Mr. Koushik Kumar Ganeeb

Evaluating Data Masking Strategies for Data Analytics

Abstract:

 

In the digital era, safeguarding sensitive information is super critical and mandatory especially with AI advancements in Data Analytics. Data masking has emerged as a critical technique to protect data privacy and security, especially in customer data driven environments such as development, testing, and training. I would like to present a comprehensive analysis of traditional data masking techniques and introduce a cutting edge approach that enhances data protection while maintaining data utility. Data Analytics at scale would require the ability to identify sensitive data and mask it without exposing it for analytical purposes. I would like to speak about the necessity of setting a standard for masking methods, evaluating their strengths, limitations, and applicability in different scenarios. The proposed technique is assessed against traditional methods to demonstrate its efficacy and potential advantages.

Profile:

Mr. Koushik Kumar Ganeeb is an innovative technology leader specializing in Data Engineering and AI-driven solutions. As a Principal Member of Technical Staff (PMTS) at Salesforce Data Cloud, he leads AI initiatives for Einstein Data Analytics, Marketing Cloud Connector, and Intelligence Reporting, with contributions impacting major tech giants like Meta, Amazon, and Google. Widely regarded as a subject matter expert, Mr. Ganeeb has authored papers in IEEE, Springer, and other top journals, and has reviewed over 100 manuscripts for 25 high-profile IEEE conferences. Additionally, he reviews prestigious fellowships such as IEEE Senior Membership and BCS Fellowship and is a Gartner Peer Community Ambassador, influencing business direction in the AI era. He is also a Top 50 ADP List Mentor, coaching and mentoring professionals about the Industry trends in the Data and AI space.