That cloud, how to protect the whole link of industrial big data?

Produced by Tiger Sniff Think Tank.

Edited by Huang Siyu.

Title Diagram Visual China.

With the rapid development of industrial big data, the full-link security of industrial big data processing on the cloud has attracted widespread attention. How to effectively respond to data breaches, malicious attacks, and other data security risks?How to ensure the security and privacy of data during full-link data transmission?What role do cloud security service providers play in cloud security for industrial big data, and how do they deal with the above security challenges?

Based on the above questions, Tiger Sniff Think Tank wrote and published"Full-link Security Practice for Processing Industrial Big Data on the Cloud".The research report responds to the urgent needs of the industry for security issues in the whole link of cloud processing, and provides professional reference opinions for relevant decision-makers and practitioners in the industry through practical cases, the core problems, solutions and hard-core technologies faced by industrial big data in cloud processing. Here are the core takeaways from the report.

Challenges and requirements for moving to the cloud.

From the link side, data migration to the cloud faces a variety of challenges in all aspects of collection, transmission, storage and use, such as the old original IT system of the enterprise, the many external flow links of data, the lack of hierarchical and classified management of data, and the restrictions of trusted technology, which are all major problems to be paid attention to and solved by the security protection of industrial data on the cloud.

The practice of industrial data cloud security emphasizes that the whole life cycle of data is regarded as a closed loop, and enterprises build a security protection system covering the entire industrial system by combining multiple fields and new technologies. Through monitoring and early warning, emergency response, detection and evaluation, functional testing and other means to meet the needs of industrial enterprises, identify and resist internal and external security threats, effectively resolve various security risks, and provide safe and credible guarantee for the intelligent development of the manufacturing industry.

In specific practice, we should pay attention to the real-time, stability, and cascading characteristics of data, and take measures to build a closed-loop data security management system in the whole process industrial field according to different scenarios.

This system should focus on "data classification and hierarchical identification, hierarchical protection, security assessment, risk disposal" and other aspects to ensure that industrial data is comprehensively and effectively protected in the process of cloud migration, so as to promote substantial progress in the security management of data in the industrial field.

Three-dimensional integrated data security governance based on business scenarios.

Case: Topsec - Digital Cloud Security Practice of Industrial Enterprises

An enterprise has serious risks of data leakage before implementing data security governance on the cloud, including massive data leakage caused by centralized data storage, separation of permissions and blurred network boundaries caused by virtualization, lagging security functions of open source software, and sensitive data leakage caused by cross-system retention.

In the practice of digital cloud security of industrial enterprises, Topsec has built an overall industrial network security solution with the core concept of "one center, three protections", including a security operation analysis system, a security protection detection system and a security service system. In view of the data leakage and other problems faced by enterprises, through two key technologies: sensitive data detection and protection based on artificial intelligence, and data leakage forensics based on personnel portraits and knowledge graphs, multi-faceted data security protection has been successfully realized.

Among them, the sensitive data detection and protection technology based on artificial intelligence realizes the prevention of data leakage and the learning of network attack patterns, effectively warning in advance and reducing the success rate of attacks.

The data leakage forensics technology based on personnel portraits and knowledge graphs realizes data tracking and traceability by analyzing and identifying network attacks, and protects the security and integrity of data.

The innovation of the project lies in the adoption of a business-based three-dimensional integrated data security governance structure, comprehensive control of products and services, business scenarios, business behavior activities, and data classification and grading methods based on machine learning, accurate identification of data assets, and strengthening data traceability and risk control.

For a more detailed introduction to the "3D Integrated Data Security Governance Structure", please click here"Full-link Security Practice for Processing Industrial Big Data on the Cloud".Fetch. Comprehensive protection of the data lifecycle.

Case 2: Winnut - Factory Data Cloud Security Practice

When Winute solves the security problem of data migration to the cloud for 3C manufacturing enterprises, it first faces the challenge of the transformation of enterprises from labor-intensive to technology-intensive, and the introduction of automation equipment and robots leads to network security risks, such as ransomware, data leakage, data tampering, etc.

In the process of migrating data to the cloud, there are problems such as massive data leakage, permission problems caused by virtualization of storage resources, lagging security of open source software, and cross-system retention of sensitive data.

To address these issues, Winnut has implemented comprehensive security measures. Firstly, various industrial data resources are sorted out and updated through industrial asset combing, and a list of important and core data is formed. This step is to ensure a complete understanding of the enterprise's data resources and lay the foundation for subsequent security assessment and governance.

Secondly, the industrial data security assessment is carried out to provide support for the subsequent data security operation through a comprehensive assessment of data security, and at the same time provide a basis for the construction of data security governance and management system. This step is based on an in-depth understanding of the current state of enterprise data security, and targeted development of security measures and planning of governance plans.

Finally, through industrial data security protection, all-round security protection for all stages of data collection, storage, processing, transmission, provision, and destruction is realized. This step translates the first two steps of understanding and assessment into actionable security measures to ensure that the entire data lifecycle is effectively protected.

This series of security measures enables enterprises to visualize, controll, and manage data security in the process of migrating data to the cloud, ensuring all-round protection of the entire data life cycle. After the implementation of the project, the company effectively reduced the potential security risks, conformed to the development trend of the 3C manufacturing industry, and achieved the goal of business guarantee and risk control.

For more details about "Winute full-link factory data migration security", please click here"Full-link Security Practice for Processing Industrial Big Data on the Cloud".Fetch. Conclusion:

The security of industrial big data is not only a technical problem, but also a systematic project of comprehensive management. By building a closed-loop system of data security management in the whole process of industrial field, enterprises can realize the protection of data in real time, stability, cascade and other characteristics. In the future, the cooperation of regulators, enterprises and security vendors will help build a more comprehensive and sustainable industrial data security order.

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