Christoph Weiss - Next Generation ML based Data Intelligence Around Process Automation (DSGVO, GDPR, etc.)

Step button next. Great.

Just a, a short overview. I joined big ID one year ago. We were about 40 people. Now we are 150. We expect to be next year at the same time, about 250 to 300. So big growth. Our headquarters in New York development centers is in Tel Aviv.

So yeah, we have a global coverage today because we are focusing mainly large accounts, enterprise accounts. This is some cases very important because we need to implement solutions on site, in different data centers. We are in the range of D GDPR and what we find as a basement.

First, you need to, of course, to see what kind of data you have and what we see as a challenge in most companies is yeah, the existing approach of finding data is not really working in regards of privacy related information. So the challenge today is really that many customers realize that the today's and the existing discovery tools and classification tools are failing because they cannot find personal information. They can only find social security numbers, names, or, or credit card numbers, but we need to find information based on person.

I mean, even if a lady 20 years ago was not married has another name than today. And if you look today for the new, with the new name, you don't find the old data. So what we are doing is with our machine learning, artificial based technology, we look also at the objectives and the attributes below the name. And if you find a connection with maybe a birthday and the same phone number, we know that data belongs also to that person with a very high chance.

So with this, we are able to provide the full picture on, on target data instead of the old way where you just have like an Explorer typing in and you get what you type in. So we do this with rethinking the data discovery approach.

So, yeah, it's important. As I mentioned that we do identify personal information, not only personal identifiable information, which are security numbers and sec or credit card numbers, we do it with the next generation machine learning technology.

And yeah, the important thing is also that you are able to connect to all your data sources you have, because you don't know before the scanning where the privacy data is residing. So you need to get a 360 central view on the target data to be able to process it as a next step here, you see an example how it's working.

You know, when we see the Gordon steward, we find on a PDF attachment, we find there maybe a policy secure insurance policy number in Oracle database. We find some additional information we see then in mail mail addresses. So we can all correlate all those and connect all pieces of data together to deliver the complete picture, which is required based on this. If our own GDPR, if somebody's asking for his data, you need to provide everything. And that's a challenge today. And then also, if somebody wants to execute the right to be forgotten, you need to delete everything.

So if you miss pieces out here are, you are not complying. And if somebody realized that part of his data is still available, even if he asks for deletion, this can create a big risk. So how do we do that? We have a mining site, which we do a connections to the data sources or Oracle databases exchange office 365. It can be in a cloud or on-prem the data sources. We do it with the agentless approach. That means you don't have to install anything on your data sources. We do it with the read only access. That means there is absolutely no risk to your data because we do not change anything.

As I said, you know, any data type. So we have a huge number of possible connectors. They're all based on rest API. And the data sources can be in the cloud like Salesforce, Workday, whatever you have or on-prem, we don't care. It's a mixture as possible in the middle.

We have the machine learning part where we train the system, what he has to look for, because you can use it, of course, mainly today in privacy management area, but more and more customer wants to use it also for different data, because they want to find data, maybe financial transaction data or insurance cases, data, where which can also be splitted all over. And then we do the reporting, or we can extract the results because we have then a central 360 view on the results on the target data. And then you can export it to your existing tools, can be privacy tool can be business trends.

Tools can be everything. The input and output is based on rest API. So pretty modern.

And yeah, so very, very flexible. Yeah, it's a very enterprise modern enterprise architecture based technology. We will not do copy. We will not copy any data. The only thing what we are doing is we are creating indexes and pointers into our platform so that we know where we found in which data source, in which Chima, in which area of, of data source, we found the privacy related data so that we have always access.

If somebody's typing in a name, we have always access to the latest data, because if you copy it first into a data lake, after five minutes, the data lake is already old and it doesn't help really for the future scanning and information flow. Yeah. It's as I mentioned, API first Docker, Kubernetes based microservice.

So yeah, it's a really modern approach. And from that point of view, yeah, the latest technology, what makes us also very unique is the broadness of our connectors. That means is unmatched data support with automa. That means, you know, if, if you want to find privacy related data, we don't care. If you want to connect to file shares, exchange servers, office 365 structured unstructured data can be in the cloud can be on site. It can be even in mainframes, if there are unique applications only written by yourself, we have a software development kit to get access to the data sources. Yeah.

So the next point, and of course is taking action. You see here a few screenshots. If you're interested, you know, we are open for online demos. You can just contact us outside in a booth.

We will, of course show you the details. We are mainly talking to privacy, to security protection and to data management people. So that's then the application layer where we also provide informa applications on top of the data recovery, where we can do all those process automation out of the box. That's my last slide. Yeah. I see you get nervous. Yeah. So we have a, a big partner ecosystem. We have SAP as a resale partner, global resale partner.

We are enterprise lift SAP, but we work also with others and depending what the customer, what kind of solution the customer wants, we can choose a mix. Sometimes customer have already some applications installed and then we just can bring up the complete picture on the data and help to increase the value of the investments they did already. Any questions? Thank you very much. Thank you.