
- Cards: When a KPI or Sumo card is powered by a DataSet with PDP policies in place, impacted users and groups can only view data on the card that is relevant to them
- Alerts: PDP policies in an alert serve to filter the data and regulate which users can subscribe.
- DataFlows: While you cannot add input DataSets to a DataFlow if you are limited by PDP policies, it is possible to apply policies to the output DataSets generated by the DataFlow.
Creating a Personalized Data Permission (PDP) policy
In order to create a personalized data permission policy you will need to have both thedataset_id of the dataset that you would like to apply a filter as well as the user_ids or group_ids that will have access applied.
Once you have a dataset_id and either auser_id or group_id, choose filter.columns within the DataSet to apply filters. Each filter will need a filter.value and a filter.operator as seen in the example below.
Sample Request
See this sample request in Java, Python.
Listing all PDP policies applied to a DataSet
It is useful to see what existing permission policies have already been applied to the DataSet. This information can be used as a reference to mirror similar group or user access when creating new policies with other DataSets. Thedataset_id is required to retrieve all of a Dataset’s policies as seen below.
Sample Request
See this sample request in Java, Python.
Updating a PDP policy
Because users and groups change so frequently in addition to data, programmatically updating a DataSet policy can ensure better accuracy and timeliness when managing access rights to sensitive data. In order to update a DataSet Personalized Data Permission (PDP), you will need both thedataset_id and the policy_id.
Sample Request
See this sample request in Java, Python.