Applying New ML Techniques to Uncover Duplicate & Derivative Data

Presented by

Roger Hale, CSO, BigID, Brandon Dunlap, Moderator

About this talk

With data growing exponentially, data sources spread across many areas, including data and multiple clouds. This leads to difficulty in proactively reducing risk and protecting critical and sensitive data. One of the largest sources of risk comes from duplicate and redundant sensitive data migrating across multiple data sources and stores. Blindspots into your derivative data can create unnecessary data exposure risks; stall cloud migration initiatives, data minimization initiatives, and M&A processes; and present an additional layer of compliance challenges across the board. Join BigID and (ISC)2 on September 15, 2020 at 1:00 p.m. BST for a discussion about these risks and how to discover, identify, and minimize duplicate and similar data. Areas covered will include: · How to identify and tag duplicate, similar, and redundant data · Map Data migration and identify original data sources · Best practices for minimizing critical data across data sources and removing duplicate data · How to apply next-gen ML techniques to reduce risk and increase confidence in your data · Build a data driven risk profile of your data sources

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BigID aims to transform how enterprises protect and manage the privacy of personal data. Organizations are facing record breaches of personal information and proliferating global privacy regulations like the EU GDPR with fines reaching 4% of annual revenue. Today enterprises lack dedicated purpose built technology to help them track and govern their customer data at scale. By bringing data science to data privacy, BigID aims to give enterprises the software to safeguard and steward the most important asset organizations manage: their customer data. BigID has offices in the US and Israel and is founded by security industry veterans spanning the identity, data security, big data and governance markets.