Data Security & Access Control

Write and dynamically enforce data policies for simplified security and access control. Immuta’s plain language policy authoring, streamlined orchestration, scalable access controls, and dynamic data masking protect data without slowing you down.

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Our Value

Unlock More Data with Dynamic Security

Immuta’s data security and access control allow data-driven organizations to simplify operations, improve data security, and unlock data’s value – without creating bottlenecks. With dynamic data security, you can:

Reduce the number of policies needed to manage data

Get the right data into the right hands at the right time

Streamline data sharing across lines of business, geographies, and third parties

Deliver more data products faster to drive bottom line results

Plain Language Policy Editor

Author policies in plain language so all security and governance stakeholders can understand how data is protected, without the need for specialized engineering resources.

Real-Time Policy Orchestration

Write policies once and enforce them everywhere, in real time across teams and regions. Separating policy from platform ensures policies are applied consistently across all cloud technologies, so you can rest assured that data is covered by the right policies, regardless of where it lives.

Attribute-Based Access Control

Scale policy enforcement with attribute-based access control (ABAC). Using attributes such as geography, department, or purpose to determine access rights, a single ABAC policy can replace more than 100 RBAC policies.

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Dynamic Data Masking

Apply data masking techniques on the fly to protect data internally and externally. With no coding or data copying required, this means you can easily put sensitive data to use – without compromising privacy.

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Data Privacy Controls

Implement advanced privacy enhancing technologies (PETs), including anonymization and randomized response, to achieve compliance with regulations like GDPR and HIPAA – no specialized skill sets or data copies required.

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Domain-Based Data Ownership

Accelerate access to data by federating governance to those closest to the data. Immuta allows you to define data controls at the enterprise and domain levels, so data owners are able to efficiently make data access decisions with full knowledge of the data's business context.

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Covering the Full Data Security Spectrum

Ready to enforce data security and access controls, but not sure where to start?

Knowing what sensitive data you have, where it resides, and how it is being accessed and used can help pinpoint potential risks, so you can put the right access controls in place to protect it. Immuta makes sure you can easily do it all – without sacrificing speed, utility, or security.

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Unlock Your Data

Our aim was to create more intelligent access control with greater efficiency. This was only possible through an advanced implementation of access control, which facilitates a higher degree of automation and transparency.

Vineeth Menon
Vineeth Menon Head of Data Lake Engineering

fewer data policies required compared to RBAC


faster time to data access


increase in data usage


cost savings versus competitive solution

Frequently Asked Questions

What is considered sensitive personal information?

Sensitive personal information refers to any data about an individual that must be kept confidential and protected from unauthorized access. Two well-known categories of sensitive personal data are personally identifiable information (PII), like first and last names, email addresses, and credit card numbers, and protected health information (PHI), such as medical records, lab results, and medical bills. Other types of sensitive data also exist, including commercially sensitive data, like private company revenues, HR analytics, and IP, as well as classified information, like top secret, secret, and confidential data. Direct identifiers, like names, are often considered highly sensitive, but indirectly identifying attributes like hair color, height, and job title, can also be considered sensitive when combined with other data sets.

What does it mean when data is de-identified vs. anonymized?

How can you tell the difference between de-identified vs. anonymized data? Data anonymization is the process of totally adjusting or removing personally identifiable information (PII) from a dataset in order to protect the individual who created the data. An anonymized data set completely scrubs or encrypts this PII to prevent it from being linked back to a given individual. Data de-identification similarly detaches direct identifiers from PII to protect individuals through methods like pseudonymization and randomization. This is done, however, in a way that does not completely sever the data from being re-identified if need be.

What should I look for in a multi-cloud governance platform?

When evaluating a multi-cloud governance platform, teams should consider the following: financial capability, product vision, market share, and partner ecosystem. Beyond this, teams need to consider a range of functional requirements, including the level of support needed, various security needs, applicable regulatory requirements, and pricing structure. Ultimately, a multi-cloud governance platform should be able to apply policies and govern access to all data in a given ecosystem, regardless of which cloud platform it is stored or accessed in.

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