Hello. I am Bruce, a Backend/Frontend team leader at CHEQUER Inc. In this episode, following the previous one, we will discuss data security related to Data Discovery in more detail.

Before diving into the main content, a brief introduction of my personal background may help in understanding the modern business environment. Over the past 20 years, I have accumulated diverse and interesting experiences in the search domain, including developing large-scale services in distributed environments, data collection, and data refinement.

As I develop security solutions, I find myself reflecting on my career transition from the search domain to the security domain. Looking back, I didn't intend for it, but I discovered a close relationship between the two areas, 'search' and 'security.'

In the security domain, it is necessary to quickly and accurately recognize data such as personal information and perform tasks such as anomaly detection using AI technology. This is where the utilization of search technology comes into play. I believe that the current situation, where the security issues regarding users' information have become more significant alongside the advancement of search technology, is the result of this connection.

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‍How data is transforming the way businesses operate and make decisions‍

The importance of data in the modern business environment is an area that everyone is aware of and empathizes with, no matter how much it is emphasized. This is because companies in various industries secure a competitive advantage by effectively collecting, analyzing, and utilizing data. At the same time, it presents significant and important challenges in terms of security.

Companies use business intelligence tools to analyze various aspects of data, such as customer behavior, market trends, and product performance, in order to make strategic decisions. Decisions based on accurate and reliable data are directly linked to the success of a company and can lead to a differentiated competitive advantage.

Furthermore, many companies are focused on providing personalized services based on customer data. Personalized marketing strategies, customized products, and services play a bridging role in promoting positive interactions with customers and increasing customer loyalty. And when a customer-centric approach is involved, companies inevitably deal with sensitive personal information. This is when the need for data security becomes apparent.

As data is considered a core resource that generates value and leads to success in corporate business, the key challenge in the modern business world is to recognize the importance of data and increase responsibility and security levels to effectively manage and protect it.

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‍Understanding the relationship between data governance and data discovery

‍I explained earlier the importance of efficient and reliable data management, and the concept that encompasses all of this is called Data Governance. More precisely, Data Governance refers to the framework and policy system for effectively managing and protecting organizational data.

It aims to ensure appropriate management throughout the entire data lifecycle, including data quality, security, regulatory compliance, access permissions, and metadata management. By coordinating and controlling all data activities within the organization, it plays a crucial role in protecting data assets and managing and utilizing data from a strategic perspective. In this series of processes, the fundamental and essential area is Data Discovery, which refers to the process of discovering and exploring meaningful information from various data sources owned by the organization.

This activity supports various stakeholders such as data analysts, business users, and decision-makers in gaining insights into data and obtaining valuable insights using data.

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‍Introducing DDP: The Ultimate Data Discovery Platform for Efficient Data Management

‍DDP can enhance the security of data and help effectively manage personal information. It is almost impossible for corporate administrators to manually identify and classify data within a vast amount of data without any tools.

In this case, DDP enables the management of massive data by providing powerful data classification and identification capabilities. The following are scenarios related to personal information detection, access control, and anomaly behavior detection.

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First, data is classified using patterns, rules, or machine learning algorithms that represent personal and sensitive information. Data containing personal information is automatically identified and classified, and recorded in a data catalog. Access to identified personal information can be controlled according to rules set by administrators. Access permissions to personal information are granted based on the roles and responsibilities necessary for that information, preventing unnecessary access.

Second, effective detection mechanisms are provided to identify abnormal behaviors of users and systems. To achieve this, normal behavior patterns are learned and used as a basis for detecting abnormal behavior, providing real-time alerts to administrators. For example, if a specific user accesses personal information that they typically do not access or downloads a large amount of data, this is identified and an alert is issued. This allows administrators to respond quickly to potential risks and record incidents through reporting and logging functions. Reports can be used for auditing and compliance purposes, as well as for enhancing security and taking preventive measures against potential threats.

Lastly, data is centrally managed, providing an overall view and perspective on personal and sensitive information, as well as specific data defined by administrators. This allows organizations to gain comprehensive insights into the location, usage, permissions, and security status of data.

DDP can contribute to enhancing security levels through scenarios such as protecting personal information, controlling data access, and identifying abnormal behavior. This plays an important role in strengthening regulatory compliance and strongly supporting organizational data security.

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So far, we discussed the conceptual story of data security using Data Discovery, which acts as an essential element in understanding CDPP (Cloud Data Protection Platform) that CHEQUER aims for. In the next episode, we will delve more into the actual operation and process of DDP. 

“We hope to see you again with another informative topic. We ask for your continued interest and support in the next episode."