Under the wave of digital transformation , enterprises have strengthened data governance , protected data security, and built a solid security barrier for the sustainable development of digital economy . This is an objective need for the development of the times. At the same time, with the implementation of the " Data Security Law ", whether from the regulatory side or the application side, the state has put forward higher requirements for enterprises from the perspectives of data governance and data security.
Currently, with the exponential growth of data volume, how to mine data value, how to break through internal data barriers, how to improve data security, etc. are all challenges facing enterprises in the process of carrying out data security governance.
In order to effectively promote data management, enterprises can start from the following aspects:
01 Comprehensive understanding of the data repository
Sensitive data exists in any format in any location: structured, semi-structured and unstructured data; local or cloud hosting environments; dark or shadow data.
Therefore, the enterprise's data governance plan must be able to view all formats and locations, continuously monitoring the enterprise's complete data repository from a single source. This is not only the basis of data security, but also the basis of data governance.
02 Data scalability
When enterprises adopt and implement cloud hosting environments to handle updates and loads, they must ensure that the amount of exponential data that is continuously generated can be merged into existing data assets. At the same time, without affecting the system, forensic data analysis can be carried out to provide audit data for enterprise compliance requirements.
03 Data discovery and classification
This is crucial to data security. Once a data breach occurs, the company can know what sensitive data has been leaked in a timely manner, so the company must ensure that it can identify the structured, unstructured and semi-structured data of all databases and sort them out.
04 Strictly control data access permissions
Enterprises should actively discover and manage privileged accounts and sensitive assets, and define legitimate behavioral policies for privileged users. Data governance plans must be able to automatically identify illegal operations, sensitive operations, and verify authorization. Once there is an abnormal behavior, immediately block and send an alert, and analyze and confirm the malicious user activities of the behavior.
05 Efficient compliance report
Enterprise solutions need to automatically generate compliance reports, including: access users, access reasons and operation details, and can help enterprises take action against abnormal behaviors that exceed compliance requirements.
06 Protect third-party services
For the governance of sensitive customer data shared with partners, enterprises must ensure that the data security and privacy practices of both parties are consistent.
07 Mitigation of internal threats
Internal threats can be difficult to identify or prevent because they are usually invisible to peripheral security solutions such as firewalls and intrusion detection systems. In addition to ongoing security training, the enterprise's data governance program must be able to detect abnormal behaviors so that the emergency response team can effectively mitigate internal threats.
Security is the prerequisite for development, and development is the foundation of security. data security construction is not achieved overnight, but a long-term and continuous construction process.
The first step in data security governance
"Digital Network" data asset sorting and database scanning system
A system with the capabilities of data asset surveying and mapping, data asset classification and grading, data asset risk assessment, etc. It can effectively assist enterprises and institutions to meet data security compliance requirements, while optimizing and improving the management and use standards of data assets.
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