Data Security & Access Control

Write, edit, and dynamically enforce data policies for simplified governance and access control. Immuta’s plain language policy authoring, streamlined orchestration, and scalable access controls secure data without slowing you down.

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Before Immuta
  • Static policies and access controls are complex and unscalable
  • Specialized engineering resources are required to manually manage enforcement
  • Hard-to-understand policies limit data consumption
After Immuta
  • Attribute-based access controls are dynamic and massively scalable
  • Flexible policies are automatically enforced at query runtime inside the data pipeline
  • Plain language policy authoring enables distributed stewardship and self-service access
Policy Authoring & Distributed Stewardship

Empower cross-functional teams to secure their data – no coding or technical expertise necessary. Immuta allows data owners to write, apply, and maintain data policies in plain language or as code. This distributed stewardship across teams reduces bottlenecks in data access and workflows, so teams can do more with their data.

Distributed Stewardship for Data Mesh Architecture
Real-Time Policy Orchestration

Automate policy enforcement in real time across teams and regions. Immuta separates policy from platform, so policies are applied consistently across all cloud technologies. Streamline access requests so users can get data in seconds, and rest assured that data is covered by the right policies, regardless of where it lives.

What is Data Orchestration?
Attribute-Based Access Control (ABAC)

Scale secure data access without dealing with role explosion. Attribute-based access control dynamically determines data access based on information related to geography, clearance level, purpose, and more – making it a more agile, scalable approach than role-based access control (RBAC). A single Immuta ABAC policy can replace more than 100 RBAC policies.

Attribute-Based Access Control
Dynamic Data Masking

Strike the right balance between data privacy and utility. Immuta’s dynamic data masking capabilities work internally and externally to protect sensitive information with techniques like hashing, regular expression, rounding, conditional masking, and k-anonymization. Masking on the fly – with no coding or data copying required – means you can put sensitive data to use without compromising it.

Dynamic Data Masking
Data Privacy Controls

Eliminate reliance on specialized skill sets and data copies, and simplify data privacy. With Immuta’s easy-to-understand data access policies, technical and non-technical stakeholders alike can apply advanced privacy enhancing technologies (PETs), including anonymization, pseudonymization, and randomized response. Bypass data privacy complexity while staying compliant with regulations like GDPR and HIPAA.

Privacy Enhancing Technologies

Explore more of Immuta’s Capabilities


Sensitive data discovery and classification

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Security and data access control

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Continuous user and data activity monitoring

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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?

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 is a privacy policy builder?

Creating data privacy policies can be done in a couple of different ways. They can be written manually by data teams, or created using a privacy policy builder tool. A privacy policy builder can provide teams with a simple and streamlined approach to policy creation. This tool should allow for various stakeholders to contribute to policy creation, and do so in plain language so that these stakeholders need not understand more technical code. Once policies are built, they should be applied automatically and universally to ensure data privacy.

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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