Securing Data in Online Learning Systems by Automated Classification
Securing Data in Online Learning Systems by Automated Classification

Summary/Abstract: Because of the huge importance data has for the modern business environment, data security is one of the main concerns leaders have, no matter the sector of activity they operate in. Modern learning environments try to leverage technological assets in order to address the current educational needs. Using the modern technology is bringing huge benefits for organizations in terms of minimized costs, rapid deployment, increased adaptability and reliability, rapid and flexible access to services but increases the exposure of organization’s data to malicious actors. Data classification, in the context of information security, is the classification of data based on its level of sensitivity and the impact to the organization if that data will be compromised. The classification of data helps determine what baseline security controls are appropriate for safeguarding that data. By grouping data in a limited set of "classes" that have similar compliance and security requirements, we have the basis to prioritize security investment and to apply common risk-appropriate controls to data across an organization. Without it, each data type might have its own set of handling requirements and it would become impossible to manage and communicate effectively. Classical schemes rely on humans to analyse information and categorize it. This approach has a series of limitations which hamper their effectiveness, negatively impacts the organization security environment and lowers employee efficiency. This article analyses the possibility of using classification algorithms running on automated machines integrated in the organization business environment to implement data classification schemes. Organizations need to realize that data classification does not equate data security.

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