Unmasking Entity-Based Data Masking: Best Practices 2025

Masking data to protect confidentiality has become a mandate more than ever before. As enterprises attempt to mitigate their workloads to multi-tenant cloud environments and data lakes etc., it makes perfect sense to revisit the masking strategies and be prepared for the future. That, too, when compliance regulations have tightened their grip over potential defaulters. 

That's exactly why entity-based masking is gaining acceptance across industry facets. It moves sensitive data across the landscape without exposing it. This enables organizations to comply with privacy regulations such as the GDPR, CCPA, and HIPPA. 

In fact, it's an industry of its own, pacing to a value of USD 1.87 billion by 2029 and recording an approximate CAGR of 14%. 

Apart from securing sensitive data assets throughout the lifecycle management process, entity-based masking strengthens the overall risk management initiatives.

What’s even more interesting is entity-based masking’s role in facilitating the development of AI and ML models using datasets.

Which Are the Top Entity-Based Masking Platforms?

The data masking market is significantly fragmented as many global players have entered the scene. Key names include, but are not limited to, IBM, Mentis, Delphix, Oracle and Informatica. Informatica's data masking follows an entity-centric approach to protecting sensitive data. It effectively discovers, classifies, and masks data across structured and unstructured sources while maintaining referential integrity.

That being said, entity-level Micro Database approach continues to be in the spotlight. The platform’s entity-based data masking is a proven, thorough method for safeguarding confidential data. It arranges scattered data from various sources into protected Micro Databases that are individually encrypted for each business entity, such as customers' orders or devices. 

This entity-focused approach streamlines data protection and ensures the consistency of references, allowing authorized users to access all information linked to a specific entity while hiding sensitive details. A solution called Mentis supports static and dynamic masking for structured and unstructured data, offering numerous built-in masking functions and the flexibility to establish personalized anonymization guidelines. By upholding data usability and maintaining connections between masked entities, it empowers organizations to adhere to privacy regulations while effectively managing analytical and operational tasks. 

While we are at it, IBM's Optim supports entity-based data masking, enabling organizations to mask data in the context of business entities. IBM’s Optim is a major BFSI sector player, offering dynamic and static masking options. 

Best Practices in Implementing Entity-Based Data Masking

Regardless of the target system’s industry, data scale and other parameters, the following are the standard best practices that every platform should diligently follow. 

Identify and Classify Sensitive Data

Data protection initiatives, including entity-based masking, have to start with discovery—the discovery of all data assets across repositories. By utilizing AI-enabled advanced scanning tools, data professionals can streamline the identification and categorization of PII data sets. This helps further facilitate seamless collaboration among data owners, security teams, and compliance professionals.

Such a collaborative approach works because it comprehensively explains the organization’s data landscape. Following this, data professionals can curate effective data masking strategies at the entity level. Ultimately, it helps prioritize sensitive data, thereby optimizing data protection initiatives and implementing security protocols for critical information. 

Define Clear Masking Policies

While there are multiple masking techniques, it is important to do the right mapping with the requirement. This should be done based on the data set’s sensitivity level, compliance requirements, data volume and other parameters. For example, data tokenization is the go-to policy if you want to hide sensitive data with non-sensitive tokens. 

Likewise, if you want to ensure that only authorized personnel and decryption keys should access the data, Encryption is the method to implement. For less volume and less sensitive but important data, a simplistic technique like substituting real data sets with fictitious data works fine.

The point is that data has to be prioritized based on its high-risk exposure. Data assets that are highly vulnerable to misuse will require the most advanced and best resource allocation for adequate protection. 

Define clear masking policies for optimal results. Organizations can avoid data corruption and maintain dataset integrity by considering data relationships while implementing masking. 

Maintain Referential Integrity 

Entity-based masking must ensure consistency and validity across multiple datasets. To achieve this, data professionals should preserve relationships between masked entities, which is essential for maintaining data quality while protecting data confidentiality. How does that help? Organizations are better positioned to prevent data corruption and context manipulation, ensuring end-to-end accuracy and reliability.

Preserving relationships is also important because multiple, interconnected entities often complicate the data structures. For example, a customer’s personal information might include their order history, past transactions, payment information, shipping address etc. Now, while masking, organizations must establish a relationship between these data types to maintain usability. 

In pursuing the same, organizations should implement consistent masking techniques across all related entities, or use platforms that automatically maintain the referential integrity during masking. 

Monitor and Audit Masked Data

We all know the importance of consistently monitoring and auditing masked data to prevent unauthorized access. It is imperative for organizations to proactively analyze access patterns and identify any suspicious activities that may trigger a compliance violation or a security breach.

Implementing robust logging and tracking mechanisms allows organizations to record and track data access activities, providing a detailed trail of who accessed the data and when. 

This ongoing monitoring and evaluation process allows organizations to fine-tune their data masking strategies, address any vulnerabilities, and enhance overall data security measures.

More Data on the Go

As data volumes grow, real-time masking capabilities and convergence with cutting-edge technologies like homomorphic encryption will be crucial. Entity-based masking will be pivotal in regulatory compliance and mitigating data breach risks. By embracing this approach, organizations can unlock their data's potential while maintaining customer trust and meeting stringent privacy standards in the data-driven future.