The digital world faces new risks every day, and with them comes the potential of data breaches that put sensitive data at risk. Microsoft estimates that there are 600 million cyberattacks per day. On the other hand, the Identity Theft Resource Center (ITRC) Annual Data Breach Report revealed that 3,205 cyberattacks in 2024 led to data breaches that compromised around 4.2 billion records.
These regular data breaches are not just costly crises; they are now a permanent problem that demands a robust data security posture management strategy, along with data masking techniques to protect sensitive data, especially where it is housed and how it is used by different teams.
What is Data Masking?
Data masking is the process of intentionally modifying sensitive data by creating an amended version of data that retains its structural similarity to the original sensitive data. The aim is to conceal the original data and utilize it for multiple purposes without exposing personal identifiers such as Personal information (PII), protected health information (PHI), financial data such as credit card information, healthcare data such as medical records, and copyright data such as intellectual property (IP) to unauthorized users.
Masked data is generally utilized for multiple purposes within the company, such as testing software, educating employees, and conducting business analytics. This keeps data safe and private by making sure that teams can work with realistic datasets without putting sensitive data at risk. This is crucial to demonstrate compliance with privacy legislation such as the GDPR, CCPA/CPRA, LGPD, etc.
Types of Data Masking
Data masking encompasses various techniques. Here are the most common approaches utilized by small to medium enterprises and large businesses:
A. Static Data Masking (SDM)
This is by far the most common form of data masking technique, where a duplicate dataset of sensitive data is created with either fully or partially masked data. A backup copy of the duplicate dataset is maintained in a different environment where any unneeded data is removed, and data is then masked to keep its original form secure. This data can then be utilized across the organization for testing purposes, development, training, etc.
B. Deterministic Data Masking
This data masking technique protects sensitive data by replacing the original data with pseudonymized data while ensuring the underlying data remains secure and confidential. This approach enables the use of data for multiple purposes without exposing critical information.
C. On-the-Fly Data Masking
On-the-fly data masking translates data into fictitious values in real-time as it flows across data environments. This approach ensures that the actual sensitive data, such as consumer records and other personal identifiers, are secured behind bars while the altered data is leveraged for testing, business core analytical decision making, or shared with third parties.
This approach is particularly crucial for businesses that regularly engage in data transfers across systems, data environments, or across geographies.
D. Dynamic Data Masking (DDM)
Also known as a real-time data-altering security strategy, dynamic data masking controls how sensitive data is viewed by individuals based on their access level. It enables data owners to assign role-based access controls where some might gain full visibility/access to the entire data set, while others might get partial access to masked data.
Consider it as a permission-based access model that’s engineered to protect sensitive data across various hierarchy levels without altering the data or employing complex processes.
Benefits of Data Masking
Data masking offers organizations a customized camouflage approach to protect sensitive data from accidental exposure and falling victim to a data breach. Here are some common benefits of data masking:
A. Securing Sensitive Data
Data masking safeguards the original dataset, which might include PII, SSN, credit card details, and healthcare-related data, by replacing it with a replacement dataset. This helps organizations to protect business-critical data as well as ensure consumers’ ultimate data protection from illegal access and disclosure in less-secure data settings.
B. Complying with Regulations
Data privacy regulations are continually developing and impose strict data security requirements. Data masking helps organizations position their data protection policies with the growing requirements of data privacy regulations, including the GDPR, CCPA/CPRA, HIPAA, PCI-DSS, and others, while decreasing the risk of non-compliance penalties, regulatory scrutiny, and reputational harm.
C. Reducing Risk of Data Breaches
Masked data is of no value to the recipient. All the malicious actors would obtain is gibberish information that’s scrambled and unrelated. This approach conceals real identifiers from ever getting exposed, significantly minimizing the threat surface and keeping organizations at ease from having their real datasets exposed.
D. Supports Safe Testing & Data Sharing
Teams within the organization can test applications and systems by working with masked datasets (not containing sensitive data) that resemble the original dataset, preventing sensitive data exposure. Additionally, the masked data can be shared across teams, such as application development to marketing and then to external vendors, without compromising business-critical information or revealing consumers’ actual data.
