 Hi, this is your host of the park and welcome to another episode of T3M or topic of this month and topic of this month is Data and today we have with us. Lizzie the more co-founder and CEO of telescope. Lizzie is great to have you on the show Thank you. Thank you for having me. It's my pleasure to host you here today And this is the first time if I'm not wrong. We're talking to each other So I would love to know a bit about the story of telescope and you know What is the problem that you're trying to solve? I think the problem that we're trying to solve is it's becoming harder and harder to secure and manage data at Medium to large companies as well as like security privacy and data teams are just drowned with like manual work And are constantly doing like operational work and overhead And so we really want to help alleviate that problem as well But if you look at the data movement, I mean a lot of companies are doing it Is there any specific either market or problem? Area that you are looking at yes in the security and privacy space So the more data that you have the harder it is to secure because you can't secure what you don't understand And so you need to gain an understanding of your data. Where is it? Where are you storing sensitive data? How secure is it being stored in order to help like help secure your customers information? Talk about how you have seen the evolution of data from the early days and Today's cloud native Kubernetes native word. I think in the early days data was kind of guarded just because with on-prem data centers Engineers were not able to touch data. It was always guarded by DB administrators And now in the cloud native world anyone can create a data store Anyone can spin up a cluster and start storing and collecting and transmitting data with just a click of a button And so that's that makes it much harder to safeguard if you can't really control what's going on You're saying, you know that different teams now have access to data First of all, it is data is hardest challenging What are the challenges that they face when they kind of interact with data? At the same time from your perspective the reason you created the company So the risk that you see that when these teams have access to the data So the big thing is yeah, if anyone can create a data store with obviously like I used to work at Airbnb Right and at Airbnb any engineering team could create a database Obviously you would need pull like request approval because like it would need approvals But sometimes those teams are overloaded with approvals and so things might slip through the crack So anyone could really spin up a database And with that happening the company doesn't really know like what that team is starting to collect like the team could say I'm collecting non PII, but how do you really know that they're not collecting and storing new types of sensitive data? And so these are the types of risks that occur is like not really knowing what types of data are being stored And where and where they're going so for example all these third parties that Company that kind of employees could be sending their data to as well Is it only that what kind of data they are creating or is it also about? What kind of access they might have to data because we can talk about the whole access management So talk about the yeah Let's look at data from the holistic point of view Yeah, so there's obviously at the data that people are storing and then it's how it's being stored obviously the configurations in the cloud are pretty Abstract so it's hard to know what you're doing when you change some configuration That might leave the data open to the world and then there's obviously the access control at most companies I assure you access is always granted, but almost never revoked So if a team no longer needs access to the data Oftentimes their access is still there even though it's not needed And so that leads to a lot of risk because the more people have access to your data the worse it is Is there any specific industry that you work on a focus on or it's like data in general securing the data It's data in general so anything consumer focus like the disease it could be fintechs It could be healthcare related technology companies Or just consumer type companies. What kind of use cases that you folks are mostly dealing with What are the biggest challenges that you do see companies are facing? we've talked to I think 200 companies even before starting building Calisco and Something you hear again and again is they're still manually labeling data So they're manually pinpointing where they're storing personal and sensitive data So they have this spreadsheet that stores all the columns and all the tables that contain PII and that causes two problems one is like this manual work is awful No one likes to do this and to it's often wrong and like point in time So that spreadsheet gets outdated as soon as it gets created and no one really thinks to update them So that leaves them at risk for non-compliance with privacy regulations and security breaches And so this is what we're seeing a lot of companies do and they kind of want to move away But the tools in the market don't really provide it of value for them to move away from this stretch And so the way we're automating this in telescope is For a lot of our customers what we do is we connect to their cloud account such as AWS GCP snowflake Azure and we automatically inventory all the data that exists there and Automatically classify where they're storing PII and who that data is about so we can tell you we found a customer's address in this Table or we found your employees first name in that as three bucket And so once we have this information then our customers are automating on top of our results So one company is using our results to automatically mask sensitive customer data and snowflake