2nd Generation Privacy Technology for Sustainability

Published On: June 15, 2020Categories: Blog

So the word is out, 1touch.io has raised $14.2m based on its extraordinary growth and delivery to Privacy stakeholders (Security, Governance, and Privacy) of the holy grail in sustainable Data Privacy Initiatives: The ongoing, FULLY automated discovery of uses of personal data in an enterprise.

Early technologies have focused on management of personal data… once you know where to look for it; but that’s hardly the problem plaguing enterprises with limited resources already reeling from the economic impact of COVID-19. Data Privacy and Data Security are intricately linked. While data security is not equivalent to data privacy, the two are fundamentally intertwined, and data security often forms the technical execution basis of fundamental data privacy functions. Both are about protecting your brand and customer loyalty. And since when has a sustainable Data Security program included as part of the design, the need to inform a system where to look for threats. Take an Anti-Virus. How worthwhile is anti-virus if you need to tell it where to look for the viruses? Similarly with Data Privacy. How useful is Data Privacy technology if you need to tell it where to look for Personal Data?

First generation Privacy technologies had low standards for Discovery. ‘Tell me where to scan for your personal data, and I will tell you where your personal data is’.


Not really.

This has not been sustainable.

Personal Data uses within an organization change on a daily basis. Enterprise expectations and discovery needs far exceed what first generation solutions can bring. Enterprises are increasingly more discerning in tracking how they use, store, and share personal data. First generation technologies offer MAPPING sold as Discovery. This is not enough to ensure brand protection, customer loyalty and adherence to regulations such as CCPA, GDPR, etc. (Think about data flows and third party sharing)

One big problem with Mapping is that it massively relies on inefficient and inaccurate manual labor. The other big gap is that Mapping does not discover ALL real uses of personal data when they involve unknown repositories or unknown data. Enterprises are tired of having to manually map and classify all their applications and repositories on an ongoing basis to determine uses of personal data.

The market craves sustainability. We are here to deliver.