- 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
- PolicySync
- Snowflake
- Redshift
- Redshift Spectrum
- PostgreSQL
- Microsoft SQL Server
- Databricks SQL
- RocksDB
- Google BigQuery
- Power BI
- UserSync
- Privacera Plugin
- Databricks
- Spark standalone
- Spark on EKS
- Trino Open Source
- Dremio
- AWS EMR
- AWS EMR with Native Apache Ranger
- GCP Dataproc
- Starburst Enterprise
- Privacera services (Data Assets)
- Audit Fluentd
- Grafana
- Access Request Manager (ARM)
- 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
- CLI actions
- Debugging and logging
- Advanced service configuration
- Increase Privacera portal timeout for large requests
- Order of precedence in PolicySync filter
- Configure system properties
- PolicySync
- Databricks
- Table properties
- Upgrade Privacera Manager
- Troubleshooting
- Possible Errors and Solutions in Privacera Manager
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- 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
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- Possible Errors and Solutions in Privacera Manager
- 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
- Example Workflow Usage
- Discovery health check
- Reports
- Built-in Reports
- Saved reports
- Offline reports
- Reports with the query builder
- How-to
- Privacera Encryption Guide
- Essential Privacera Encryption terminology
- Install Privacera Encryption
- Encryption Key Management
- Schemes
- Scheme Policies
- Encryption Schemes
- Presentation Schemes
- Masking schemes
- Encryption formats, algorithms, and scopes
- Deprecated encryption formats, algorithms, and scopes
- Encryption with PEG REST API
- PEG REST API on Privacera Platform
- PEG API Endpoint
- Encryption Endpoint Summary for Privacera Platform
- Authentication Methods on Privacera Platform
- Anatomy of the /protect API Endpoint on Privacera Platform
- About Constructing the datalist for protect
- About Deconstructing the datalist for unprotect
- Example of Data Transformation with /unprotect and Presentation Scheme
- Example PEG API endpoints
- /unprotect with masking scheme
- REST API Response Partial Success on Bulk Operations
- Audit Details for PEG REST API Accesses
- REST API Reference
- Make calls on behalf of another user
- Troubleshoot REST API Issues on Privacera Platform
- PEG REST API on Privacera Platform
- Encryption with Databricks, Hive, Streamsets, Trino
- Databricks UDFs for encryption and masking
- Hive UDFs
- Streamsets
- Trino UDFs
- 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
- Request Access
- Approve access requests
- Service Explorer
- User/Groups/Roles
- Permissions
- Reports
- Audit
- Security Zone
- Access Control using APIs
- AWS User Guide
- Overview of Privacera on AWS
- Set 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
- S3 browser
- Getting started with Minio
- Plugins
- How to Get Support
- Coordinated Vulnerability Disclosure (CVD) Program of Privacera
- Shared Security Model
- Privacera documentation changelog
Privacera Access Management
How access policy enforcement works
Privacera Access Management works with and extends Apache Ranger to provide data access governance with centralized management of authorization policies and auditing.
There are several approaches to policy enforcement based on the data store and the type of access. All provide consolidated audit logging. And in all cases, data does not have to stream through Privacera’s code, so the overhead added by any policy enforcement is kept to a minimum.
Access control via Ranger policy enforcement points
Where available, Privacera leverages Apache Ranger-style distributed policy enforcement points for access control. Many data processing engines, such as Hive, Spark and Presto, support these plugins. Policies created and managed in the Privacera portal are distributed to, and synchronized with, these policy enforcement points. Access control decisions happen at the engine, inline with query execution.
