Enterprises face a significant risk when it comes to managing sensitive data. Copying production data into non-production environments for activities such as application development, testing, and data analysis can expose this data to unauthorized access and increase the chances of a data breach. To combat this risk, enterprises can turn to data masking, a process that modifies private data while protecting the original from unwanted access. Data has become one of the most valuable assets for businesses across industries. With the increasing importance of data privacy and security, organizations are constantly finding ways to protect sensitive information from unauthorized access.

What is Data Masking?

Data masking is a technique used to protect sensitive data by replacing it with fictitious yet realistic data. It involves masking or obfuscating sensitive information such as social security numbers, credit card details, or personally identifiable information (PII), while preserving the format and structure of the original data. The purpose of data masking is to create a version of the data that can be used for development, testing, or analysis, without compromising the confidentiality and integrity of the information.

How Does Data Masking Work?

Data masking works by applying various techniques to alter sensitive data without modifying the underlying business logic or functionality. These techniques may include encryption, substitution, shuffling, or adding noise to the original data. By doing so, organizations can ensure that only authorized personnel have access to the sensitive data, while the masked data remains usable for non-production purposes.

Why is Data Masking Important?

Data masking plays a crucial role in addressing data privacy concerns and complying with regulations such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). It allows organizations to safely share data with external parties, perform software testing using realistic but non-sensitive data, and minimize the risk of a data breach or unauthorized access.

Benefits of Data Masking

Enhanced Data Protection

By replacing sensitive data with realistic yet fictitious information, data masking ensures that only authorized individuals can access and work with the actual sensitive data. This protects the organization from data breaches, insider threats, or accidental exposure of sensitive information.

Compliance with Privacy Regulations

Data masking is an effective method to comply with privacy regulations like GDPR or HIPAA. By masking sensitive information, organizations can share data for purposes such as software development, testing, or analytics, while keeping the original data secure and maintaining privacy compliance.

Data Availability for Non-production Use

Data masking allows organizations to create masked copies of production data for non-production environments like testing or development. This ensures that developers, testers, or analysts can work with realistic data without the risk of exposing sensitive information.

Cost and Time Efficiency

By using masked data instead of actual sensitive data, organizations can reduce the costs associated with securing and managing sensitive information. It also saves time by eliminating the need to manually sanitize or anonymize data for non-production use.

Data Masking Techniques

There are several data masking techniques that organizations can employ based on their specific needs and requirements. Some common techniques include:

Encryption

Encryption is a widely used technique in data masking where sensitive information is encoded using cryptographic algorithms. This ensures that only authorized individuals with the decryption key can access the original data.

Substitution

Substitution involves replacing sensitive data with fictitious but realistic data. For example, a social security number may be replaced with a randomly generated number that follows the same format.

Shuffling

Shuffling is a technique where the order of data elements is randomized while maintaining the overall structure and integrity of the data. This helps to prevent the identification of individuals based on the order of their data.

Noise Addition

Noise addition involves adding random data or noise to the original data. This makes it difficult to decipher the actual sensitive information, even if an unauthorized individual gains access to the masked data.

Data Masking in Oracle

Let’s explores how Oracle Data Masking offers a comprehensive solution for encrypting data and reducing risks in enterprise environments.

Data masking involves the modification of sensitive data in a way that makes it impossible to identify the person or entity referenced in the data. This process ensures that the original data is protected from unauthorized access while maintaining the integrity of the data structure and attributes. By masking sensitive data, enterprises can prevent reverse engineering tactics and maintain the privacy and security of their data. Oracle Data Masking provides end-to-end security for automating the masking process in enterprise environments. It allows enterprises to allocate test databases from production while ensuring compliance with regulations. This solution is accessible through Oracle Enterprise Manager, which offers built-in SQL functions and tools that enable the masking of sensitive data in database tables.

Managing Data Masking with Oracle

Oracle Data Masking utilizes a two-tier framework consisting of a cloud-controlled User Interface (UI) or a Graphical User Interface (GUI) for administrators to manage all data masking-related tasks. These components can be accessed either locally or through a web browser. The core of the framework is the Oracle Enterprise Manager, which comprises the Oracle Management Server and a database repository.
The Oracle Enterprise Manager (OEM) provides administrative user accounts and manages processes such as jobs and events that facilitate the flow of information between the Cloud Control UI and the nodes with Enterprise Management Agent. Enterprise Management Agents are installed on each monitored host and communicate with the Management Server to monitor targets and manage hosts.

Application Data Modeling for Masking

Application Data Modeling (ADM) simplifies the data discovery process by automatically identifying Oracle database tables and mapping them with sensitive data columns. This enables the easy identification of relationships between database columns and provides analytics by identifying patterns in column data, names, and comments. ADM allows businesses to list tables, applications, and database relationships that handle sensitive data and columns, enabling the production of test data with data masking and subsetting.

Centralized Data Masking/Subsetting Library

Oracle Data Masking offers a centralized data masking format library that allows security administrators to centralize definitions for masking formats. This ensures consistent masking rules across all enterprise systems, regardless of the location or type of sensitive data. The library includes multiple built-in data masking formats and primitives that can be used in masking definitions. It also provides SQL and PL/SQL functions for defining complex masking formats.

Different Masking Transformations

Oracle offers various masking transformations to protect sensitive data effectively. These transformations include:

Conditional Data Masking

This transformation enables businesses to mask data based on various conditions. For example, a mask that lists unique identification numbers can be different across countries, allowing for tailored masking in the same column.

Compound Data Masking

This transformation masks and groups together related database columns, ensuring that masked data retains its original relationships. For instance, address fields in a table can be masked while maintaining consistency in the respective address field values.

Deterministic Data Masking

This transformation provides consistent outputs across different databases for the same input data, ensuring data and system integrity in multi-application environments.

Shuffling

This transformation randomly rearranges the fields in a table column, breaking the mapping between data elements. It can be used to mask personal health information, for example.

Key-Based Reversible Data Masking

This transformation allows for the encryption and decryption of raw data using secure key strings, preserving the original format of the input data. It enables secure data sharing with third parties while maintaining data integrity.

Format-Preserving Randomization

This transformation randomizes input data while preserving its original format, including length, special characters, and the position of numbers and characters.

Simplified Data Subsetting Process

Oracle Data Masking also offers a simplified data subsetting process that allows businesses to extract subsets of data based on specific goals and conditions. Whether businesses need to extract a percentage subset of a database table or subset data based on time-based conditions, Oracle provides the tools to accomplish these tasks.By utilizing Oracle Data Masking and its data subsetting capabilities, businesses can obtain real-time, dynamic views of application schemas, enabling efficient validation and further data subsetting tasks.

Ensuring the security and privacy of sensitive data is of utmost importance for enterprises. Oracle Data Masking offers a comprehensive solution for encrypting data and reducing risks in non-production environments. By leveraging the power of data masking and subsetting, enterprises can protect their data from unauthorized access while maintaining data integrity and compliance with regulations. With Oracle's robust framework and various masking transformations, businesses can encrypt their data and reduce the risk of breaches, achieving effective and secure data usage.

Data masking is a vital tool for protecting sensitive information while ensuring its usability for non-production purposes. By employing various techniques such as encryption, substitution, shuffling, or noise addition, organizations can mitigate the risk of data breaches, comply with privacy regulations, and efficiently utilize data for development, testing, and analysis. Data masking is an essential part of modern data management strategies, safeguarding valuable assets and maintaining the trust of customers and stakeholders.