Real-Time (On-The-Fly) Data Masking Explained
Data masking, or data obfuscation, is the practice of hiding original data with modified content, such as characters or numbers, to protect sensitive information while maintaining its usability for certain operations like testing or training.
The core objective of data masking is to generate an alternate version of data that is indiscernible and cannot be reverse-engineered (either easily or at all), thus safeguarding data classified as sensitive.
The importance of data masking lies in its ability to render data useless to attackers, reduce risks associated with application testing and data sharing, and help organizations remain compliant with data privacy laws.
What Is Real-Time (On-The-Fly) Data Masking?
Real-time data masking takes the foundational concept of data masking a step further by applying these principles as data is queried, accessed, or changed, with or without storing the altered data permanently.
This dynamic approach is crucial for operations requiring up-to-the-minute data access, such as customer support or live analytics, ensuring that sensitive data is instantaneously obfuscated upon access.
Instantaneous Protection
Real-time data masking provides immediate protection for sensitive data as it moves. This instant obfuscation ensures that unauthorized users cannot view or use sensitive information, which is especially critical in environments with real-time data usage.
Preserving Data Usability and Integrity
Even as data is masked on the fly, its structure and usability for authorized purposes remain intact. This means that while the data is protected from potential security threats, its functional value for processes like analysis, development, and decision-making is preserved.
How Is Real-Time Data Masking Applied?
Real-time data masking enables organizations to protect sensitive information dynamically as it's accessed or processed. This technique modifies data on-the-fly, ensuring that only authorized users can view unmasked, sensitive data. Let's explore how real-time data masking is applied.
In Database Queries and Applications
Real-time data masking is primarily applied within database systems and applications, where it dynamically alters sensitive data based on predefined rules and user privileges. This ensures that sensitive information, such as personal identification numbers, financial details, and health records, is only accessible to users with the necessary authorization. The process, also known as dynamic data masking, involves:
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User-Role Based Masking
The system automatically applies masking based on the user's role or access level. For example, a customer service representative may only see the last four digits of a customer's social security number, while a manager can view the entire number.
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Seamless Application Integration
Proxy-based dynamic data masking can work with existing applications without the need for significant modifications. Query interception, interpretation, and masking allows organizations to protect sensitive data without disrupting their operational workflows or application performance.
Through Incremental Data Masking Tools
Incremental data masking tools offer another layer of security by applying real-time masking to data as it changes, ensuring that only the most recent, masked data is visible to unauthorized users. This approach is particularly useful in environments with frequent data updates or insertions. It includes:
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Dynamic Response to Data Changes
Whenever data is inserted, updated, or deleted, change data capture tools that can also mask the data ensure that the changes are immediately reflected in the replicated version of the data. This can provide immediate protection for rows moving into a test schema, for example.
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Support for Various Data Types
Whether dealing with structured data in databases, semi-structured data in NoSQL databases or EDI files, or unstructured data in documents and images, incremental data masking tools can apply appropriate masking techniques to a wide range of data types, maintaining the integrity and usability of the data.
In Compliance with Privacy Regulations
Real-time data masking is also a key component in compliance strategies for various data protection regulations such as GDPR, HIPAA, and CCPA. By ensuring that sensitive data is only accessible to authorized personnel, organizations can significantly reduce the risk of data breaches and non-compliance penalties. This application includes:
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Automated Compliance
By automating the data masking process based on regulatory requirements, organizations can ensure consistent compliance across all data access points.
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Audit and Reporting Capabilities
Real-time data masking tools often include features for auditing and reporting on data access and masking activities, which is crucial for demonstrating compliance with data protection regulations.
What Are the Advantages of Real-Time Data Masking?
Real-time data masking offers a host of benefits, making it an indispensable tool for modern data security and privacy strategies. Its advantages extend beyond mere compliance, providing robust protection for sensitive information while maintaining the functionality and accessibility of data for authorized use.
Enhanced Data Security
By masking sensitive data in real-time, organizations can prevent unauthorized access, reducing the risk of data breaches and the exposure of personal or confidential information.
Regulatory Compliance
Real-time data masking helps organizations comply with strict data privacy regulations by ensuring that only authorized users can access sensitive data, thereby avoiding hefty fines and reputational damage.
Minimal Impact on Performance
Unlike traditional data protection methods that may slow down system performance, real-time data masking is designed to operate efficiently on specific data sets only, ensuring data is protected without significant impact on application or database performance.
Flexibility and Scalability
Real-time data masking solutions are adaptable to various data types and environments, from on-premises databases to cloud-based storage, providing a scalable solution that grows with the organization’s data protection needs.
What Are the Disadvantages of Real-Time Data Masking?
While real-time data masking is a powerful tool for protecting sensitive information, it is not without its challenges. Understanding these potential drawbacks is essential for organizations to effectively implement and manage real-time data masking solutions.
Complexity in Implementation
Setting up real-time data masking can be complex, requiring a deep understanding of the organization's data architecture and the specific requirements for protecting different types of sensitive information.
Potential for Configuration Errors
Incorrectly configured masking rules can lead to either excessive masking, which may hinder business operations by obscuring too much information, or insufficient masking, which leaves sensitive data exposed to unauthorized access.
By carefully considering these aspects and choosing the right real-time data masking solutions, organizations can effectively balance the need for data security with the requirements for operational efficiency and regulatory compliance.
IRI Real-Time Data Masking Solutions
IRI provides solutions aimed at securing data in real time, focusing on technologies like Ripcurrent for incremental data masking. These tools ensure data privacy by masking sensitive information as it's processed or updated. Here’s a closer look at what IRI offers in this space:
Ripcurrent in IRI Voracity
Within the IRI Voracity platform, IRI Ripcurrent technology applies consistent, rule-based static data masking functions to sensitive data in rows on the move. When PII values are inserted or updated across a variety of databases – including MS SQL, MySQL, Oracle, and PostgreSQL – Ripcurrent will replicate and mask the data into target schema.
Enhanced Oracle Database Security
For Oracle databases, Ripcurrent not only provides incremental data masking but also introduces a real-time trigger option. This feature allows for encrypting or decrypting data during queries, exemplifying in-situ data masking for real-time data protection.
Structured File Support
Addressing the real-time data protection requirements for structured (flat) file sources, you can set up a file watcher program through Powershell, for example, to trigger a FieldShield operation when there are new or modified files detected in the operating system.
Comprehensive Data Masking Tools
Beyond Ripcurrent are standalone data masking tools like FieldShield for relational databases and flat files, DarkShield for databases, files, documents, and images, and CellShield for Excel (all of which are also included in the IRI Voracity data management platform).
FieldShield is particularly powerful because it can leverage the structured data manipulation language of the CoSort SortCL program to accommodate advanced business logic in data masking jobs, and combine PII masking with ETL, data cleansing, migration, and report generation.
For more information, see:
https://www.iri.com/solutions/data-masking/real-time-data-masking
Conclusion
In conclusion, while real-time data masking is not without its challenges—such as complexity in implementation and potential for configuration errors—its benefits can outweigh these concerns. Enhanced data security, regulatory compliance, minimal performance impact, and scalability make real-time data masking an indispensable tool for certain use cases.
By carefully selecting and implementing the right solutions, like those offered by IRI, businesses can safeguard sensitive information while maintaining operational efficiency and compliance with data protection laws.
Real-time data masking not only fortifies data security but also empowers organizations to navigate the complexities of data privacy regulations confidently. Its strategic application ensures the safe handling of sensitive data on an event-driven basis and thus gives data security governance professionals another way to protect data at risk.
