of the house into the sales side of the house, and I was the chief customer officer over there, uh, six and a half years. Great journey 2021.

We were sold to Rapid seven on a 350 transaction. Um, and then I joined the detection and response, um, practice at Rapid seven, um, as leading product management over there. Um, this is where we're exposed to, again, MDR services, the soc, um, and a lot of the different phase that we are doing today at Conifers.

Uh, great run over there. Um, one before experience meeting a lot of great people and customers. Um, and this is what inspired we see the challenges, see how you provide so services in large scale, uh, and see what's working and not working gave me the opportunity to kind of understand what, what we think, what I think what I believe should be the next stage in detection and response and security operations.

Um, and inspired me to kind of, uh, open Conni first and start with Conni first. Excellent. What a journey.

Oh yeah, what a great journey, man. All right. Only only in the tech world do you hear, you know, these kinds of fantastic, fantastic, uh, career paths.

Um, what made you found or get involved with Conifers? What, what, what was the passion behind that? So, when I was at Rapid seven, um, I was implementing a data science project within the Fred Intelligence, um, product line that we had.

And I've seen such a great like results while applying, um, some of those techniques into increasing the efficiency, uh, of our analysts and our ability to do things much faster, in much higher quality. Um, and that inspires me to just, you know, go ahead and see how we can broadly implement that within other departments. And again, obviously being at Rapid seven and being exposed to so many MDR customers and sell customers really helped me to, you know, like I said, better understand, see the challenges, see the needs, see the market, and understand what a great opportunity, um, it would be if we could bring all this like greatness of data science and the most recent innovations of AI into the stock, uh, and what great problems we can solve.

Um, and that's what brought me into it. Very cool. Alright, let's, let's pivot if we can now with with Conifers, um, you know, started this company.

Look, AI is certainly, uh, on top of everyone's mind, especially this week with what went on with deep seek and stuff. But what, you know, this isn't, this is ai, this is you. When I look at AI and security, to me there's two different pieces of it.

One is securing against ai, right? The bad guys are going to use AI too, and we need to to make sure we we, you know, have a handle on that. The second though is leveraging AI to be more efficient in the security that we do.

Conifers seems to me is more of, um, using leveraging AI to be more efficient, to offer better security, right? Absolutely. Um, tell us a little bit about that.

Yeah, for sure. So I think first we wanna take, I wanna take a step back and just said, AI is great and everybody's talking about ai, but what we're really after is solving hardcore security problems. So we've focusing on the business impact and the business outcomes on the things that we are doing, and we leverage great technology of making it happen.

Like I've hear the buzz around AI kind of, you know, every company has the word AI to every product line that they currently have, but there is a little bit of dissatisfaction in the market with the outcomes that you see, uh, from some of those like products that you see out there. So we are, our main focus is to solve hard security problems through with AI and with data science. Um, and what we are doing with confer is we are helping organization to achieve what we call solve excellence and be more efficient.

And efficiency has been talked about a lot over the last, you know, 10 years in security operations. But unfortunately with all the different revolutions of the tools that you have seen that we were able, we were never able to actually see the ROI and get to the point that finally we are able to unlock the efficiency gates, but also more effective. What AI can bring to the table with the right combination with the individuals that you currently have in the organizations is the ability to scale up your coverage, making sure that you catch a lot more early warning signs and you actually increase your quality and accuracy levels and scaling up in a way that you've never seen before.

So what we're doing there with Conifer, it's we we taking like holistic look at your stop as it's run today, we help you to recognize what are the best areas of opportunity that you have to be more efficient and effective and start with a very, very moderated, um, engagement model that help you to have business success and see value, um, bring those use cases and onboard them to your specific organization with a great combination between the models that we bring in pre-trained and your institutional and organizational knowledge. So we take all of your incidents that you currently have in your SOC today and we able to troubleshoot them and investigate them in more depth, in greater scalability, uh, which leads into efficiency, but also greater EFF effectiveness as a result of it as well. Excellent.

Very interesting. Um, you guys recently announced $25 million in funding. Yes.

Yep. Tell us a little about that. Yeah, so, you know, that has been really, really exciting.

Um, you know, that's, that's came up as big trust from our investors. Um, and we're very proud to have like we've take, like, take this amount of money and make put it into good use. Uh, there are multiple things that we're going to do with this $25 million.

And again, first thing is just our go to market in making sure that we, um, making ourself available to some of the great organizations that exist out there. If it's service providers, if it's um, enterprise organizations, we were able to go and access them. But on the technology side, we run a lot of interesting things.

