- Platform Release 6.5
- Privacera Platform Release 6.5
- Enhancements and updates in Privacera Access Management 6.5 release
- Enhancements and updates in Privacera Discovery 6.5 release
- Enhancements and updates in Privacera Encryption 6.5 release
- Deprecation of older version of PolicySync
- Upgrade Prerequisites
- Supported versions of third-party systems
- Documentation changelog
- Known Issues 6.5
- Platform - Supported Versions of Third-Party Systems
- Platform Support Policy and End-of-Support Dates
- Privacera Platform Release 6.5
- Privacera Platform Installation
- About Privacera Manager (PM)
- Install overview
- Prerequisites
- Installation
- Default services configuration
- Component services configurations
- Access Management
- Data Server
- UserSync
- Privacera Plugin
- Databricks
- Spark standalone
- Spark on EKS
- Portal SSO with PingFederate
- Trino Open Source
- Dremio
- AWS EMR
- AWS EMR with Native Apache Ranger
- GCP Dataproc
- Starburst Enterprise
- Privacera services (Data Assets)
- Audit Fluentd
- Grafana
- Ranger Tagsync
- Discovery
- Encryption & Masking
- Privacera Encryption Gateway (PEG) and Cryptography with Ranger KMS
- AWS S3 bucket encryption
- Ranger KMS
- AuthZ / AuthN
- Security
- Access Management
- Reference - Custom Properties
- Validation
- Additional Privacera Manager configurations
- Upgrade Privacera Manager
- Troubleshooting
- How to validate installation
- Possible Errors and Solutions in Privacera Manager
- Unable to Connect to Docker
- Terminate Installation
- 6.5 Platform Installation fails with invalid apiVersion
- Ansible Kubernetes Module does not load
- Unable to connect to Kubernetes Cluster
- Common Errors/Warnings in YAML Config Files
- Delete old unused Privacera Docker images
- Unable to debug error for an Ansible task
- Unable to upgrade from 4.x to 5.x or 6.x due to Zookeeper snapshot issue
- Storage issue in Privacera UserSync & PolicySync
- Permission Denied Errors in PM Docker Installation
- Unable to initialize the Discovery Kubernetes pod
- Portal service
- Grafana service
- Audit server
- Audit Fluentd
- Privacera Plugin
- How-to
- Appendix
- AWS topics
- AWS CLI
- AWS IAM
- Configure S3 for real-time scanning
- Install Docker and Docker compose (AWS-Linux-RHEL)
- AWS S3 MinIO quick setup
- Cross account IAM role for Databricks
- Integrate Privacera services in separate VPC
- Securely access S3 buckets ssing IAM roles
- Multiple AWS account support in Dataserver using Databricks
- Multiple AWS S3 IAM role support in Dataserver
- Azure topics
- GCP topics
- Kubernetes
- Microsoft SQL topics
- Snowflake configuration for PolicySync
- Create Azure resources
- Databricks
- Spark Plug-in
- Azure key vault
- Add custom properties
- Migrate Ranger KMS master key
- IAM policy for AWS controller
- Customize topic and table names
- Configure SSL for Privacera
- Configure Real-time scan across projects in GCP
- Upload custom SSL certificates
- Deployment size
- Service-level system properties
- PrestoSQL standalone installation
- AWS topics
- Privacera Platform User Guide
- Introduction to Privacera Platform
- Settings
- Data inventory
- Token generator
- System configuration
- Diagnostics
- Notifications
- How-to
- Privacera Discovery User Guide
- What is Discovery?
