AI-driven Zero Trust Data Operation Security Platform

DataSecOps creatively establishes an AI-driven Zero Trust Data Operation Security Platform, providing users with automated data value discovery and data security services. It achieves comprehensive discovery and risk analysis of multi-source data assets of various types, full-process data flow governance and risk perception, as well as adaptive and precise data security protection.

The platform integrates the concept of DataSecOps (Data Security Operations), based on a Zero Trust data security architecture and driven by artificial intelligence technology. It performs non-disruptive mapping of the entire data business process, ensuring that security protection and data business operations run independently without affecting each other. The DataSecOps Zero Trust Data Operation Security Platform manages and tracks various types and sources of personal privacy data and business data, establishing a comprehensive view of sensitive data assets. It promotes fast data flow and secure collaboration, prevents the misuse of sensitive internal data, and provides adaptive and precise dynamic protection based on data roles and user risks, creating a data security ecosystem centered around data operations.

Comprehensive Multi-Source Data Asset Discovery and Risk Analysis.

Help users organize and inventory the types, quantities, and distribution locations of data assets within their organization. Automatically create a comprehensive view of data assets, and identify risks related to data exposure and misuse. Leverage the platform's built-in library of tens of thousands of data format recognitions and thousands of data classification models, combined with advanced data content deep parsing and artificial intelligence classification technologies, to conduct detection and analysis of all types of data assets from multiple sources within the organization. Automatically create data asset classification and storage directories for users, supporting multi-dimensional rapid searches for file content, personal information, and data lineage relationships.

Comprehensive Data Flow Governance and Security Protection

The platform maps the entire data business process of users without any modifications to the business or network, ensuring that the data tracking and risk protection system operates independently of the business process, without interference. This helps users standardize data usage compliance, promotes rapid data circulation and secure collaborative sharing, and prevents internal data misuse risks. The platform focuses on protecting the value of flowing data, labeling and tracking the usage and changes of various types of data, including structured and unstructured data from various sources. It supports intelligent aggregation and rapid traceability of data usage chains, enabling real-time awareness of data misuse and circulation risks.

Adaptive Precise Protection Based on Data Roles and User Risk

Lightweight, non-intrusive full-process environment detection and risk perception for data. Based on zero-trust identity role authentication and environmental awareness, driven by distributed risk identification, dynamically perceive risks in the use of data across various business process links and data roles. Provide the most precise and suitable protection measures according to differences in risk levels, usage environments, transfer links, and user roles, without affecting the normal flow efficiency of data and the normal operation of business.

Security Protection for Application Systems without Modification

No modification to the network and applications is required. Based on a zero-trust data security architecture, it ensures the security of application system access and data usage. Automatically hides critical business functions from unauthorized devices or access. Continuously assesses data security risks for legitimate users and authorized devices. Utilizes seamless data security sandbox and micro-segmentation storage technologies to create multiple security levels for application system access and data usage environments. Establishes a zero-trust data security channel between the application system and users. Accurately identifies user identities, dynamically defines application system access boundaries, and adapts access permissions accordingly. Prevents various unauthorized access, identity spoofing, and illegal downloads, ensuring the security of business data usage and outflow.

Zero-Trust Data Security Workspace for Critical Business and Data

Based on a zero-trust data security architecture, it uses adaptive seamless data security sandbox technology as the core to build a zero-trust data security workspace on the user's computing side. This workspace can connect with the user's key data systems, core applications, and important sensitive data. It does not change user habits or affect business processes, creating an isolated environment for the use of ordinary and sensitive data, ensuring the same level of security for data online and offline, preventing various risks such as illegal downloads, intentional or unintentional dissemination, and internal data misuse. Effectively prevents malicious software ransom attacks and user malicious leaks. User identities are intelligently bound to the security sandbox, dynamically defining data usage boundaries, and adjusting workspace permissions based on user risk changes, building a risk-adaptive trusted data usage environment.

Content Introduction

Data security leftward shift is an inevitable trend in the development of data-centric security in the digital age, enabling data security capabilities to be preemptive, continuously tracking the data processing and usage process at the first site of data processing, and spanning the entire data processing and circulation link, uncovering the true source of data risks.

Background

Against the backdrop of humanity entering the data age and the digital transformation of enterprises, the amount of sensitive data within organizations has surged, business processes have become increasingly complex, and the number of user roles that come into contact with sensitive data has increased. There is a significant risk of data spreading and misuse during internal storage and circulation, with malicious leaks and attacks on stolen data on the rise.

In the past, security protection had a clear distinction between internal and external networks, with the assumption that the internal network was secure and trustworthy. Security actions were concentrated at the boundary between internal and external networks, deploying a series of detection, monitoring, and interception measures to block the possibility of data leakage. With the increase in data usage scenarios and the comprehensive migration of data to the cloud, the number of nodes involved in the data lifecycle has increased, and data forms have become diverse. Before data reaches the boundary, it is prone to unauthorized misuse, disorderly spread, and chaotic storage during internal circulation due to lack of monitoring, leading to increased risks of malicious leaks and attacks. Boundary protection alone leaves significant blind spots in data protection, leading to the leftward shift in data security.

To combat the new challenges in data security, we need new protection concepts and security architectures to ensure that data is securely stored, used, and shared in storage, applications, and terminals. This requires extending data protection measures from traditional boundary interception to the entire data operation process, meeting compliance requirements while truly identifying and controlling risks. Early identification and tagging of data assets within the organization, as well as recording the trajectory and state changes of data flow, have become powerful breakthrough methods. Security strategies should make fine-grained, dynamic controls based on the risk changes of the data itself.

Practical Applications

DataSecOps believes that by embedding security attributes in DataOps, it is possible to achieve the technical implementation of data security left shift. DataSecOps is an automated security approach, with the basic idea of continuously tracking the entire process of data processing and usage at the first site of data operations. This allows for monitoring the entire process of data transformation and flow, directly addressing the polymorphism and multiple copies of data, and identifying the true source of data risks. This extension of data security capabilities is an inevitable product of data security left shift.

Beijing DataSecOps Technology Co., Ltd. has developed a Zero Trust Data Operation Security Platform to address the trend of data security left shift, focusing on data identification and data flow mapping, automated protection, continuous risk monitoring, and data security compliance assessment. Through the DataSecOps protection concept, it is possible to more comprehensively identify data, more effectively protect important data, and identify risks earlier, breaking down communication barriers between business, IT, and data security teams, and overall reducing the cost of data security construction while increasing its benefits.