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:

• 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.

• 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:

• 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.

• 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:

• Automated Compliance

By automating the data masking process based on regulatory requirements, organizations can ensure consistent compliance across all data access points.

• 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.

Frequently Asked Questions (FAQs)

1. What is real-time data masking?

Real-time data masking is the process of obfuscating sensitive information dynamically at the moment it is accessed, queried, or changed. It ensures that users without proper authorization see only masked values, while the original data remains protected in live systems.

2. How does real-time data masking differ from static data masking?

Static data masking permanently alters sensitive data in non-production environments like testing or development. Real-time data masking, on the other hand, applies masking rules at runtime, without changing the stored data, making it suitable for production systems where data must remain usable and secure simultaneously.

3. What types of data can be masked in real time?

Real-time data masking can be applied to structured data in relational databases, semi-structured formats such as JSON and XML, and even file-based data in flat files. With the right setup, it can also extend to unstructured sources like documents and images through tools that support event-driven masking processes.

4. How is real-time data masking applied in databases?

In databases, real-time masking is applied by intercepting queries or replicating modified records and applying masking logic before the data is exposed. Access control rules and user roles determine whether the original or masked data is shown, ensuring sensitive fields are protected based on authorization levels.

5. What is the role of Ripcurrent in real-time data masking?

Ripcurrent is IRI’s technology within the Voracity platform that performs incremental data masking. It masks sensitive data as it is inserted or updated in supported databases, such as Oracle, SQL Server, MySQL, and PostgreSQL. The masked data is written into target schemas for safe downstream use.

6. Can real-time data masking be used with file-based systems?

Yes. Real-time protection for file-based systems can be achieved by setting up a file watcher mechanism that triggers masking jobs when new or updated files are detected. Tools like IRI FieldShield can then be executed automatically to apply masking functions as soon as files appear or change.

7. How does user-role-based masking work?

User-role-based masking determines what level of data visibility a user has depending on their access privileges. For example, a support agent might see only masked values, while an administrator with higher privileges can view the unmasked version. This ensures sensitive data is only revealed when necessary and appropriate.

8. What are the benefits of real-time data masking?

Real-time data masking provides immediate protection for sensitive data during access, helping prevent breaches and data misuse. It supports compliance with privacy regulations and enables secure data handling in live systems without altering application logic or degrading performance.

9. What are the challenges of implementing real-time data masking?

Challenges include setting up the appropriate masking rules for different user roles, ensuring accurate query interception, and configuring the system so that masked data does not interfere with application functionality. There is also a risk of under- or over-masking data if the rules are not clearly defined.

10. Can real-time data masking help meet regulatory compliance?

Yes. Real-time data masking supports compliance by ensuring that sensitive information is only accessible to authorized users. It also enables audit logging and policy enforcement, which are critical for demonstrating adherence to laws such as GDPR, HIPAA, and CCPA.

11. How does incremental data masking differ from real-time query masking?

Incremental data masking protects data as it is changed or inserted, usually in replication or ETL processes. Real-time query masking alters data at the point of access without storing the masked result. Both provide dynamic protection, but they are applied at different stages in the data lifecycle.

12. What makes IRI’s real-time data masking approach different?

IRI’s approach to real-time data masking emphasizes consistent, rule-based protection for sensitive values as they are created or modified. Through Ripcurrent and other masking engines within the Voracity platform, organizations can enforce masking across diverse systems without disrupting workflows or requiring extensive customization