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Sensitive data protection in testing environment

Pricchaa provides software solutions to detect and encrypt sensitive data elements within testing data sources. More specifically, Pricchaa enables you to:

  1. Identify sensitive data elements automatically or user configuration mode
  2. Encrypt sensitive data elements while preserving format and referential integrity between all source systems
  3. Maintain referential integrity throughout the life cycle of the project including incremental test data
  4. Decrypt test results for validation 

Download Product Specification

Supported Data Sources

Source TypeSupported Sources
Big Data PlatformApache Hadoop, Cloudera, Hortonworks, MapR
No SQL DatabasesMarkLogic, MongoDB, Cassandra, Hbase, Hive
Cloud SourcesS3, AWS Redshift, Snowflake
Regular DatabasesOracle, MS-SQL, Vertica, Teradata
File SystemLinux, Windows, SAS Files

Why Pricchaa ?

Lightning Fast

Linearly scalable platform architected to handle large data volume located in a variety of sources including HDFS, Oracle, SAS, Cassandra, MongoDB, File Systems

Real Time Alerts

Real time data and usage anomalies alerts leveraging proven deep learning algorithms

Unified Platform

A unified platform to detect, encrypt and protect sensitive information present in your data repositories, real-time data exchanges, cloud applications.

Self Service

Easily connect to data sources, auto discover sensitive data, and encrypt data elements in a few clicks using intuitive easy to use interface.

Our Advantages

Advanced Encryption Technology
Adaptive Machine Learning Algorithm
Designed for Big Data
Compliance Ready
Independent and Non-Intrusive
Cloud Ready
Scalable
Advanced Encryption Technology

We have implemented advanced Format Preserving Encryption using FE1 scheme. We also used Key Management Interoperability Protocol(KMIP) to store the encryption keys securely

Adaptive Machine Learning Algorithm

Our solution leverages big data deep learning algorithms to detect sensitive information, data anomalies, access violations and usage anomalies.

Designed for Big Data

Designed for processing large data volume. Supports traditional data sources as well no-sql big data sources. Architected for searching unstructured data such as PDF, Social Media, Microsoft Office, EPUB, compressed files etc.

Compliance Ready

Eighteen different checks are pre-built to support PII and PHI privacy data types

Independent and Non-Intrusive

Independently and non-intrusively examines data

Cloud Ready

Seamlessly examines enterprise data present in private or public cloud

Scalable

Linearly scalable to support processing of large data volume using commodity hardware

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Installation Requirements

Pricchaa provides both cloud and on-premise installation option. Following specifications are applicable for the On-Premise Installation

Server System Requirements

  • 2 quad-/hex-/octo-core CPUs, running at least 2-2.5GHz
  • 64-512 GB RAM
  • 1– 2 TB Hard Drive
  • 2- 4 Nodes
  • 10 Gigabit Ethernet

Operating System

  • Linux ( Ubuntu 12+ or Redhat 6/7 ) – 64 bit

Big Data Stack

  • Hadoop 2.6 ( MapR, Cloudera, HortonWorks)
  • Spark 2.0

Other Software

  • Apache, PHP 5.5
  • MySQL 5.7

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