Data Masking

Data Security
By substituting real data with masked data, it ensures that even if unauthorized access occurs, the data is of no practical use to the attacker. This method is particularly effective in non-production environments such as development, testing, and analytics, where the use of live data is unnecessary and risky.

Data Privacy Compliance
Most privacy regulations require organizations to implement appropriate technical measures to ensure security of PII data. Data masking fulfills this requirement by anonymizing personal data, thus protecting individuals’ privacy.

Data Sovereignty
By masking sensitive data before it leaves its country of origin, organizations can ensure compliance with laws that mandate data to remain within specific geographic boundaries. This reduces the risk of violating data sovereignty regulations while still enabling global data processing and analysis.

Learn about Data Privacy vs Data Security

The below table provides a brief overview of various common data masking techniques:

Below is an image showing how Cyral Database Security presents the same data with different levels of masking based upon user roles and authorization levels. This can provide users with data which can be manipulated and tested upon, without revealing actual sensitive data and PII.

There are a number of data masking strategies that teams may employ. Below are three of the most common Data Masking strategies:

• Static Data Masking: Involves masking data at rest, typically in a non-production environment. The data is masked and then stored, ensuring that sensitive information is never exposed.
• On-the-Fly Data Masking: Similar to dynamic masking but specifically for data in transit. It masks data as it is moved from one environment to another, ensuring that sensitive information is protected during transfer.
• Dynamic Data Masking: Masks data in real-time as it is accessed by applications or users, without altering the underlying database. This allows for secure access to data without modifying the original dataset.

Implementing data masking effectively comes with several complexities that must be addressed to ensure that masking achieves its intended purpose of protecting sensitive information.

• Data Masking at Rest is Expensive:
  Implementing data masking at rest often requires maintaining multiple copies of the database to ensure that both masked and unmasked data are available as needed. This duplication can be costly in terms of storage and resource management. Additionally, keeping these copies synchronized and up-to-date adds operational overhead, increasing the complexity and expense of the masking process. The costs associated with storage, processing power, and administrative efforts can be substantial, making it a significant investment for organizations.
• Complexity of using Database Roles:
  Creating and managing masking policies typically involve using database roles, which requires significant time and expertise. Database administrators must have a deep understanding of the data structure and the specific masking needs of different datasets. Configuring these roles accurately is a complex task that demands careful planning and ongoing maintenance. As database environments grow and evolve, keeping masking policies aligned with changing requirements becomes increasingly challenging, necessitating continuous oversight and adjustments.
• Lack of Granularity in Policies:
  Data masking policies are often not granular enough, as they typically operate at the column level rather than the row level. This limitation means that sensitive information within specific rows cannot be selectively masked based on context or user access levels. As a result, data masking may either be too restrictive, hindering legitimate data use, or too lenient, failing to adequately protect sensitive information. Achieving the right balance requires more advanced and flexible masking techniques that go beyond simple column-level policies.
• Unclear Ownership of Policies:
  The implementation of data masking policies often involves multiple teams, including database administrators, security teams, and data owners. This multi-team involvement can lead to unclear ownership and responsibility for maintaining and updating masking policies. Without clear delineation of roles and responsibilities, inconsistencies and gaps in data masking practices can occur. Effective data masking requires well-defined governance structures and collaboration among all relevant stakeholders to ensure comprehensive and consistent policy enforcement.
• Service Accounts Render Policies Ineffective:
  Many applications, BI tools, and ETL jobs use service accounts to access databases, and these service accounts are often shared among multiple users. This shared access can render data masking policies ineffective because it is difficult to enforce user-specific masking rules when multiple users are mapped onto the same service account. As a result, sensitive data may be exposed to users who should not have access to it. Addressing this challenge requires implementing more sophisticated access controls and ensuring that service accounts are used in a way that supports effective data masking.

By addressing these complexities, organizations can implement robust data masking strategies that effectively protect sensitive information while supporting business operations.

Implementing an effective data masking strategy requires a practical framework that addresses the complexities of different data environments. Here is a structured approach to achieve robust data masking.

Adaptive Strategy:
Adopting an adaptive masking strategy ensures that the appropriate masking techniques are applied based on the type of data asset.  For production databases and any replicas, dynamic data masking can be used to mask data in real-time, providing on-the-fly obfuscation without altering the underlying data. For ETL jobs and other data processing tasks, on-the-fly masking can be employed to ensure that data is masked as it is being transferred or processed. This adaptive approach ensures that data is protected in various scenarios while maintaining operational efficiency.

Centralized Policies:
Implementing centralized masking policies across the entire data stack enhances consistency and control. Using an external authorization service to manage these policies decouples policy management from database administration, simplifying oversight and reducing the risk of inconsistencies. Centralized policy management allows for uniform enforcement of masking rules across different databases and applications, ensuring that sensitive data is consistently protected regardless of its location or usage context.

Identity Federation:
Specifying data masking policies in terms of federated identities allows for more precise and context-aware enforcement. Policies can be invoked based on a user’s IAM entitlements, ensuring that masking rules are applied dynamically based on the user’s role and permissions. This approach also involves ensuring that queries from service accounts are annotated with the actual user identity, maintaining the effectiveness of masking policies even in environments where service accounts are used. Identity federation helps in maintaining a fine-grained control over who can see what data.

Automated Coverage:
Ensuring that data masking policies follow the data requires automated and comprehensive coverage. Policies should be specified based on the type of data (e.g., PII) rather than specific fields, allowing them to adapt to changes in data schemas and new data sources. Implementing discovery and classification tools helps in keeping the data labeling updated, ensuring that all sensitive data is identified and appropriately masked. Automated tools can scan databases to detect sensitive information and apply masking policies automatically, reducing the administrative burden and enhancing security.

By following this practical framework, organizations can implement effective data masking strategies that protect sensitive information across various environments and use cases. This approach not only enhances security but also ensures compliance with data privacy regulations and supports efficient data management practices.

Cyral’s platform enables a policy-based approach to data masking. These policies are specified using central IAM identities and entitlements, and can be enforced even when users are hidden behind service accounts.

Data Masking plays a pivotal role in protecting sensitive information within databases, offering a balance between data security and usability. By obscuring sensitive data elements while maintaining data integrity and regulatory compliance, organizations can safeguard against data breaches, mitigate risks, and uphold trust with stakeholders. As data volumes grow and regulatory requirements evolve, the adoption of robust data masking strategies will remain essential for maintaining the confidentiality and integrity of sensitive information in an interconnected digital landscape.

Through careful implementation, continuous monitoring, and adaptation to emerging technologies, organizations can realize the full potential of data masking to secure their most valuable asset: data.

Cyral Database Security enables you to apply strategies and techniques, including Data Masking, to ensure that your database’s sensitive data remains protected.