Identify Sensitive Data in Your Databases With the Classification Engine

Identify Sensitive Data in Your Databases

Having visibility into where sensitive data is stored is the foundation of any cybersecurity and compliance strategy. Classification enables businesses to prioritize security efforts, mitigate risk and meet regulatory requirements by identifying specific data groupings that must be protected.

Data classification is a set of processes that identify and tag data. Its benefits include:

Data Discovery

Discover, classify, and label columns that contain sensitive data in your databases with the SQL database discovery and classification engine in Azure. This process helps you identify and protect sensitive information in your databases, as well as meet standards for privacy and compliance.

Today’s businesses store petabytes and exabytes of data across data centers, file shares, networks, cloud storage, backups, and more. Yet most lack the visibility into this data they need to protect it from emerging cybersecurity threats and maintain regulatory compliance.

The key to overcoming this challenge is the combination of automated discovery and classification processes. These provide critical benefits, including heightened visibility into your data, helping you comply with regulations, and reducing the risk of costly data breaches. They also democratize data by making it easier for non-technical business users to uncover new patterns and potential points of vulnerability – and create an agile, secure workflow for data.

Data Security

Data classification is a critical step in the information security process. Classification ensures that a company understands the value and potential risks of its data. It also helps companies comply with regulatory mandates like SOX, HIPAA and PCI DSS.

Information classification tags files and digital transactions with labels that identify their sensitivity levels. These sensitivity levels range from high to low. High-sensitivity data includes documents that, if stolen or destroyed, could have a catastrophic impact on an individual or organization. This level also includes sensitive internal data, like operating procedures.

Medium sensitivity data identifies internal documents that, if disclosed, would not have a significant effect on an organization or individuals. This category also includes non-sensitive internal information, like transaction receipts. Low-sensitivity data identifies public information that is available to everyone. It can include information like customer profiles, employee agreements and company brochures. Data can be classified in three states: at rest, in use or in transit.

Data Management

Data classification identifies and tags information with varying levels of security requirements. The process also helps companies understand the value of their data and the impact if it were lost, stolen or compromised. It enables organizations to identify and protect sensitive data while facilitating risk management, record retention and legal discovery processes.

A comprehensive data classification program can help businesses find redundant, extraneous and forgotten information that might be wasting space or hindering efficiency. It can also reduce the amount of sensitive data that needs to be stored to meet compliance standards. Well-written procedures and policies that define categories and criteria can make it easier for employees to promote effective data stewardship practices. The policies should be clear and considerate of the security and confidentiality concerns associated with each data type. Each category should be categorized into sensitivity levels, such as high, medium and low. This makes it easy to access and retrieve regulated information on demand, as required by modern compliance regulations.

Data Reuse

Data reuse is one of the most important aspects of research. It allows different researchers to gain new insights by analyzing data that have been collected for other purposes. It also makes it possible for scientists to collaborate and share their findings with other researchers. Ideally, data should be well-described and curated, and it should be shared under clear terms and conditions.

Data-driven innovation and policy initiatives rely on linear frameworks of traceability, transparency and control. But, as Barad argues, machine learning generates reuse entanglements that can never be reduced to instrumental relationships of master and tool.

These entanglements are driven by platformed economies and deep learning processes that create new taxonomies, practices and political dynamics. To understand them, it is essential to broaden analyses of the politics of data from bounded assessments of data and algorithms toward more enmeshed analyses of reuse entanglements. For example, use of identifiers that link people, concepts, calibration targets and comparanda in an archaeological dataset would facilitate the reuse of that data by providing researchers with access to the broader context in which they are operating.