Your organization’s data is extremely important. And it is being pulled into AI initiatives faster than most leaders are prepared for.
The rapid expansion of enterprise data into AI and analytics workflows poses unique risks to sensitive data, but many organizations are still grappling with regulatory blind spots and misguided or rudimentary compliance efforts. To effectively and safely adopt AI, it is critical to act now to protect your organization from theft, security, and compliance risks. The problem is that many executive leaders don’t know where to begin.
AI and data security experts from Perforce Delphix share the unique challenges of adopting AI into data and analytics workflows. Steve Karam, Ilker Taskaya, Ross, Millenacker, and Jatinder Luthra explore:
- Common compliance issues in AI adoption: Understand emerging trends, challenges like “right to be forgotten,” and their business implications.
- How to protect sensitive data at every stage: Learn best practices, such as better masking techniques, for securing data during AI model training and data use.
- The role of automated compliance checks: Discover why it’s important to introduce strong compliance practices into the MLOps flow as organizations expand the role of data in automated workflows.
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Presenters
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.
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.