Enforce fine-grained access control at the column-, row-, and cell-level, with consistent, native enforcement across all leading cloud data platforms — without writing code or copying data.
Capability
Policy Orchestration
Immuta empowers data teams to automate data policy orchestration and enforcement in real-time across regions and teams.
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Before Immuta
- Policy creation and enforcement requires manual effort that takes months
- Enforcement is data platform-specific, not universal
- Thousands of lines of SQL code required
With Immuta
- Automated access takes seconds
- Enforcement works consistently across platforms
- Limited or no code required
Fine-Grained Data Security
Dynamic Data Masking
Apply dynamic masking policies at query runtime with hashing, regular expression, rounding, conditional masking, replacing with null or constant, with reversibility, with format preserving masking, and with k-anonymization, as well as external masking, without the need to write code or copy data.
Dynamic Data MaskingPolicy Orchestration
Orchestrate policy enforcement by mapping powerful ABAC and PBAC models to foundational access controls, without running unsupported data access patterns by the data platform vendor or impacting SLAs.
Secure Data Collaboration
Data-level zones automatically equalize access rights for all users, making it easy and safe to publish derived data sets without leaking data to users with different permissions.
Access Request Workflows
Self-service workflows enable data consumers to request access to data, acknowledge approved usage purposes, request access control changes, and propose new data collaboration projects.
Impersonation
Organizations using service accounts from BI dashboards can impersonate each end user to restrict access based on existing policies, without any disruptive changes or risk of a data leak.
What is data de-identification?
Data de-identification is the removal of personal information, such as names, specific geographic locations, telephone numbers, and Social Security numbers, to prevent the identification of specific individuals within a data set. This practice mitigates privacy risks and prepares data for access, analysis, and sharing
Learn moreWhy is it important to de-identify data?
De-identifying data preserves individuals’ privacy and enables valuable data sharing and use. De-identification is a core requirement for HIPAA compliance, as it ensures that medical and health data can be used in areas such as research, policy assessment, and comparative effectiveness studies, without compromising the individual’s right to privacy.
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