So that's one use case another use case is companies are automating data deletion on top of us for Compliance purposes. Do you also feel that we need a culture shift or you see that a culture shift is also happening or how we handle data should become a Organization wise because as you said everybody is creating some data So number one is that do we need a culture shift when it comes to handling data and number two is that is it already happening? Or you think that we have to do some work to make it happen. I think it's definitely already happening I think the culture shift needs to happen You can write as many policies as you want in terms of how to handle data But no one really reads those policies So they really need to be integrated in the software development life cycle and you see that already happening like Airbnb my whole security The whole sick data security team was software engineers So we were all like implementing coded coding and like Integrating the policies within the development life cycle and with telescopes like you can easily integrate all of our APIs within your Software development life cycle to prevent those reaches or prevent that data from being stored in the first place Oh, when we look at company like Airbnb, these are big companies and also they are more data-centric companies Let's also look at average companies who once again they are as you said, you know Don't know which kind of company they're they're dealing with dealer How much is this happening at that level and if you feel that hey because we have to secure them as well I think all companies are start collect a lot more data than you would think And they also a lot of that data is useless And so it's just sitting there and it's not just the Airbnb's of the world But most companies store at least terabytes if not petabytes of data And that's common practice and I think now you're seeing more modern companies think about data minimization Whereas those like not as modern companies still are collecting more and more information in the name of big data Yeah, how companies irrespective of whether they're mature or big companies or startups whether they're greenfield of deployment when they look at data so that they can have a very You know kind of data-centric approach from from the very beginning the one recommendation I have is don't think about like collecting as much data as you can is Think about it in a way of collecting the right data and the data that's going to improve your metrics whatever those metrics are because If you're collecting everything and anything then most of that data will sit unused and you won't truly get the value out of the data That you would if you thought about it and a holistic sense Yeah, how are you kind of catching to notice to you what kind of solutions offerings you have at telescope? We offer two things one is to help you secure the data that you've already Collected and two is secure or prevent data from being stored in the first place So for the second part we offer an API that can classify and redact data Before it gets stored so you can plug it into your code base to prevent sensitive data from leaking into logs From collecting sensitive data and storing it in a database or even fraud preventing users from sending credit card numbers to one another Things like that and then this the more platform based solution to help you understand the data that you've already collected so we discover classify and Automatically help you remediate any security and privacy issues you have within your cloud data stores and third party vendors I mean if you look at as you're discussing This is kind of crowd migrant we live in a data driven word and then we talk about security and I talked a lot things first of all There's a lot of evolution happening with the observatory monitoring tracing But what whenever I talk to folks, you know one thing that keep happening is that you know All happen is that they get a lot of no notification alerts a lot of fatigue happen there While it's good to kind of build a culture of more like Approach to our security of data, but also we have to make it easier for teams also So they can continue to focus and then with a with how getting either distracted by those alerts or it just like There are so many others that actually the real issues can go under the carpet So talk about how you folks are dealing with this to to simplify it make it easier for teams Yeah, definitely I mean the biggest like thing that people people complain about is alert fatigue due to the amount of false positives So the two ways that we can help with that is one we have the best in class classification system And we classify things in two ways So one is saying you have an address In this table and it's an address and the second one is saying who is this address about? Is it a customer's address? Is it a public address? Is it? Is it a restaurant's address so with these things you can get not only okay We found like sensitive information in this table, but we can actually truly tell you this is your customer's information So you need to deal with this ASAP And then second of all we don't We provide APIs and so we want to steer away from the alert based Platforms and want teams to take our data and automate on top of it instead of just getting alerted And so once you can provide that trust that your classifications are accurate Then they'll be comfortable enough to use your APIs and automate automatically Instead of just getting alerted and manually sifting through those alerts and then taking action manually as well Is it thank you so much for taking time out today and of course talk about the company and Evolution of data and how you folks are helping organizations, you know securing the data Thanks for all those insights, and I would love to chat with you again. Thank you. Thank you so much swap