- Discovery Dashboard
- Scan Techniques
- Processing order of scan techniques
- Add and scan resources in a data source
- Start or cancel a scan
- Tags
- Dictionaries
- Patterns
- Scan status
- Data zone movement
- Models
- Disallowed Tags policy
- Rules
- Types of rules
- Example rules and classifications
- Create a structured rule
- Create an unstructured rule
- Create a rule mapping
- Export rules and mappings
- Import rules and mappings
- Post-processing in real-time and offline scans
- Enable post-processing
- Example of post-processing rules on tags
- List of structured rules
- Supported scan file formats
- Data Source Scanning
- Data Inventory
- TagSync using Apache Ranger
- Compliance Workflow
- Data zones and workflow policies
- Workflow Policies
- Alerts Dashboard
- Data Zone Dashboard
- Data zone movement
- Workflow policy use case example
- Discovery Health Check
- Reports
- How-to
- Privacera Encryption Guide
- Overview of Privacera Encryption
- Install Privacera Encryption
- Encryption Key Management
- Schemes
- Encryption with PEG REST API
- Privacera Encryption REST API
- PEG API endpoint
- PEG REST API encryption endpoints
- PEG REST API authentication methods on Privacera Platform
- Common PEG REST API fields
- Construct the datalist for the /protect endpoint
- Deconstruct the response from the /unprotect endpoint
- Example data transformation with the /unprotect endpoint and presentation scheme
- Example PEG API endpoints
- /authenticate
- /protect with encryption scheme
- /protect with masking scheme
- /protect with both encryption and masking schemes
- /unprotect without presentation scheme
- /unprotect with presentation scheme
- /unprotect with masking scheme
- REST API response partial success on bulk operations
- Audit details for PEG REST API accesses
- Make encryption API calls on behalf of another user
- Troubleshoot REST API Issues on Privacera Platform
- Privacera Encryption REST API
- Encryption with Databricks, Hive, Streamsets, Trino
- Databricks UDFs for encryption and masking on PrivaceraPlatform
- Hive UDFs for encryption on Privacera Platform
- StreamSets Data Collector (SDC) and Privacera Encryption on Privacera Platform
- Trino UDFs for encryption and masking on Privacera Platform
- Privacera Access Management User Guide
- Privacera Access Management
- How Polices are evaluated
- Resource policies
- Policies overview
- Creating Resource Based Policies
- Configure Policy with Attribute-Based Access Control
- Configuring Policy with Conditional Masking
- Tag Policies
- Entitlement
- Service Explorer
- Users, groups, and roles
- Permissions
- Reports
- Audit
- Security Zone
- Access Control using APIs
- AWS User Guide
- Overview of Privacera on AWS
- Configure policies for AWS services
- Using Athena with data access server
- Using DynamoDB with data access server
- Databricks access manager policy
- Accessing Kinesis with data access server
- Accessing Firehose with Data Access Server
- EMR user guide
- AWS S3 bucket encryption
- Getting started with Minio
- Plugins
- How to Get Support
- Coordinated Vulnerability Disclosure (CVD) Program of Privacera
- Shared Security Model
- Privacera Platform documentation changelog
Expunge policy
The Expunge policy removes sensitive information such as usernames and email addresses from your data. This information is moved into a quarantine folder.
The fields in the lookup file are compared to the records in the resource files. If the tag is found (the value in the lookup file matches the value in the resource file for the specified tag (Search for tags)), then the field value in the resource file will be deleted. Ensure that the header of the lookup file matches the header of the tag to be searched.
Note
The resource file should be scanned before applying the Expunge policy. The Expunge policy does not work on real-time or offline scans.
Expunge policy supported data sources
Thr Expunge policy supports the following data sources. Click the tab to display the data sources that are supported in the cloud.
AWS
S3
Snowflake
Redshift
AuroraDB Postgres
AuroraDB MySQL
PostgreSQL
Microsoft Azure
MSSQL Server Synapse
GCP
Google Cloud Storage
Expunge policy supported file formats
For a list of supported file formats that the Expunge policy can be applied to, see Supported file formats by policy type
Expunge policy fields
The following fields are included in the Expunge policy:
Name: The name of the Expunge policy.
Type: The type of policy.
Alert Level: The level of alert: high, medium or low.
Description: The description of the Expunge policy.
Status: A toggle to enable or disable the policy. It is enabled by default.
Application: The data source from which the scanned resources can be accessed and where the Expunge policy will be applied.
Lookup Application: The name of the data source containing lookup file. The lookup file should be in
.csv
format, with tag names in the header columns.Lookup File Location: The location of the lookup file.
Quarantine Location: The location of the data removed from the input file.
Note
Some applications such as Snowflake and Presto SQL follow the
[Db].[Schema].[Table]
hierarchy. You need to provide the Quarantine location in the correct format[Db].[Schema]
for these applications.Archive Location (Optional): The location of a copy of the original file.
Note
Some applications such as Snowflake and Presto SQL follow the
[Db].[Schema].[Table]
hierarchy. You need to provide the Archive location in the correct format[Db].[Schema]
for these applications.Search for tags: Tags that identify and classify the data to be removed.
Auto Run: If this feature is enabled, the Expunge policy is applied after a specified time interval.
Lookup File Location: Add a
.csv
file to the Lookup File Location field, and it should specify which sensitive data needs to be removed from resources based on tags. For example: File name is input.csv file with EMAIL tag (sample@gmail.com).When the file is being scanned, if “sample@gmail.com” tagged with EMAIL is matched, then this row will be removed.
Consider the following example:
A file, test_file.csv, is added to a data zone. Search for as EMAIL tag is added.
The scheduler is triggered and the system applies the Expunge policy to the resource (test_file.csv).
After applying the Expunge policy, a row in test_file.csv that contains sensitive information is removed from the file and moved to the specified quarantine location.