In this post, I will show top 10 data masking techniques enterprises use to prevent large-scale data breaches.
Data breaches present a major threat to enterprises, exposing sensitive personal and business information. Protecting data is essential for maintaining customer trust, meeting regulatory requirements, and avoiding financial penalties.
Data masking helps organizations safely use data in development, testing, analytics, and other environments without exposing real sensitive information. It’s a core part of many security programs because it enables operational use of data, lowers the risk of unauthorized exposure, and reduces the potential impact of breaches.
Below are 10 of the top approaches enterprises use to conceal original data values. A note on terminology: some items are “core techniques” (how values are transformed), while others are “masking modes” (when and where masking is applied, like static vs. dynamic). In practice, enterprises combine both to balance usability, performance, and risk.
Table of Contents
1. Data substitution
Data substitution is a widely used technique that replaces real data with fictitious, but plausible, values. For example, names, addresses, and other personally identifiable information in a customer database might be replaced with fictional equivalents that look realistic.
The key requirement is that these values can’t be linked back to real people, while still preserving formatting and basic characteristics so systems and applications behave normally.
2. Shuffling within datasets
Shuffling rearranges the position of data elements within a particular column or dataset. Instead of generating new values, this method takes existing data and randomly permutes it across records. The original association between specific data points and individuals is broken, but the values remain valid and familiar for internal use cases.
3. Masking out sensitive sections
Masking out hides parts of sensitive fields using placeholder characters or symbols. In many cases, only certain segments of the data are visible (for example, the last four digits of a credit card number) while the rest is hidden. This approach can offer a balance between usability and privacy by showing just enough information to serve the business need.
4. Tokenization
Tokenization replaces sensitive data with unique identifiers (tokens) that reference the real data stored securely elsewhere. Unlike masking, tokenization is designed to be reversible for approved workflows, such as payment processing or customer support, where authorized services may need to map tokens back to original values.
This reduces risk because tokens hold no intrinsic meaning outside authorized systems, but it also means governance and vault security matter – because reversibility changes the threat model.
5. Dynamic data masking
Dynamic data masking (DDM) protects sensitive values in real time, obscuring data as applications retrieve it rather than altering what’s stored. This allows enterprises to keep production data intact in secure systems while exposing only masked results to users or services without appropriate privileges.
Role-based rules typically govern who sees what level of detail. It’s worth noting that DDM is best understood as a “delivery mode” – it controls what people see – rather than a replacement for transforming data in non-production environments.
6. Static data masking
Static data masking involves creating a masked version of a dataset that’s stored separately from the production copy. Enterprises use this approach when preparing data for development, quality assurance, and analytic workloads, where teams need broad access without exposing real identities.
The biggest success factor for static masking is consistency: masked datasets must maintain structure, relationships, and meaning across systems so applications behave as expected and analytics remain trustworthy.
7. On-the-fly masking
On-the-fly masking operates as data moves between systems or environments. This approach masks data while it’s being transferred from production to testing, analytics, or cloud environments so that sensitive information isn’t exposed in staging areas or interim pipeline steps.
It’s especially useful for CI/CD pipelines, data replication, and data lake integrations, where “mask after landing” can still leave a window of exposure.
8. Statistical masking
Statistical masking preserves the aggregate statistical properties of a dataset while concealing individual values. A company analyzing employee salaries, for example, might preserve the average, median, and range for reporting purposes while replacing each person’s exact salary with a value that maintains overall distributions.
This is valuable for research and business intelligence where analytic integrity matters. The tradeoff is that statistical approaches require care to avoid re-identification risks when combined with other attributes.
9. Nulling out sensitive fields
Nulling out refers to replacing sensitive data fields entirely with null values, ensuring that no real attribute values remain in a dataset copy. This is highly secure and appropriate when the real attribute is not needed downstream.
For example, marketing teams may only need customer IDs and purchase histories for segmentation, without requiring addresses or personal identifiers. Enterprises sometimes combine nulling with other approaches to tailor protections while keeping data usable.
10. Format-preserving encryption
Format-preserving encryption (FPE) encrypts sensitive values in a way that keeps the original data format. For example, a credit card number can be transformed into another number that still looks like a credit card number and passes basic format checks.
FPE sits closer to encryption than classic masking. It can be a strong option when you need cryptographic protection while still satisfying strict format requirements, but key management, access controls, and downstream usability should be considered carefully.
Contextualizing Masking Strategies
Across these approaches, businesses must tailor their data masking strategy to specific needs and regulatory demands. Development teams often require realistic but protected data to validate application behavior. Analytics teams may need statistical integrity without personal identifiers. Security teams need consistency, auditability, and minimal exposure during movement and provisioning.
Many organizations use a layered approach – combining, for example, static masking for non-production environments with dynamic masking for controlled production access, and using on-the-fly masking to reduce exposure in pipelines. The most mature programs also address common enterprise gaps such as maintaining relationships across systems, avoiding inconsistent “one-table-at-a-time” masking, and extending protections beyond structured databases into documents and files.
Aligning these approaches with compliance frameworks such as GDPR or HIPAA helps ensure that data handling practices meet legal requirements while minimizing exposure.
The Cost Of Data Breaches
2025 was another year that showed the huge financial impact of a data breach. For many organizations, the consequences include regulatory fines, compensation payouts, prolonged downtime, reputational damage, and increased insurance premiums.
Companies based in the United States often face higher costs per incident, driven by litigation risk and regulatory pressure, with major industry reports continuing to place the average U.S. breach cost in the multi-million-dollar range. These figures include direct expenses like forensic investigations and emergency response, along with indirect costs such as customer churn, lost business opportunities, and brand damage.
The year was full of high-profile breaches. At the University of Phoenix, for example, more than 3.5 million people were reportedly affected, with full names, Social Security numbers, dates of birth, and internal IDs exposed, and reporting tied the incident to Clop-linked activity.
Another ransomware group, Medusa, was linked to an attack on SimonMed that reportedly affected more than a million people, exposing a broad set of personal and health-related information. The case reinforced a familiar lesson: strong identity controls matter, but data protections must assume perimeter defenses can fail.
SoundCloud also faced a reported security incident that impacted a significant portion of its members, exposing user emails and profile information. Security analysts pointed to the need to strengthen access controls and to protect sensitive data at its core so that stolen records have limited value.
Several other cases served as reminders of both the frequency of breaches and the outsized costs of poorly protected data.
Using Automation And Governance
Automation plays an increasing role in data masking at scale. Enterprises use tools that automatically discover and classify sensitive data, apply masking rules based on defined policies, and generate audit logs for compliance and reporting.
Strong governance reduces the burden on security teams and helps maintain consistent protections across systems and environments. And while data masking can dramatically reduce the impact of breaches by rendering exposed records far less valuable, it’s most effective when treated as part of a broader security architecture that also includes monitoring, anomaly detection, and incident response.
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