Data Masking Challenges
Although data masking offers several benefits, it does come with a set of challenges:
A. Multi-Cloud Complexity
Organizations have data residing across various data points, including on-premises data systems, multiple databases, third-party services (AWS, Google Cloud, Microsoft Azure), and hybrid-cloud environments. Cloud providers come with native capabilities that differ from one another.
Ensuring standard masking policies across diverse data environments can be difficult, resulting in misalignment among data platforms, performance overload, and inconsistent masking that may disclose sensitive data.
B. Overmasking and Undermasking
Another preeminent challenge is the overmasking of sensitive data. In an effort to protect sensitive data, organizations tend to overmask datasets, resulting in erratic datasets that are far too random to be used for any purpose. At the same time, undermasking sensitive data, thinking it has been masked enough, can result in sensitive data exposure. The key is to strike the right balance between overmasking and undermasking to maximize data utility.
C. High Initial Setup and Maintenance
Most small to medium-sized enterprises struggle with dedicating planning efforts and resources that require detail-oriented workflows that mask sensitive data without risking any gaps in the process that could jeopardize data to malicious actors or unauthorized individuals. The high initial setup cost is a big challenge for organizations with limited funds and personnel resources. Additionally, data masking requires constant upgradation and maintenance to accommodate evolving security protocols and industry best practices.
Data Masking Techniques Used to Secure Sensitive Data
Multiple data masking techniques work alone and in conjunction with one another to secure sensitive data. Most common approaches include:
A. Nulling Out
Sensitive fields are removed or replaced with null or blank values. Although it’s a straightforward technique, it does result in sensitive data losing its value.
B. Value Variance
Sensitive data is replaced by random variations or values. This is mostly done on numerical values.
C. Data Encryption
Sensitive data is encrypted to protect it from unauthorized access. A decryption key is the only way authorized users can gain access to the encrypted data. This is by far the most common and widely used data masking technique.
D. Data Scrambling
Sensitive data is scrambled and rearranged completely.
E. Data Substitution
Sensitive data is replaced with realistic but fake values.
F. Data Shuffling
Sensitive data is shuffled within a column so it remains realistic but unlinkable to the original subject.
G. Pseudonymisation
Sensitive data is replaced with pseudonyms while still allowing re-identification under controlled environments.
Best Practices for Implementing Data Masking
Data masking isn’t a definitive all-in-one masking solution. It requires a robust data security architecture that works in collaboration with security protocols, industry-wide best practices, and knowledge from data privacy regulations. Here are some common best practices to implement data masking within your organization.
A. Identifying Sensitive Data
The fundamental technique is to identify all data at hand. This involves identifying sensitive data sitting on-premises, in the cloud, in a hybrid cloud, in silos, in shadow IT systems, etc. The main technique is to undertake a complete data discovery and classification activity to determine where sensitive data exists.
B. Defining Masking Rules
Once data is discovered, businesses may examine data residency across regions and implement data masking strategies that fit with legal requirements and business-critical tasks.
C. Monitoring and Auditing
No system is foolproof without ongoing monitoring and regular audits. As a best practice, enterprises must regularly monitor masked data environments and audit compliance to ensure system efficacy.
Organizations must understand that data masking isn’t just a background security approach but rather a core component of ensuring utmost data security while sensitive data is in transit or at rest.
Automate Data Masking with Securiti DSPM
As regulatory pressure increases and data environments grow more complex, organizations can no longer rely on manual methods to tackle the growing number of data being generated, stored, and shared to ensure compliance. DSPM offers a proactive, automated, and scalable solution to beef up the overall data security posture against evolving threats.
Securiti's Data Command Center (rated #1 DSPM by GigaOM) provides a built-in DSPM solution, enabling organizations to secure sensitive data across multiple public clouds, private clouds, data lakes and warehouses, and SaaS applications, protecting both data at rest and in motion.
Schedule a demo to learn how Securiti addresses your organization’s unique data security, privacy, and governance needs with a unified Data + AI Command Center.
