Secure

Dynamic Data Masking

Immuta’s dynamic data masking protects data by modifying or hiding sensitive values without changing the underlying data. With Immuta’s masking capabilities, you can apply security and privacy controls at query runtime and unlock more value from your sensitive data without sacrificing security.

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Our Value

Maximizing Utility & Security with Dynamic Data Masking

Immuta’s dynamic data masking helps organizations across every industry simplify operations, improve data security, and unlock sensitive data’s value. With dynamic data masking, you can:

Increase permitted use cases so you can do more with your data

Accelerate time to access by masking data when it is queried

Share data while complying with rules, regulations, and data sharing agreements

Reduce engineering burden by dynamically enforcing policies without copying data

Plain Language Policy Authoring

Immuta allows you to author attribute-based access control policies in plain language or as-code, so they’re easy for any stakeholder to manage. This allows you to mask data without the need for specialized engineering resources.

Dynamic Data Masking

Copying data and manually removing or anonymizing values can delay analysis and weaken data’s utility. Immuta’s dynamic data masking policies support hashing, regular expression, rounding, conditional masking, and replacing with null or constant, with reversibility, with format preserving masking, and with k-anonymization, as well as external masking – all without ever copying or moving data.

Conditional Data Masking

Protect yourself from data leaks without resorting to manual changes. Immuta automates access restrictions based on masking policy conditions, such as time-based windows, users’ geographies, and data in adjacent cells or reference tables. With Immuta’s conditional logic in masking policies, you get flexible policy enforcement with reducing risk.

Covering the Full Data Security Spectrum

How do you know where to apply data masking policies and if those policies are working? Dynamic data masking is a key part of data security, but it’s one piece of the puzzle. With Immuta, you can discover, secure, and monitor data to detect risks. See how you can do all three – without sacrificing speed or utility.

Find out more about the Immuta Data Security Platform.

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Partners

Dynamic Data Masking
for Any Platform

Easily connect with Snowflake to dynamically mask sensitive data in the Data Cloud.

Integrate with the Databricks Lakehouse Platform to dynamically enforce masking policies.

Customers

Unlock Your Data

“If you don’t automate around your security and simplify it, the biggest problem you’re going to have is some human making a mistake. I don’t have to worry about building security tools. The possibilities exist for us in a real sense because we can get to that level of finesse around our controls.”

Kaj Pedersen Chief Technology Officer, AstrumU
2x

more use cases unlocked

200+

data products created via a secure data mesh

400%

enhancement in analytics by using sensitive data securely

30,000

queries protected across 700+ tables

See our customers' stories

Frequently Asked Questions

What is static data masking?

Static data masking (SDM) masks data at rest rather than in active use. It does so by creating a copy of an existing data set and scrubbing it of all sensitive and/or personally identifiable information (PII). Once the information is masked, the data can then be stored, shared, and accessed for use without putting sensitive information at risk.

Data Masking 101: A Comprehensive Guide
What is dynamic data masking?

Dynamic data masking is the process of using fake, hidden, or purposefully “noisy” data to conceal or mask the sensitive elements in a data set. Within existing databases, masking techniques such as k-anonymization, differential privacy, and randomized response can protect sensitive data from being reverse-engineered or re-identified. These masking techniques help ensure data remains private without hindering its ability to be used for analysis.

What Are Data Masking Best Practices?
What is the difference between data masking vs. encryption?

Data masking operates by modifying or hiding sensitive values in a data set without changing the underlying data. In doing this, it creates a “fake” version of the data that is still usable for analysis/tools/etc. Data encryption, on the other hand, completely changes data into an illegible code that can only be reversed back into its original form with the assistance of an encryption key. This renders the data useless to anyone without the encryption key, where masked data can still be used for various purposes without exposing the more sensitive information involved.

What Are the Most Common Types of Data Masking?
Is data nulling considered data masking?

Yes, data nulling can be considered a data masking technique. Nulling masks sensitive data in a data set by replacing any given value with a NULL. When applied, the underlying data will appear to be NULL rather than its original value. This removes any identifiability from the column, at the cost of removing all utility. It is useful to apply this policy to numeric or text attributes which have a high re- identification risk, but little analytic value.

What Are Data Masking Best Practices?
Additional Resources

Read More About Dynamic Data Masking

White Paper
Data Masking 101: A Comprehensive Guide
Blog
What Are Data Masking Best Practices?
Blog
How to Mask Sensitive Financial Data with Databricks and Immuta

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