We have the cognitive, so is our proprietary patent pending platform that we created that utilize iGen AI as well as many out of flavors of data science to bring great results when it's come to accuracy, but also cost efficiency, um, into the enterprise. Uh, so keep working on our platform, um, and also kind of accelerating our path with training more models and make them more efficient, uh, and use the, you know, most recent technology in order to bring that, um, bring that great opportunity to be more effective and efficient to a lot of other different organizations out there. Agreed.

Um, you know, I I wanted this, excuse me, my tongue gets tied there. Specifically, I wanted to mention the cognitive stock platform. This is a trademark, kind of, it's kind of the heart of what you guys are offering right now, and it, and it's really, you know, it's native AI and it's offered to organizations to solve the critical stock challenges at scale, right?

Because, you know, I, I've been in the security world myself 25 plus years and one of the companies I had helped co-found at one point we really moved into an MSSP type of model, right? We were, or, and I, you know, it's one thing to have a stock or a smaller company, a mid-size company, when you start getting a large enterprise or an MSP kind model where you're managing multiple organizations, you know, every little problem is magnified at scale. Absolutely Little, little problems become big problems at scale.

Talk about cognitive sock and how it helps there. Yeah, so there are few things that we're doing and I think you hit the nail on the head there. Um, doing ai, it's, you know, great and do AI on small scale is great.

The ability to have consistent results in scale. This is something which is extremely important and consistent results in scale, which are unique to specific organizations. 'cause each organization is different with it risk tolerance, with its assets, with its institutional knowledge.

So being able to take that and make sure that the model are being fine tuned to those specific organizations, um, that's really key. And make sure that you can do it in scale across different organizations. When you talk with a service provider or even, you know, Alan's uh, kind of enterprise organizations today, there aren't many service providers themselves with the different subsidiaries in the acquisitions that they do.

So this is something which we invested a lot in being in, in the process of being able to adjust ourselves to the specific technology and institutional knowledge that each one of the tenants or the organizations are using. We have this continuous learning that we do. And this is another opportunity to say that, you know, humans are not going anywhere.

And in order for us to, you know, we discovered a lot of information about a customer from this data, from the interactions with, with the analyst. Uh, but everything that we ingest into the baseline of each one of our tenant and customers is being audited and validated by human. And why we did that, um, if you would be, if you would lack sometimes AI just to learn things by itself in certain areas without human supervision, especially when we know in some areas that you might have some bad behavior, you might scale bad behavior.

And if you start ingesting small bites of bad behaviors into your models, you will scale up this bad behavior. And that, as you said, a small problem becomes really big overnight. And that's probably would be the end of the AI implementation for that specific organization or service provider.

So it's still important to have human in the loop. It's, it's still important to have human oversight and it's still important to have certain procedures and processes as doing just more information, especially critical information into the baseline of the models, uh, which are relevant to specific customers. Being able to, whatever needs to be controlled, be controlled and audited.

'cause when things goes wrong, first prevents things from going wrong. And if things go wrong, be able to nail it down, figure out where it is, and eliminate, um, what's going wrong as soon as you can. So, super important.

I I agreed, agreed. Honor fruit we're almost set of time, but I wanna make sure we get this stuff in Tom. ai.

Absolutely. And is there, like what's the on-ramp? Is there a free trial?

Is there Yeah, you know, how, how do people get onto it? Yeah, so we have first we, we do only, well, we sell our software only to customers that see the value in it. So we offer proof of concept and we would love to engage with organization and service provider that wanna come and see the value.

Um, it's pretty easy integrating with the existing system platform ports and procedures and processes that you have in house. It doesn't take, uh, long and then within 30 days, um, usually, you know, most of our customers able to see significant value. Um, and our unique implementation model allows, uh, allows the customer to control how fast they wanna run and how, you know, how what is their readiness to go and adopt more and more AI in their organization.

And as we develop that process of being able to, you know, let go for ai, it's also important that we stay on control of that process and making sure that every organization feel comfortable with the speed it takes them to enable ai. I kill it. Alright, Bob, thanks for being here on Text Drug TV today with us.

I appreciate you explaining all this to the audience. Best of luck with Conifers, come back and keep us posted as this continues to evolve and, and, uh, grow. Thank you very much Alan.

Appreciate it all. Thank you Tom Fiddling, CEO Conifers AI here, Aren Techstrong tv. We'll take a break.

We'll be back in a moment.