Data Masking

Types of data masking

Data masking is not a one-size-fits-all approach. Depending on the use case and the level of security required, organizations can implement different types of masking techniques to better protect their attack surface and prevent data loss. Let’s discuss some of the most common types of data masking used in cybersecurity and privacy-focused environments.

Static data masking

Static data masking involves creating a masked copy of a database to be used in non-production environments, such as testing or development. This technique replaces sensitive data in a duplicate dataset with nebulous values, ensuring the original data remains secure. Static masking is ideal for scenarios where data needs to be shared externally or with teams who do not require access to real data and/or to whom the principle of least privilege access (LPA) is applied.

Dynamic data masking 

Dynamic data masking applies obfuscation – or obscuring – of data in real time when users query a database. Unlike static masking, this technique does not alter the original dataset but rather intercepts and masks data during access, based on user permissions. Dynamic masking is commonly used in environments where data must be protected from unauthorized users while allowing authorized users to view the original values.

Deterministic data masking

Deterministic data masking replaces sensitive values with consistent, repeatable masked values. For example, a customer’s name could always be replaced with the same pseudonym or alias. This approach is useful when masked data needs to maintain relationships across datasets, such as ensuring that customer data in one system corresponds to the same customer in another. It’s especially helpful in scenarios involving analytics or cross-system data integrity.

On-the-fly data masking

On-the-fly data masking applies obfuscation to data as it is transferred or moved between systems. This type of masking does not store the masked data but ensures sensitive information is protected during migration or replication processes. It’s often used during real-time data transfer for secure application testing or cloud migrations, providing immediate protection without requiring a separate masked dataset.

Tokenization 

Tokenization replaces sensitive data, such as credit card numbers or social security numbers, with randomly generated tokens. These tokens retain the same format as the original data but are meaningless without access to the tokenization system. This method is widely used in payment processing and financial industries to protect critical information.

Data encryption 

While technically distinct from data masking, data encryption is often used in tandem with it. Encryption secures sensitive data by converting it into an unreadable format using cryptographic algorithms. Masking may be applied to create an additional layer of protection, especially in environments where decrypted data is required for specific operations.

Shuffling 

Shuffling involves rearranging data within a column to randomize its order while retaining the overall structure of the dataset. For instance, employee salaries might be scrambled across records to obscure the original values while maintaining realistic-looking data for analysis or testing purposes.

Nulling or blanking out 

In some cases, sensitive data is entirely replaced with null or blank values. This approach is typically used for fields that are not critical for operational use, minimizing exposure risks by removing sensitive details altogether.

Common data masking techniques 

Organizations use a variety of techniques to mask sensitive data, depending on their specific use case and compliance needs. These techniques aim to balance data security with usability, ensuring masked data remains functional for testing, analysis, and other operations. While there is some crossover with the previous section, it’s critical to be aware of the following techniques as well.

• Substitution: Replacing original data with fictitious but realistic values, such as swapping real names with fake ones or altering social security numbers to appear valid.
• Data redaction: Removing or obscuring parts of the data, such as masking all but the last four digits of a credit card number.
• Character masking: Hiding certain characters within a dataset, such as converting a phone number like “(555) 123-4567” to “(XXX) XXX-4567.”
• Format-preserving masking: Altering the data while keeping its overall format intact, such as ensuring credit card numbers remain 16 digits with valid prefixes.
• Date aging: Modifying date fields by adding or subtracting a random number of days, which can protect sensitive timelines while maintaining realism.
• Randomization: Replacing values with entirely random ones that follow the same structure, ensuring no discernible relationship to the original data.

Common challenges in data masking

While data masking is an effective way to protect sensitive information, successfully implementing it comes with its own set of challenges. Organizations must address the following obstacles to ensure data remains secure without compromising its utility. Below are some of the most common challenges a security operations center (SOC) might face in data masking.

Ensuring data accuracy and consistency

One of the biggest challenges in data masking is maintaining the accuracy and consistency of the masked data across systems. For example, in environments where multiple datasets are interconnected, inconsistently masked data can break dependencies and render the data unusable. Ensuring relational integrity while masking sensitive information requires careful planning and execution.

Balancing security with usability 

Masked data must strike a delicate balance between security and usability. Over-masking data can make it less valuable for tasks like software testing or data analysis, while under-masking can increase the risk of data leakage by exposing sensitive information to potential theft. Organizations need to identify the appropriate level of masking to meet their specific use cases without compromising functionality.

Scaling data masking for large volumes

In today's era of big data, organizations often deal with massive datasets that span multiple systems and environments. Applying data masking at scale can be resource-intensive and challenging, particularly in real-time or dynamic environments. Security orchestration, automation, and response (SOAR) tools are essential to effectively handle large-scale masking.

Complying with diverse regulations

Different industries and regions have unique data privacy regulations, such as GDPR, CCPA, and HIPAA. Ensuring that data masking efforts meet these diverse and often overlapping compliance requirements can be complex. Organizations must stay updated on regulatory changes and adapt their masking strategies accordingly.

Addressing performance impacts

Data masking can introduce latency and processing overhead, especially in dynamic or real-time masking scenarios. This is particularly problematic in environments that require high-speed access to data. Implementing data masking techniques that minimize performance impacts is crucial for maintaining operational efficiency.

Why is data masking important? 

Data masking plays a critical role in cybersecurity and data privacy by acting as a critical network access control protocol. It also ensures sensitive information remains secure while still being usable for non-production purposes.

As organizations increasingly handle large volumes of sensitive data, implementing robust masking techniques has become essential for mitigating risks and maintaining compliance. Let’s take a look at some key benefits of data masking for organizations.

• Protects sensitive information: Prevents unauthorized access to PII, financial data, and other sensitive information, reducing the risk of data breaches.
• Supports regulatory compliance: Helps organizations comply with data privacy regulations – like the previously mentioned GDPR and HIPAA – by safeguarding sensitive data during processing and storage.
• Minimizes insider threats: Ensures employees, contractors, or partners accessing masked data cannot misuse the underlying sensitive information, lowering the risk of insider-related incidents.
• Enables secure testing and development: Provides realistic but anonymized data for testing, development, and training environments without exposing sensitive customer or business information.
• Maintains data utility: Preserves the usability and functionality of datasets for tasks like analysis and reporting, enabling organizations to gain insights while keeping data secure.
• Simplifies data sharing: Facilitates safe sharing of datasets with supply chain partners, such as vendors or service providers, without revealing sensitive details and effectively managing third party risk.
• Reduces risk in cloud environments: Protects sensitive data during cloud migrations or while operating in cloud-based environments, mitigating the risks of exposure during transit or storage.
• Improves customer trust: Demonstrates a proactive approach to data privacy and security, fostering greater trust among customers and stakeholders.
• Prevents non-compliance fines: By meeting legal data protection standards, organizations can avoid the hefty penalties associated with non-compliance.
• Mitigates the impact of cybersecurity incidents: Ensures that even if a breach occurs, sensitive information remains obfuscated and unusable to attackers. Organizations can also carry out breach and attack simulations (BAS) to gain a deeper understanding of specific breach scenarios.