Simplifying Data Security: Insights from Lightbeam.ai’s CEO Himanshu Shukla

Hey everyone, it's Alan Shimmel. Welcome back here to Techstrong tv. In this segment, I want to introduce you to Hemanchu Shukla.

ai. Let's welcome him. Homan, you.

Welcome to techron tv. Hi, Alan, nice to be here. Nice to have you on, and thank you for joining us.

So Homan you, before we jump into Lightbeam and what we want to talk about with copilot and the risk associated, I, I wanna spend a little bit of time talking about you. You're the co-founder and CEO over at Lightbeam. And you know, it's up to you to tell us what, what possessed you to go out and start a company and co-founder company and be CEO.

Let's hear a little bit about your journey to founding seed, uh, founding lightbeam. Sure. So Lightbeam is all about simplifying data security.

And, uh, if I look at, uh, uh, data security as a problem, it's a very broad area. And, uh, it's a very complex problem, primarily because of lack of automation. And this is something that we ran into at my previous company where I was working at Nutanix.

I was leading the AIOps team there and we were collecting, uh, customer data. And this customer data got shared with our customer success team, our product management team. And after some time, we didn't have any clue about where all the sensitive data was and the sensitive data was in form of, uh, uh, reports.

It was in our data lake. It was spread across the whole organization, and we had no clue about where all it has spread across. Uh, and especially to differentiate between the customer data and the employee data was extremely hard.

And then we couldn't figure out how to protect this data. And that's what inspired us to start something which can automate the D two day operations part of managing the sensitive data. And that's what, uh, got us started, uh, with Lightbeam because we found that, uh, using the new AI capabilities, you can automate, build a system that can automate and simpl simplify the data security operations and reduce the risk of handling the sensitive data.

And if you see the way the world has been evolving, uh, this is something which is really, really critical that organized organizations need to be really careful about how they're managing customer data because it is not only, uh, reputational risk, but also something which, uh, I would, if I'm sharing my data with a organization, I would want them to handle it really carefully. And this is something which, uh, Lightbeam is all geared towards. I love it.

I love it. Um, when did you find found Lightbeam, by the way? It was founded in, uh, December of 2020.

And at that time, yeah, AI was very much in infancy. All the, uh, models, uh, which were, there were just transformer models, which are the fundamental basis for all the newer generation AI technologies like Chat, GPT, uh, we're just getting started. And, uh, uh, as we all can see, all these AI models has transformed the world in the past four to five years.

Yeah. So this, the Lightbeam was kind of a COVID baby, then you guys came founded right into COVID interesting times to find a, found a company, huh? Yes, yes, yes.

That was an interesting journey, how we got started, the initial stages when we were building the company where everyone was working from their home. But, uh, uh, it was tough, but at the same time, exciting because this problem just, just got compounded because everyone was working, uh, remotely. Yeah.

And then, uh, sharing of the data and responsibly sharing of the data for every organization has become a lot more complex over a period of time and, um, exciting times. Absolutely. I wanna talk a little bit about DDoS, right?

I've, you know, I've, I've been in security myself for 25, 30 years, just back from Black Hat last week. Um, you know, DDoS has been a scourge for a long time, right? Uh, friends of mine in Akamai and CloudFlare, right?

They built their whole, not their whole business, but a good piece of their business was built around trying to stop DDoS attacks. And, you know, DDoS went from, from, uh, a hobby type of attack to professionals and the amount, I mean, you know, today's DDoS attacks, I don't have to tell you there, they're massive, massive, the amount of peak data and, and stuff that, that can come in there. So we've, we've, uh, it, it, it, it, it boggles my mind when you think about, you know, how just what was a relatively simple, uh, attack surface, a relatively simple path has become such a, a base for different kinds of attacks and different kinds of security risk and, and everything else.

ai, I assume that's the website. Yes. Yes.

And, and before we jump in, we're gonna talk a little bit about copilot and some of the other things, but before we do, for people who maybe want to get find out more about lightbeam Engage, what would be your best advice? Like, what's the on-ramp to engage with lightbeam? So, uh, if you were to, uh, talk about lightbeam, it would be all about how an organization is managing the data responsibly, how the data enters within the organization, how it gets, uh, disposed, or how it gets shared with the people within the organization.

