Transcript
My name is Michael Schneider, Director and Senior Principal Architect at Zscaler. Today's topic is about data, and more importantly, about data security. Data security is a topic that's top of mind for all organizations, or better said, should be. Why is data so important? When you think about the move that many organizations have undergone, where networks have been transformed, data centers were moved to public cloud, there are no servers. There might not even be networks. There are, of course, PCs and laptops, but the data that remains the core asset to organizations, and that is what matters the most and should be protected the most. In addition, thinking historically, when we look at the growth of data over the time, and thinking about it, reveals when we see that, for example, in 2010, we only had two zettabytes of data from a global perspective. That already sounds like a lot. However, the problem reveals when we would think about the growth of data over a certain period of time. We see here the growth indicated almost by this exponential curve that we see. That leads to the situation at the scale that we have. You see, it's almost exponential, bringing us to an amount this year in 2025 of 181 zettabyte. That's the explosion of data, the sprawl of data, which, when thinking about it, makes the problem even worse. More data, more data to protect. And data protection is not just a technical topic. Obviously, protecting data is in the interest of governments. When we think about it, and we consider the various standards that have been created globally, we see that in various jurisdictions, several areas of data protection were raised. In the EU, we have GDPR. We have the Artificial Intelligence Act. In India, we've got DPDP. Other standards that are all aimed at safeguarding data exist on a global basis. And with that, compliance becomes a very, very important topic for organizations, considering that those standards mandate data security, essentially. AI itself is a bit of a game changer. It motivates users to deliver their data to the various AI tools. Why would they do that? AI lifts from the input of users. It lifts from the data that users supply. And it also rewards users. It rewards users in a way that it fulfills a task that otherwise the user would have to do manually, analyze my data, write me an article, write me a document, research source code. All the things we've discussed are obviously a very good reason to think about safeguarding your data, making sure the data is protected against loss, leakage, or other means of exposing it. Next, we want to talk about how the Zscaler data security platform can help organizations safeguarding their data, protecting their data, secure their data in a fully cloud-connected ecosystem. Let's understand where data needs to be protected. In a cloud-connected ecosystem, nothing is linear. I mean, that's not different from legacy networks. But this provides a problem because ultimately, we end up in a scenario where we need to provide various enforcement points for protecting that data. We see technology such as a CASB, which is also called the Cloud Access Security Broker. We see the network DLP. We see web proxies. We see the endpoint. All of those are areas where data is either transferred, stored, or handled. When I would now go ahead and, for all of those various points, would acquire point product solutions, I end up in various, various problems. One problem, the lack of visibility. Lack of visibility means, well, if I don't have one point covered, I'm blind. I might not see it. Also, when I have multiple products, I end up in excessive alerting. Excessive alerting causing alert fatigue, which is a phenomenon in which I get so many alerts that I start to see being blind. I don't see the forest for the trees. I can't see the important things happening. It's costly, complex. Multiple products cost a lot of money. Integrating them is complex, takes time. Takes also time to operate as I end up having multiple points of failure, which then also leads to inefficient operations, meaning my operations teams, my administrators, need to learn multiple products, multiple different workflows, look at multiple different alerts. Considering all of that, when I apply those problems that we just talked about to this environment, I see that in all the various areas that I try to cover, actually more problems appear than I find solutions. And that is clearly a reason why going point product creates more difficulty than it helps. And that thought led us at Zscaler to build a unified platform that covers those areas and provides enforcement of data leakage prevention or generally said, data security policies across all of them with a unified platform, which is not integrated, but which is converged. So, integrating out of a single glue and providing unified workflows and mitigating those problems that we've just talked about.
