 Hello. Welcome to the FluentCon North America. Thank you very much for joining this conference today. I'm very excited to have you and people connecting virtually as well. I am Kisa, one of the Fluent community members, working with colleagues and friends to accelerate adoption of FluentD and explore what a new adventure, next adventure is, to bring industry to the next level. It is my privilege to share what we've been working and discuss what we can work together as a community. I also run a city office of a company called Itochu Technosolutions America, Silicon Valley arm of publicly traded global IT solution provider, people sometimes call us CTC. In this session, I will talk about our effort on some of the operations and the security challenges as we've contributed a tool called DIAC tool, which is allowing you to automate some of your troubleshooting process. And in terms of security wise, we contributed a plugin called Sanitizer, which masks sensitive information in your FluentD pipeline to bring another level of data security in your organization. Okay, let's get started. First of all, a little bit about us. We are one of the IT solution providers in California, driving technology enablement around hyperscale and cloud-native technology, such as Open Computer Project initiated by Facebook, allowing people to operate infrastructure at the scale, addressed by data center hardware designs. And cloud-native, patterns of operational practices made available from companies who run their applications and infrastructures at scale and driven by CNCF. Many of you already know, but FluentD is, I would say, a default standard open source software for your very first mile towards the entire observability journey, which provides a unified layer for logging and now supports metrics. It is a graduated CNSF project. There are more than 7K plus contributors across 1K plus companies, and the community is still growing. Well, we see why people start using FluentD and FluentBit, or where people think logs to be distributed and are used in multiple tools around observability. Data generated by machines are not only for application infrastructure monitoring, but for more evolving use cases such as machine learning, utilizing data across different applications and security tools more instantly, and storing for a long period of time for compliance or sometimes for engine purpose. Also, because FluentD has out-of-box capabilities around data processing, such as processing and filtering, more people look at this as a tool to manage data volume, by removing fields and records. However, although those are very important, I would say operation is much harder job, and people spend more time than just selecting a new software. But in fact, there are some of the things that we hear more from our friends and colleagues in the community. Scalability is where we see there are more considerations and practices need to be incorporated to run FluentD at scale. TK, who is my colleague, will talk more about it in the afternoon. Configuration is also another element that does not only matter at the initial implementation, but how you would maintain and make sure how properly FluentD is running in your infrastructure is another topic, which our friends in Caleptia talk today. Now let's talk about a diagram tool first. Through the course of activities, we've identified a few typical problems within a troubleshooting process. First one is a configuration error. One example is a file descriptor in a corner parameter which needs to be increased. Second is collecting necessary logs itself. Lastly, information of your specific systems such as FQJDN, IP addresses, name of your applications, in your FluentLogs or logs that you're delivering to destinations shouldn't be exposed for troubleshooting process. Those are what you addressed with the diagram tool. Diagram tool is a tool to simplify troubleshooting process and make it more secure. This tool delivers the functions you need to automate your manual checking and the data collection processes in order to accelerate the troubleshooting activities. The tool does three things. First one is collecting parameters, configuration and logs. Then validation allows these parameters to be checked if those are configured properly. Lastly, masking sensitive information such as FQJDN, IP addresses, and you can also specify your own keywords by using regex before sending all those configuration logs to experts for troubleshooting. Another error that we addressed was security. Huge amount of data has been collected from machines. Collected data is consumed for several purposes. However, not all operation and security members do not have to see raw privacy data or confidential data within the collected logs. That's where the idea of sanitizer plugin came from and this tool that's masked specified data in a FluentD pipeline as one of the filter plugins. This will allow you to maintain data security and privacy policy within your organization and then potentially prevent risk of data breach. This slide shows you how sanitizer plugin can be utilized. For those use cases where we need raw privacy data need to be masked, this plugin does it within a FluentD pipeline and if you would like to store all privacy, I mean, raw privacy data for compliance or forensic purpose, sanitizer will give a flexibility to allow you to configure what destinations you would like to turn the masking on and what destinations you would like to send raw data as well. Now let's take a look at how this works in motion. I showed a video that demonstrates the sanitizer plugin. So let me play with it now. Let's first see. The example is to mask IP and hostname. Let's first see the configure file. Okay. All right. And then you can now specify, find key, IP and host rules are configured with patterns. These are preset masking rules. So you don't have to write a regex right here. And then let's see. We send logs there. This case we are using elastic here. You will see data already masked. And now let's go to regex. Second, we're utilizing regex. Let's go to configuration here. This case is utilizing phone number as an example plus social security number type of format data. And then we configure it writing regex there. And then sending data to elastic search. And then you see those logs to be sent. Data in elastic are masked. So that's basically how sanitizer works. As we are really embracing the community effort to bring this vendor neutral, unified and robust tools and community to be accessible to everyone, we would like to also make sure your journey of FluentD FluentBit has maximized value for your operation. And even by working together, we will have many people and organizations to bring observability journey to the next stage. Also, to make your production use to be very confident with the confidence, we have a commercial services around FluentD FluentBit, which is called a FluentD subscription network. Please contact us if you need any help. Well, this is for my presentation. If you have any questions or comments, I'll be around. And then hope you enjoy the rest of the conference and have a great day. Thank you.