Uh, and if you are sharing it outside the organization with your partners, uh, what data are you sharing with them? So do you have any clue around the sensitive data, across the disparate pieces which you might be having, and then having an automation built on top of it, uh, so that if, uh, the data has been residing with you for past seven years or more, are you able to retire the data? Who's accessing this data?

Um, uh, for example, if an HR employee is having access to employee sensitive data, that is all fine, but if, uh, uh, a salesperson is having an, uh, access to employee sensitive data, that's a total no-no. How do you manage these policies around the organization is what lightbeam is all about. Uh, it is all about organizations helping organizations manage the data in a responsible manner.

And that's how I would say that, uh, uh, as we grow, go into the AI world, which is there data is going to become more and more, uh, important because, uh, there would be agent ais, which would be taking actions based on the data which is being fed to them. And are you having the right data being fed to the AI agents would be the world going forward. And that's where, um, as the data becomes more and more important, companies are, uh, like lightbeam are the ones which are helping organizations manage their data.

Absolutely. Alright, if it's okay, let's change gears a little bit, and I want to jump into Microsoft copilot, locking it down. You know, like much of what we're seeing with ai, the, uh, potential for good, the potential to make our lives better, easier, work better is great.

But the dark side is, it also represents potential risk and challenges. And it's why we can't have nice things on the internet, right? Because there's, there are people out there who look in organizations and countries, nation states and everything else who, who look to exploit every new thing that comes down the pike.

And this is AI is no different and copilot is no different. Talk to us. You know, I think we're all aware of the great things copilot can do for us and help us, but talk to us sort of the dark side here of copilot.

What, what's the downside? So if you look at copilot, it has given immense power in terms of democratizing the usage of data within the organization, because now you can feed the data to copilot, uh, and then you can ask questions. So whatever you use to take hours and hours in terms of digging the data, figuring out what is happening within the data can be answered within seconds.

But at the same time, uh, the other part of it is by democratizing the data, at times you are handing it over to people who shouldn't be having access to this data. So, for example, uh, in an old, uh, older world, I might be a part of a group where sensitive data is shared, but I would not even know about it because it is lying somewhere in some drive. Deep down, I don't have to worry about that part, meaning I won't even look at it.

So someone shared with me an employee salary information, which is highly confidential, or their medical records, which were, uh, collected. Uh, and I won't even get to know about it because I'm not dig looking for that information there. But now with copilot, because it is, uh, looking through every, uh, no and corner, I can just type in a query and it would give me the results and the, this data might be 10 years old, but it is available or accessible to me.

So this accessibility of the data, the shadow data, which was there, reciting in organization, is all now available to me. And that exposes a, uh, big, big problem for the whole organization. Because now if I, by mistake, I made a folder, which was open, and, uh, uh, copilot learns about it, it, uh, uh, collects it, then it is accessible to everyone within the organization.

So this is the big risk which, uh, which was always there for organization. It was just not exposed, uh, in the way Copilot has made that information available. And that's, that's what, uh, uh, is a big challenge, which is there.

So for example, I was, uh, reading through, uh, some article where what happened was a company was laying off people and they had just a file name, which was saying, uh, layoff list, and someone searched for it. Uh, although the file was not accessible, the name of the file was accessible to that individual, and that information leaked within the organization. So that's a huge problem for every company, which is there.

So now how do they manage every bit of information being exposed to copilot, uh, in a responsible manner? So part of what makes Copilot so powerful though is that it has access to that information. And so by saying, okay, we're not going to give copilot access to layoff list xls or something like that, right?

It, it takes away from the functionality I think of, of the product. But again, this is why we can't have nice things, right? Because you know what, there's a given a take there.

How does Lightbeam help with that? Uh, so what C Lightbeam does is it would continuously ma uh, monitor who has access to whose data. So if you look at, um, the whole, um, world, which is there today, people have, uh, people are talking about who has access to what data.

So there's a small difference between who has access to whose data versus what data. So just to, uh, elaborate a little bit further, let's take this case that you have a nine digit number, which is social security number, which is there now this is the what of the data, which is there now the moment, and on its own, it has got no meaning because if, uh, I give you nine digit number, is it truly sensitive? I don't think so, but the moment I associate that nine digit number with Alan, it has got a whole different meaning because now whole of your identity is exposed to everyone.

