The race toward AI adoption is accelerating, and with it comes significant regulatory challenges. According to the Perforce Delphix 2024 State of Data Compliance and Security Report, 95% of organizations are concerned about regulatory non-compliance in AI environments.
The challenge for many organizations is finding a way to preserve data privacy and ensure compliance without introducing bottlenecks or compromising the quality of their AI and analytics outcomes.
Steve Karam and Jatinder Luthra from Perforce Delphix and Jonathan Wilcox from Prelude Systems engage in an in-depth discussion on overcoming these challenges. This webinar covers actionable best practices, including how to:
- Automate the detection and irreversible masking of sensitive data to avoid reidentification.
- Achieve petabyte-scale performance by integrating masking into ETL data flows.
- Ensure realism of masked data for quality AI and analytics outcomes.
Presenters
Steve Karam
Steve Karam is an enterprise technologist and outcome-driven leader with diverse expertise in customer success, product management and engineering, education, SaaS, cloud, data technologies including AI/ML and natural language processing, and beyond across nearly every market vertical. A restless and relentless learner and advocate for personal and team growth who works primarily with federal, financial services, and healthcare organizations within Perforce. In the technical arena Steve is proficient or expert level with cloud architecture, multiple RDBMS, NoSQL, multiple programming languages including Python and React, common DevOps & SDLC tools, AI/ML, SaaS architecture, blockchain, data analytics, and several other topics.
Jatinder Luthra
Jatinder is a technology enthusiast, passionate about building innovative solutions and challenging conventional methods. As a Senior Principal Solutions Engineer in Perforce Delphix, he is focused on cloud business.