Classification of data is generally defined as a process of organizing data in such a way that it can be used and protected more efficiently. At a basic level, the classification process makes it easier to find and retrieve data, and we learn how to classify it effectively.
The classification of data is of particular importance when it comes to risk management, compliance and data security. The types of data classification are often relevant as a starting point, followed by the database itself. Public data is often presented as public data, while limited data is of high security level and represents the most sensitive data.
Sensitive data should be kept in a secure place where it belongs, such as a secure storage facility or private data center, and not in the public domain.
Data management policies and procedures are necessary to ensure that data is collected, structured, organized and stored in an appropriate manner. If properly implemented, this approach will provide an operational framework for employees and third parties involved in the storage, transfer and retrieval of data. Only when all this is in place can a data management framework be implemented.
If you cannot define what data is, how it is collected, where it is currently stored and how to access it, then you cannot regulate it any more. Federal and state laws require that access must be limited to this type of information.
Ethical, security and data protection concerns are among the most important factors in the academic community when it comes to access to institutional data and the use of data for research.
The university is committed to establishing and maintaining quality standards for data, while complying with the requirements for data protection and compliance, including relevant concepts and constructs of information security. Data governance is the management of the data used in the company, which includes a defined set of policies, procedures and procedures for its implementation. These directives aim to improve access to data, accuracy and integrity by applying appropriate security controls and safeguards for risk management.
To protect university data, the guidelines provide a framework that enables the university to comply with federal and state laws, regulations and guidelines regarding data management, classification and incident response. Policies and procedures should be well defined and easy for staff to interpret when promoted to comply with the rules. It contains guidelines for accessing and using data responsibly and for the procedure in case of data compromise.
Classification of data is more urgent than ever, especially for data of EU citizens. The categories should include, for example, the type of data included in the classification, the type of data transferred or stored and the potential risks associated with a breach of security policy.
It is vital that companies classify data in such a way that everything covered by the GDPR is easily identifiable and that adequate safeguards can be put in place. The highest class of data is taxonomy, which defines the most common types of information, such as names, dates of birth, addresses, phone numbers, email addresses and so on.
First, the CDO is motivated by the need for a data classification taxonomy that lists the classes of data sensitivity and shows how classification is linked to the defined privacy policies. Alternatively, data protection provisions in the GDPR could be implied, which also need to be folded into the classifications of taxonomies.
Second, the CDO should introduce an evaluation of the data classification of UNSW guidelines for data handling for all systems and repositories. The owners of data systems at UN SWS are obliged to determine their responsibility for the processing of their data. The evaluation of the classifications of the data will then determine how the UNWS guidelines for data handling are to be applied to the system or data repository that is classified.
Below are the steps needed to complete the process of data classification for all UNSW systems and data repositories, as well as the implementation of the CDO’s recommendations.
In this context, data management should be considered as an important part of data management. Data management refers to the way data is managed and protected as an asset and is a key component of the UNSW Data Governance Framework (DGF). The process of data classification is regulated by links and handles as well as by the use of tools such as the Data Management Framework.
Data governance is best understood as a process for creating a uniform and appropriate use of an organization’s data. This goes beyond the classification of data, which makes it important to adjust the level of data management to take into account the needs of the organization and its data management policies and procedures.
The fact is that data classification and data management have long been at the heart of the security framework. There must always be a focus on the application of security wherever valuable data flows, wherever and wherever it is.