So there is, uh, whose data becomes a really important part of it. So what Lightbeam is doing is monitoring who has access to whose data. So for example, going back to the, uh, earlier conversation, if HR has access to employee data, that's all fine, but if, uh, uh, sales has access to HR data, that's a total no, no.

So this is what Lightbeam is monitoring within the organization continuously. So you can just, uh, put it on autopilot wherein you are configuring these broader rules in terms of who has access to whose data. And the moment this policy is violated, lightbeam will automatically go and revoke those accesses which are there.

So in this particular case, if, uh, you have given access to everyone with respect to the layoff which was happening, or someone else who shouldn't be having access to that light beam will go and revoke that access and clean up the cache, which copilot, copilot might have, uh, built in within it. And that's way, that's how it is going and maintaining the security posture within your organization. I love it.

Very good. Um, how is this packaged like Beam though? Like, so is it by how many users you have?

Is it, I mean, I'm just trying to think logically, how would you package and sell this? So, so it gets, um, so you are asking about what is our licensing model? Yeah, it's licensing.

So, so it is, uh, we charge, uh, organizations based on, uh, two different, uh, categories. One is to say that one is if you're using Microsoft or Google, it is on a per user basis. Uh, and the, or the other one is based on the terabytes of data that you are managing.

So for example, if you are using an S3 bucket, you might be having a petabyte of data. So the cost would be just based on the petabyte of data, or you could be having in different applications that using petabyte of data. So the MO model model's very simplistic there.

We will just be looking at how much is data under management within Lightbeam, essentially how much data lightbeam is monitoring for access as well as the content there. And the other part of it is if you're using Microsoft ecosystem or Google ecosystem, because people are used to paying on a per user basis. Yeah.

Per user. Yeah. Okay.

Correct. So, you know what Kind just make makes me laugh. Aman, you're throwing around petabytes a day are like, they're nothing, right?

Uhhuh, we never, we used to dream about petabytes, you know, terabytes, gigabytes, I mean, you know, but now we're talking petabytes, like it's every day, it's just a couple petabytes, right? Uh, crazy, crazy for those of us. Exactly.

Exactly. Like, uh, the world has evolving so fast and, uh, organizations have been collecting so much of data. In fact, we are in conversations with companies who have collected close to 87, 89 petabytes of data.

So, oh My God, that's kind of a, uh, and now if you go and ask them to say how much is sensitive data within that 87 petabyte, they have got absolutely no clue. No idea, no idea. And not even tagged as such.

This is the funny thing, which is they like, um, during the early stages of Google, uh, they would count for like, it is a 40 GB of data, and it's a huge amount of data on which hold of the web search was working. Now It's crazy. I, my personal Google Drive, you know, from our Google package is eight, eight terabytes, I think, or something like that.

It's crazy. It cra I, yes, I don't even, but yet I use it and I'll probably fill it up. But that's the world.

We find it. And that's why you need a ing right? Because you don't, there's so much there even, you know, Even make you, I go to you and ask you to say that, look, do you know what data are you carrying in your eight terabytes of data?

You have absolutely no clue how many places you might have scanned a document. Your passport information might be there. Do you know how many copies of your passport or driver's license you might have created on your own Google Drive, which You might have.

I know for a fact, yes. It's, it's just crazy. And then if you imagine like for your mortgage information, but, uh, this is not the place where light beam plays in, but it is needed for each and every individual also to manage the data.

Uh, I agree with you. This is, well, you know, it it, I forget what the term is, but it's, uh, the more you have, the more you need, right? Yeah.

And that's what it is. ai. We appreciate it.

Uh, keep it up and, and you know what? Come back and visit us. Keep us abreast of, uh, developments there.

It sounds like, you know, you're doing this five years now, right? It's, you're on your way. Please, uh, come back and thanks for being on Text Strong tv.

Perfect. It was pleasure talking to you, Alan. And uh, thanks for having us here.

Yes, Haman Shukla, uh, CEO co-founder at Lightbeam AI here on Text Strong tv. We're gonna take a break. We'll be right back.