Create Policies API Examples
Subscription Policies
Anyone Can Subscribe
name: Anyone
policyKey: subscription anyone
type: subscription
actions:
type: anyone
automaticSubscription: false
description: Rationale
circumstances:
- type: tags
tag: DiscoveredAnyone Can Subscribe When Approved
name: Approval
policyKey: subscription approval
type: subscription
actions:
type: approval
approvals:
- specificApproverRequired: false
requiredPermission: OWNER
- specificApproverRequired: true
requiredPermission: GOVERNANCE
description: Rationale
circumstances:
- type: columnTags
columnTag: DiscoveredUsers with Specific Groups or Attributes
Users with Specific Groups or Attributes (Advanced)
Individual Users You Select
Data Policies
Data Owner Restrictions
Masking Policies
Conditional Masking
Conditional Masking (Using Otherwise Clause)
With a Constant
Format Preserving Masking
With Hashing (No Tags)
K-Anonymization (Using Fingerprint)
Sample data is processed during computation of k-anonymization policies
When a k-anonymization policy is applied to a data source, the columns targeted by the policy are queried under a fingerprinting process that generates rules enforcing k-anonymity. The results of this query, which may contain data that is subject to regulatory constraints such as GDPR or HIPAA, are stored in Immuta's metadata database.
The location of the metadata database depends on your deployment:
Self-managed Immuta deployment: The metadata database is located in the server where you have your external metadata database deployed.
SaaS Immuta deployment: The metadata database is located in the AWS global segment you have chosen to deploy Immuta.
To ensure this process does not violate your organization's data localization regulations, you need to first activate this masking policy type before you can use it in your Immuta tenant. To enable k-anonymization for your account, see the k-anonymization section on the app settings how-to guide.
K-Anonymization (by Specifying K)
Sample data is processed during computation of k-anonymization policies
When a k-anonymization policy is applied to a data source, the columns targeted by the policy are queried under a fingerprinting process that generates rules enforcing k-anonymity. The results of this query, which may contain data that is subject to regulatory constraints such as GDPR or HIPAA, are stored in Immuta's metadata database.
The location of the metadata database depends on your deployment:
Self-managed Immuta deployment: The metadata database is located in the server where you have your external metadata database deployed.
SaaS Immuta deployment: The metadata database is located in the AWS global segment you have chosen to deploy Immuta.
To ensure this process does not violate your organization's data localization regulations, you need to first activate this masking policy type before you can use it in your Immuta tenant. To enable k-anonymization for your account, see the k-anonymization section on the app settings how-to guide.
K-Anonymization (by Specifying Re-identification Probability)
Sample data is processed during computation of k-anonymization policies
When a k-anonymization policy is applied to a data source, the columns targeted by the policy are queried under a fingerprinting process that generates rules enforcing k-anonymity. The results of this query, which may contain data that is subject to regulatory constraints such as GDPR or HIPAA, are stored in Immuta's metadata database.
The location of the metadata database depends on your deployment:
Self-managed Immuta deployment: The metadata database is located in the server where you have your external metadata database deployed.
SaaS Immuta deployment: The metadata database is located in the AWS global segment you have chosen to deploy Immuta.
To ensure this process does not violate your organization's data localization regulations, you need to first activate this masking policy type before you can use it in your Immuta tenant. To enable k-anonymization for your account, see the k-anonymization section on the app settings how-to guide.
Make Null Using Column Regex
Randomized Response
Randomized Response (by Specifying Standard Deviation)
Using a Regex
With Reversibility
Using Rounding (Date)
Using Rounding (Using Fingerprint)
Using Rounding (Numeric)
Minimize Data Created Between
Purpose Restrictions
Any Purpose
Purpose in Server
Row-level Policy
By Time
Where User
Custom Where Clause
Multiple Policies
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