PontusVision GDPR Open Source IT Solution

This is the PontusVision GDPR Open Source IT Solution.  The world’s first open source platform that helps businesses comply with the General Data Protection Regulation (GDPR).

Open Source GDPR

PontusVision GDPR Open Source IT Solution’s beta edition was just launched on the 27th of October at the London Tech Day.  We will be updating this site and send out notifications via Twitter (@PontusVision) in the next coming days.

PontusVision GDPR Open Source

The PontusVision GDPR Open Source IT solution can help companies comply with GDPR in three  steps:

gdpr-extract  Extract – Enables c ustomers to extract personal information from a variety of different areas, including e-mails, MS Office documents, Relational Databases, CRM Systems, and Big Data Lakes.

gdpr-track  Track – Enables customers to track the origin of the data, including where the data came from, how to delete it, update it, and stores the data into a Graph database.

gdpr-comply  Comply – Gives data protection officers a web portal with a single view of the Data, including the ability to fulfil subject access requests, and data breach analysis (figure out which data was impacted by security breaches).


Why Pontus

PontusVision GDPR Open Source IT Solution is the only one in the market that combines the following features in one product:

Open Source – all Pontus Vision GDPR software has been open sourced.  The UK Government department where the platform was born has very progressive attitudes for using and producing open source software.   This gives customers a clear view of the code, and prevents vendor lock-in.

 Cloud Neutral – our solution does not rely on any cloud vendor-specific technologies.  The solution can be deployed on-prem, within any cloud vendor that supports Linux Servers, and even across cloud vendors for extra resiliency.

 Cyber Security – we have had to get our architecture and design revised by a number of accreditors including reviews from NCSC/GCHQ.  This enables customers to be reassured that the platform is as safe as their needs require.

 Scalable Automation – The Pontus Vision GDPR Architecture and design have as few manual steps as possible to enable vast quantities of data to be processed.  The solution is able to scale to 100s of billions of records.

gdpr-many-formats Many Formats – Pontus Vision GDPR was designed and built as a modular solution that is capable of taking data from hundreds of different formats.  We also include the ability to create bespoke sources and create a reusable library of components.


Our architecture follows our simple three steps of Extract Track Comply:

Open Source GDPR Architecture

On the Extract part of the design, we are using a powerful open source flow management infrastructure (Pontus-NiFi) based on the Apache NiFi project; that enables users to convert data from a variety of platforms ready for the Track phase.

On the Track part of the design, we store data into a canonical format, and can run either Online Transaction Processing (OLTP), or Online Analytics Processing (OLAP) queries on the data to clean up the application.  We use a gremlin Tinkerpop 3.3.0 compliant graph database do front those queries, and store the data into Apache Hbase 1.3.1 and index it with Elastic Search 5.6.3.  We can also apply very rich redaction/filtering rules inside these stores to ensure that not even an administrator can see sensitive data.  All the data is encrypted both in-flight (TLS) and at-rest (using dmcrypt), with keys optionally stored in a Hardware Security Module (HSM).




Lastly, the Comply part of the architecture is what gives users the ability to query the data.  We ensure that all users are authenticated by using a combination of either Apache Knox or Nginx as HTTPs Gateways, with KeyCloak to authenticate users and generate a JSON Web Token (JWT) that can then be used to track user queries throughout the system.  KeyCloak can authenticate users from a variety of external (OpenID, SAML, OAUTH2) as well as internal sources (e.g. Active Directory).  The user queries can be easily modified to cater for the user needs without any new code being created.

Try It

Here’s the link to our dockerhub image: