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What is PK Privacy?
Organizations need to keep up with the rapidly changing privacy landscape while ensuring the protection of the personally identifiable information they manage. PK Privacy offers these entities a way to automate the processes involved in data discovery and reporting, which helps to ease some of the operational hurdles posed by privacy regulations. This solution enhances the efficiency of the DSAR response process, allowing compliance to be achieved more effectively and within financial limits. It also permits the creation of automated rules to delete or redact sensitive data as soon as it is recognized. The platform supports pseudonymization, anonymization, and de-identification of sensitive personal information, thereby providing an extra layer of security. In addition, it identifies data that may need to be archived or deleted based on factors like age or lack of activity. Organizations are also empowered to detect and confirm breaches through ongoing surveillance, enabling them to accurately evaluate and report the implications of such incidents. By ensuring continuous oversight of endpoints, servers, and enterprise applications, PK Privacy allows organizations to manage their privacy data competently and adhere to compliance requirements. Furthermore, integrating these automated solutions not only simplifies the complexities of privacy management but also enhances the overall security of data, fostering a culture of accountability and trust within the organization.
What is PHEMI Health DataLab?
In contrast to many conventional data management systems, PHEMI Health DataLab is designed with Privacy-by-Design principles integral to its foundation, rather than as an additional feature. This foundational approach offers significant benefits, including:
It allows analysts to engage with data while adhering to strict privacy standards.
It incorporates a vast and adaptable library of de-identification techniques that can conceal, mask, truncate, group, and anonymize data effectively.
It facilitates the creation of both dataset-specific and system-wide pseudonyms, enabling the linking and sharing of information without the risk of data leaks.
It gathers audit logs that detail not only modifications made to the PHEMI system but also patterns of data access.
It automatically produces de-identification reports that are accessible to both humans and machines, ensuring compliance with enterprise governance risk management.
Instead of having individual policies for each data access point, PHEMI provides the benefit of a unified policy that governs all access methods, including Spark, ODBC, REST, exports, and beyond, streamlining data governance in a comprehensive manner. This integrated approach not only enhances privacy protection but also fosters a culture of trust and accountability within the organization.
Integrations Supported
Amazon Web Services (AWS)
Apache Hive
Apache Ranger
Apache Spark
Cloudera
Hadoop
Microsoft Azure
Microsoft Power BI
Tableau
Integrations Supported
Amazon Web Services (AWS)
Apache Hive
Apache Ranger
Apache Spark
Cloudera
Hadoop
Microsoft Azure
Microsoft Power BI
Tableau
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
PKWARE
Date Founded
1986
Company Location
United States
Company Website
www.pkware.com/solutions/data-privacy
Company Facts
Organization Name
PHEMI Systems
Date Founded
2013
Company Location
Canada
Company Website
www.phemi.com
Categories and Features
Data Privacy Management
Access Control
CCPA Compliance
Consent Management
Data Mapping
GDPR Compliance
Incident Management
PIA / DPIA
Policy Management
Risk Management
Sensitive Data Identification
Categories and Features
Big Data
Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates
Data Governance
Access Control
Data Discovery
Data Mapping
Data Profiling
Deletion Management
Email Management
Policy Management
Process Management
Roles Management
Storage Management
Data Lineage
Database Change Impact Analysis
Filter Lineage Links
Implicit Connection Discovery
Lineage Object Filtering
Object Lineage Tracing
Point-in-Time Visibility
User/Client/Target Connection Visibility
Visual & Text Lineage View
Data Management
Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge
Data Privacy Management
Access Control
CCPA Compliance
Consent Management
Data Mapping
GDPR Compliance
Incident Management
PIA / DPIA
Policy Management
Risk Management
Sensitive Data Identification
Data Security
Alerts / Notifications
Antivirus/Malware Detection
At-Risk Analysis
Audits
Data Center Security
Data Classification
Data Discovery
Data Loss Prevention
Data Masking
Data-Centric Security
Database Security
Encryption
Identity / Access Management
Logging / Reporting
Mobile Data Security
Monitor Abnormalities
Policy Management
Secure Data Transport
Sensitive Data Compliance