Access control via PolicySync
For data sources like RDBMS where Ranger-style access control plugins are not available, access control policies are enforced via policy syncrhonization. Privacera’s PolicySync component translates the configured access control policies into the data source’s native access control framework, for example by sending a relational database GRANT/REVOKE statements, generating views where needed for additional layers of access control like data masking and row filtering, and so on. When there are policy changes in Privacera, new or changed objects in the data source, or changes to users, groups and roles, updates are pushed to the data source to keep it aligned with the latest policies. For details on configuring PolicySync, see https://docs.privacera.com/latest/platform/pm-ig/policysync_advanced_custom_prop/
Access control via data access server
For data in object stores like S3 or ADLS, access requests flow through Privacera’s Data Access Server for policy enforcement. The Data Access Server integration method redirects data access requests to a Privacera ‘authentication broker’ inserted into the control and data flow. For requests that are allowed based on authentication and other policy checks, the authentication broker generates a signed URL that the requestor can use to fetch the requested data directly from the object store. All access attempts are audited.
For details on configuring Data Access Server, see AWS.
Automated access request workflows
Privacera provides enterprises with a seamless and automated method to manage user access provisioning and de-provisioning for their data. Using the proven open source business process workflow engine Flowable, which is embedded with Privacera platform, enterprises can now automatically manage the creation and updating of policies based on requests from users based on their roles or based on the attributes such as location and other metadata for the data that they need to get access instead of prior manual authoring of access control policies and managing entitlements through manual user intervention of the policy administrators.
Using Access Workflows feature, enterprise users who need access to data that they need to do their jobs, for example, data associated with a particular project or based on their corporate role or need for accessing all data that has specific data classifications attached to it, can now raise an access request ticket directly within Access Management. These requests are then routed to the appropriate approvers who control access to the data using the workflow engine. Approvers can be set up using a specific role. Such approvers can be the policy administrators or data owners or data stewards, for example, who typically control access to the data by writing access control policies within Access Management.
Once the users file such a ticket requesting access to a specific role or the data directly, a ticket is created and the user is provided an email notifying that a ticket for access has been raised and the appropriate approvers for that data are also notified by email. The approvers can then log in to Access Management and make a decision whether to approve or reject this request. For example, the approvers can review the ticket details to validate the business purpose, the scope of data requested and the duration of access requested and make any modifications to these based on enterprise-wide standards and policies. If the request is approved by the approver, Privacera platform automatically creates relevant policies to reflect the user's requested data entitlements whether by creating policies or adding users to the roles without any manual intervention from the approver. An audit entry linking this approval action to the ticket id is generated to provide traceability and for compliance purposes automatically in Access Management.
Types of Access
Based on a user role
Based on tags applied to a data element
Based on a resource that is already connected to Access Management
Based on a resource that is not yet connected to Access Management
Access Type _ANY
The _ANY
access type appears in the audit record when the Ranger plugin implicitly derives database permissions. This occurs when a user has any permission for any resource in a database, such as a single column.
Feature summary
Access Management through ABAC (Attribute-Based Access Control) and RBAC (Role-Based Access Control) policies
Resource-based (physical and logical metadata), as well as classification or tag-based (business metadata), access control policies
Comprehensive non-repudiable normalized audits with rich event metadata that detail 'who', 'what', 'when', and* 'where', * along with business context for each access request - whether allowed or denied
Built-in reports and dashboards for access governance, audit, and compliance
Polices are applied to resources to control access. They consist of:
Controlled access datasets which are subsets of connected data repositories and databases, defined by any combination of database, table, and column access (wildcards supported) or for filesystem/object stores based on object, file, folder names (with wildcard support for paths).
Enforcement period with start and end dates and times for access policies.
Users, Roles, or Groups: Users and Groups can be synchronized from enterprise sources such as LDAP, Active Directory, Azure AD. Roles can be composed of any combination of users, groups or other roles (nested) to map to permissions in policy conditions.
Fine-grained Permissions: “Select”, “Update”, “Create”, “Drop”, “Alter”, "Read”, “Write”.
Flexible composition of schemes for positive and negative permissions: Policy conditions include “Allow”, and “Deny” access as well as layer permissions specifying “Exclude from Allow” and “Exclude from Deny”.